.github/ISSUE_TEMPLATE/bug_report.yml

@@ -37,20 +37,20 @@ body:
     id: what-should
     attributes:
       label: What should have happened?
-      description: Tell what you think the normal behavior should be
+      description: tell what you think the normal behavior should be
     validations:
       required: true
   - type: input
     id: commit
     attributes:
       label: Commit where the problem happens
-      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
+      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
     validations:
       required: true
   - type: dropdown
     id: platforms
     attributes:
-      label: What platforms do you use to access the UI ?
+      label: What platforms do you use to access UI ?
       multiple: true
       options:
         - Windows
@@ -74,27 +74,10 @@ body:
     id: cmdargs
     attributes:
       label: Command Line Arguments
-      description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
+      description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below
       render: Shell
-    validations:
-      required: true
-  - type: textarea
-    id: extensions
-    attributes:
-      label: List of extensions
-      description: Are you using any extensions other than built-ins? If yes, provide a list, you can copy it at "Extensions" tab. Write "No" otherwise.
-    validations:
-      required: true
-  - type: textarea
-    id: logs
-    attributes:
-      label: Console logs
-      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
-      render: Shell
-    validations:
-      required: true
   - type: textarea
     id: misc
     attributes:
-      label: Additional information
+      label: Additional information, context and logs
-      description: Please provide us with any relevant additional info or context.
+      description: Please provide us with any relevant additional info, context or log output.

.github/workflows/run_tests.yaml

@@ -18,7 +18,7 @@ jobs:
           cache-dependency-path: |
             **/requirements*txt
       - name: Run tests
-        run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+        run: python launch.py --tests --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()

README.md

@@ -13,11 +13,11 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Prompt Matrix
 - Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
-    - a man in a `((tuxedo))` - will pay more attention to tuxedo
+    - a man in a ((tuxedo)) - will pay more attention to tuxedo
-    - a man in a `(tuxedo:1.21)` - alternative syntax
+    - a man in a (tuxedo:1.21) - alternative syntax
-    - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
+    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
-- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
+- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
 - Textual Inversion
     - have as many embeddings as you want and use any names you like for them
     - use multiple embeddings with different numbers of vectors per token
@@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion.
     - CodeFormer, face restoration tool as an alternative to GFPGAN
     - RealESRGAN, neural network upscaler
     - ESRGAN, neural network upscaler with a lot of third party models
-    - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+    - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
     - LDSR, Latent diffusion super resolution upscaling
 - Resizing aspect ratio options
 - Sampling method selection
@@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion.
      - drag and drop an image/text-parameters to promptbox
 - Read Generation Parameters Button, loads parameters in promptbox to UI
 - Settings page
-- Running arbitrary python code from UI (must run with `--allow-code` to enable)
+- Running arbitrary python code from UI (must run with --allow-code to enable)
 - Mouseover hints for most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
 - Tiling support, a checkbox to create images that can be tiled like textures
@@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion.
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts
-- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
 - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
 - Generate forever option
 - Training tab
@@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Clip skip
 - Hypernetworks
 - Loras (same as Hypernetworks but more pretty)
-- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt. 
 - Can select to load a different VAE from settings screen
 - Estimated completion time in progress bar
 - API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
@@ -91,6 +91,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
+- 
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -100,10 +101,11 @@ Alternatively, use online services (like Google Colab):
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
 ### Automatic Installation on Windows
-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
+1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
 2. Install [git](https://git-scm.com/download/win).
 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
-4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
+5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
 
 ### Automatic Installation on Linux
 1. Install the dependencies:
@@ -119,7 +121,7 @@ sudo pacman -S wget git python3
 ```bash
 bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
 ```
-3. Run `webui.sh`.
+
 ### Installation on Apple Silicon
 
 Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
@@ -153,9 +155,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
 - xformers - https://github.com/facebookresearch/xformers
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
-- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
-- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
 - Security advice - RyotaK
-- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

configs/instruct-pix2pix.yaml

@@ -1,98 +0,0 @@
-# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
-# See more details in LICENSE.
-
-model:
-  base_learning_rate: 1.0e-04
-  target: modules.models.diffusion.ddpm_edit.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: edited
-    cond_stage_key: edit
-    # image_size: 64
-    # image_size: 32
-    image_size: 16
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: hybrid
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: false
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 0 ]
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        in_channels: 8
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 128
-    num_workers: 1
-    wrap: false
-    validation:
-      target: edit_dataset.EditDataset
-      params:
-        path: data/clip-filtered-dataset
-        cache_dir:  data/
-        cache_name: data_10k
-        split: val
-        min_text_sim: 0.2
-        min_image_sim: 0.75
-        min_direction_sim: 0.2
-        max_samples_per_prompt: 1
-        min_resize_res: 512
-        max_resize_res: 512
-        crop_res: 512
-        output_as_edit: False
-        real_input: True

extensions-builtin/Lora/extra_networks_lora.py

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared
+from modules import extra_networks
 import lora
 
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
@@ -6,12 +6,6 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
         super().__init__('lora')
 
     def activate(self, p, params_list):
-        additional = shared.opts.sd_lora
-
-        if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
-            p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
-            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
-
         names = []
         multipliers = []
         for params in params_list:

extensions-builtin/Lora/lora.py

@@ -2,34 +2,18 @@ import glob
 import os
 import re
 import torch
-from typing import Union
 
-from modules import shared, devices, sd_models, errors
+from modules import shared, devices, sd_models
-
-metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
 
 re_digits = re.compile(r"\d+")
-re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_compiled = {}
+re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
-
+re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
-suffix_conversion = {
+re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
-    "attentions": {},
-    "resnets": {
-        "conv1": "in_layers_2",
-        "conv2": "out_layers_3",
-        "time_emb_proj": "emb_layers_1",
-        "conv_shortcut": "skip_connection",
-    }
-}
 
 
-def convert_diffusers_name_to_compvis(key, is_sd2):
+def convert_diffusers_name_to_compvis(key):
-    def match(match_list, regex_text):
+    def match(match_list, regex):
-        regex = re_compiled.get(regex_text)
-        if regex is None:
-            regex = re.compile(regex_text)
-            re_compiled[regex_text] = regex
-
         r = re.match(regex, key)
         if not r:
             return False
@@ -40,33 +24,16 @@ def convert_diffusers_name_to_compvis(key, is_sd2):
 
     m = []
 
-    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+    if match(m, re_unet_down_blocks):
-        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
-        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
 
-    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+    if match(m, re_unet_mid_blocks):
-        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"diffusion_model_middle_block_1_{m[1]}"
-        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
 
-    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+    if match(m, re_unet_up_blocks):
-        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
-        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
-
-    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
-        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
-
-    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
-        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
-
-    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
-        if is_sd2:
-            if 'mlp_fc1' in m[1]:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
-            elif 'mlp_fc2' in m[1]:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
-            else:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
 
+    if match(m, re_text_block):
         return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
 
     return key
@@ -76,23 +43,6 @@ class LoraOnDisk:
     def __init__(self, name, filename):
         self.name = name
         self.filename = filename
-        self.metadata = {}
-
-        _, ext = os.path.splitext(filename)
-        if ext.lower() == ".safetensors":
-            try:
-                self.metadata = sd_models.read_metadata_from_safetensors(filename)
-            except Exception as e:
-                errors.display(e, f"reading lora {filename}")
-
-        if self.metadata:
-            m = {}
-            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
-                m[k] = v
-
-            self.metadata = m
-
-        self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text
 
 
 class LoraModule:
@@ -132,22 +82,15 @@ def load_lora(name, filename):
 
     sd = sd_models.read_state_dict(filename)
 
-    keys_failed_to_match = {}
+    keys_failed_to_match = []
-    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
 
     for key_diffusers, weight in sd.items():
-        key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+        fullkey = convert_diffusers_name_to_compvis(key_diffusers)
-        key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
+        key, lora_key = fullkey.split(".", 1)
 
         sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
-
         if sd_module is None:
-            m = re_x_proj.match(key)
+            keys_failed_to_match.append(key_diffusers)
-            if m:
-                sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
-
-        if sd_module is None:
-            keys_failed_to_match[key_diffusers] = key
             continue
 
         lora_module = lora.modules.get(key, None)
@@ -161,21 +104,15 @@ def load_lora(name, filename):
 
         if type(sd_module) == torch.nn.Linear:
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.MultiheadAttention:
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
         elif type(sd_module) == torch.nn.Conv2d:
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         else:
-            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
-            continue
             assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
 
         with torch.no_grad():
             module.weight.copy_(weight)
 
-        module.to(device=devices.cpu, dtype=devices.dtype)
+        module.to(device=devices.device, dtype=devices.dtype)
 
         if lora_key == "lora_up.weight":
             lora_module.up = module
@@ -221,120 +158,25 @@ def load_loras(names, multipliers=None):
         loaded_loras.append(lora)
 
 
-def lora_calc_updown(lora, module, target):
+def lora_forward(module, input, res):
-    with torch.no_grad():
+    if len(loaded_loras) == 0:
-        up = module.up.weight.to(target.device, dtype=target.dtype)
+        return res
-        down = module.down.weight.to(target.device, dtype=target.dtype)
 
-        if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+    lora_layer_name = getattr(module, 'lora_layer_name', None)
-            updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+    for lora in loaded_loras:
-        else:
+        module = lora.modules.get(lora_layer_name, None)
-            updown = up @ down
+        if module is not None:
+            res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
 
-        updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+    return res
-
-        return updown
-
-
-def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
-    """
-    Applies the currently selected set of Loras to the weights of torch layer self.
-    If weights already have this particular set of loras applied, does nothing.
-    If not, restores orginal weights from backup and alters weights according to loras.
-    """
-
-    lora_layer_name = getattr(self, 'lora_layer_name', None)
-    if lora_layer_name is None:
-        return
-
-    current_names = getattr(self, "lora_current_names", ())
-    wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
-
-    weights_backup = getattr(self, "lora_weights_backup", None)
-    if weights_backup is None:
-        if isinstance(self, torch.nn.MultiheadAttention):
-            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
-        else:
-            weights_backup = self.weight.to(devices.cpu, copy=True)
-
-        self.lora_weights_backup = weights_backup
-
-    if current_names != wanted_names:
-        if weights_backup is not None:
-            if isinstance(self, torch.nn.MultiheadAttention):
-                self.in_proj_weight.copy_(weights_backup[0])
-                self.out_proj.weight.copy_(weights_backup[1])
-            else:
-                self.weight.copy_(weights_backup)
-
-        for lora in loaded_loras:
-            module = lora.modules.get(lora_layer_name, None)
-            if module is not None and hasattr(self, 'weight'):
-                self.weight += lora_calc_updown(lora, module, self.weight)
-                continue
-
-            module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
-            module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
-            module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
-            module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
-
-            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
-                updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
-                updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
-                updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-
-                self.in_proj_weight += updown_qkv
-                self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
-                continue
-
-            if module is None:
-                continue
-
-            print(f'failed to calculate lora weights for layer {lora_layer_name}')
-
-        setattr(self, "lora_current_names", wanted_names)
-
-
-def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    setattr(self, "lora_current_names", ())
-    setattr(self, "lora_weights_backup", None)
 
 
 def lora_Linear_forward(self, input):
-    lora_apply_weights(self)
+    return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
-
-    return torch.nn.Linear_forward_before_lora(self, input)
-
-
-def lora_Linear_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
 
 
 def lora_Conv2d_forward(self, input):
-    lora_apply_weights(self)
+    return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
-
-    return torch.nn.Conv2d_forward_before_lora(self, input)
-
-
-def lora_Conv2d_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
-
-
-def lora_MultiheadAttention_forward(self, *args, **kwargs):
-    lora_apply_weights(self)
-
-    return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
-
-
-def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
 
 
 def list_available_loras():
@@ -347,7 +189,7 @@ def list_available_loras():
         glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
         glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
 
-    for filename in sorted(candidates, key=str.lower):
+    for filename in sorted(candidates):
         if os.path.isdir(filename):
             continue
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -1,19 +1,14 @@
 import torch
-import gradio as gr
 
 import lora
 import extra_networks_lora
 import ui_extra_networks_lora
-from modules import script_callbacks, ui_extra_networks, extra_networks, shared
+from modules import script_callbacks, ui_extra_networks, extra_networks
 
 
 def unload():
     torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
-    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
     torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
-    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
-    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
-    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
 
 
 def before_ui():
@@ -24,33 +19,12 @@ def before_ui():
 if not hasattr(torch.nn, 'Linear_forward_before_lora'):
     torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
 
-if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
-    torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
-
 if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
     torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
 
-if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
-    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
-
-if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
-    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
-
-if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
-    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
-
 torch.nn.Linear.forward = lora.lora_Linear_forward
-torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
 torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
-torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
-torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
-torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
 
 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
-
-
-shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
-}))

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -15,15 +15,20 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for name, lora_on_disk in lora.available_loras.items():
             path, ext = os.path.splitext(lora_on_disk.filename)
+            previews = [path + ".png", path + ".preview.png"]
+
+            preview = None
+            for file in previews:
+                if os.path.isfile(file):
+                    preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
+                    break
+
             yield {
                 "name": name,
                 "filename": path,
-                "preview": self.find_preview(path),
+                "preview": preview,
-                "description": self.find_description(path),
-                "search_term": self.search_terms_from_path(lora_on_disk.filename),
                 "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
-                "local_preview": f"{path}.{shared.opts.samples_format}",
+                "local_preview": path + ".png",
-                "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
             }
 
     def allowed_directories_for_previews(self):

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

@@ -89,15 +89,22 @@ function checkBrackets(evt, textArea, counterElt) {
 function setupBracketChecking(id_prompt, id_counter){
     var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
     var counter = gradioApp().getElementById(id_counter)
-
     textarea.addEventListener("input", function(evt){
         checkBrackets(evt, textarea, counter)
     });
 }
 
-onUiLoaded(function(){
+var shadowRootLoaded = setInterval(function() {
+    var shadowRoot = document.querySelector('gradio-app').shadowRoot;
+    if(! shadowRoot)  return false;
+
+    var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
+    if(shadowTextArea.length < 1)  return false;
+
+    clearInterval(shadowRootLoaded);
+
     setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
     setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
-    setupBracketChecking('img2img_prompt', 'img2img_token_counter')
+    setupBracketChecking('img2img_prompt', 'imgimg_token_counter')
     setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-})
+}, 1000);

html/extra-networks-card.html

@@ -1,15 +1,11 @@
-<div class='card' style={style} onclick={card_clicked}>
+<div class='card' {preview_html} onclick={card_clicked}>
-	{metadata_button}
-
 	<div class='actions'>
 		<div class='additional'>
 			<ul>
 				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
-			<span style="display:none" class='search_term'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
-		<span class='description'>{description}</span>
 	</div>
 </div>
 

html/licenses.html

@@ -417,248 +417,3 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 </pre>
 
-<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
-<small>Some small amounts of code borrowed and reworked.</small>
-<pre>
-   Copyright 2023 The HuggingFace Team. All rights reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
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-   See the License for the specific language governing permissions and
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-
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-<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
-<pre>
-The MIT License (MIT)
-
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-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-</pre>

javascript/aspectRatioOverlay.js

@@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){
 		currentHeight = e.target.value*1.0
 	}
 
-	var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
+	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
 
 	if(!inImg2img){
 		return;
@@ -22,7 +22,7 @@ function dimensionChange(e, is_width, is_height){
 
     var tabIndex = get_tab_index('mode_img2img')
 	if(tabIndex == 0){ // img2img
-		targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
+		targetElement = gradioApp().querySelector('div[data-testid=image] img');
 	} else if(tabIndex == 1){ //Sketch
 		targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
 	} else if(tabIndex == 2){ // Inpaint
@@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){
 		if(!arPreviewRect){
 		    arPreviewRect = document.createElement('div')
 		    arPreviewRect.id = "imageARPreview";
-		    gradioApp().appendChild(arPreviewRect)
+		    gradioApp().getRootNode().appendChild(arPreviewRect)
 		}
 
 
@@ -91,26 +91,23 @@ onUiUpdate(function(){
 	if(arPreviewRect){
 		arPreviewRect.style.display = 'none';
 	}
-    var tabImg2img = gradioApp().querySelector("#tab_img2img");
+	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
-    if (tabImg2img) {
+	if(inImg2img){
-        var inImg2img = tabImg2img.style.display == "block";
+		let inputs = gradioApp().querySelectorAll('input');
-        if(inImg2img){
+		inputs.forEach(function(e){
-            let inputs = gradioApp().querySelectorAll('input');
+		    var is_width = e.parentElement.id == "img2img_width"
-            inputs.forEach(function(e){
+		    var is_height = e.parentElement.id == "img2img_height"
-                var is_width = e.parentElement.id == "img2img_width"
-                var is_height = e.parentElement.id == "img2img_height"
 
-                if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+			if((is_width || is_height) && !e.classList.contains('scrollwatch')){
-                    e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+				e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
-                    e.classList.add('scrollwatch')
+				e.classList.add('scrollwatch')
-                }
+			}
-                if(is_width){
+			if(is_width){
-                    currentWidth = e.value*1.0
+				currentWidth = e.value*1.0
-                }
+			}
-                if(is_height){
+			if(is_height){
-                    currentHeight = e.value*1.0
+				currentHeight = e.value*1.0
-                }
+			}
-            })
+		})
-        }
+	}
-    }
 });

javascript/contextMenus.js

@@ -43,7 +43,7 @@ contextMenuInit = function(){
 
     })
 
-    gradioApp().appendChild(contextMenu)
+    gradioApp().getRootNode().appendChild(contextMenu)
 
     let menuWidth = contextMenu.offsetWidth + 4;
     let menuHeight = contextMenu.offsetHeight + 4;

javascript/edit-attention.js

@@ -1,6 +1,6 @@
 function keyupEditAttention(event){
 	let target = event.originalTarget || event.composedPath()[0];
-	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
+	if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 	let isPlus = event.key == "ArrowUp"

javascript/extensions.js

@@ -1,8 +1,7 @@
 
-function extensions_apply(_, _, disable_all){
+function extensions_apply(_, _){
-    var disable = []
+    disable = []
-    var update = []
+    update = []
-
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
         if(x.name.startsWith("enable_") && ! x.checked)
             disable.push(x.name.substr(7))
@@ -13,28 +12,15 @@ function extensions_apply(_, _, disable_all){
 
     restart_reload()
 
-    return [JSON.stringify(disable), JSON.stringify(update), disable_all]
+    return [JSON.stringify(disable), JSON.stringify(update)]
 }
 
-function extensions_check(_, _){
+function extensions_check(){
-    var disable = []
-
-    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
-        if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
-    })
-
     gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
         x.innerHTML = "Loading..."
     })
 
-
+    return []
-    var id = randomId()
-    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
-
-    })
-
-    return [id, JSON.stringify(disable)]
 }
 
 function install_extension_from_index(button, url){

javascript/extraNetworks.js

@@ -5,16 +5,18 @@ function setupExtraNetworksForTab(tabname){
     var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
     var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
     var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
+    var close = gradioApp().getElementById(tabname+'_extra_close')
 
     search.classList.add('search')
     tabs.appendChild(search)
     tabs.appendChild(refresh)
+    tabs.appendChild(close)
 
     search.addEventListener("input", function(evt){
         searchTerm = search.value.toLowerCase()
 
         gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
-            text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
+            text = elem.querySelector('.name').textContent.toLowerCase()
             elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
         })
     });
@@ -46,39 +48,10 @@ function setupExtraNetworks(){
 
 onUiLoaded(setupExtraNetworks)
 
-var re_extranet   =    /<([^:]+:[^:]+):[\d\.]+>/;
-var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
-
-function tryToRemoveExtraNetworkFromPrompt(textarea, text){
-    var m = text.match(re_extranet)
-    if(! m) return false
-
-    var partToSearch = m[1]
-    var replaced = false
-    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
-        m = found.match(re_extranet);
-        if(m[1] == partToSearch){
-            replaced = true;
-            return ""
-        }
-        return found;
-    })
-
-    if(replaced){
-        textarea.value = newTextareaText
-        return true;
-    }
-
-    return false
-}
-
 function cardClicked(tabname, textToAdd, allowNegativePrompt){
     var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
 
-    if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
+    textarea.value = textarea.value + " " + textToAdd
-        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
-    }
-
     updateInput(textarea)
 }
 
@@ -94,86 +67,3 @@ function saveCardPreview(event, tabname, filename){
     event.stopPropagation()
     event.preventDefault()
 }
-
-function extraNetworksSearchButton(tabs_id, event){
-    searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
-    button = event.target
-    text = button.classList.contains("search-all") ? "" : button.textContent.trim()
-
-    searchTextarea.value = text
-    updateInput(searchTextarea)
-}
-
-var globalPopup = null;
-var globalPopupInner = null;
-function popup(contents){
-    if(! globalPopup){
-        globalPopup = document.createElement('div')
-        globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
-        globalPopup.classList.add('global-popup');
-
-        var close = document.createElement('div')
-        close.classList.add('global-popup-close');
-        close.onclick = function(){ globalPopup.style.display = "none"; };
-        close.title = "Close";
-        globalPopup.appendChild(close)
-
-        globalPopupInner = document.createElement('div')
-        globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
-        globalPopupInner.classList.add('global-popup-inner');
-        globalPopup.appendChild(globalPopupInner)
-
-        gradioApp().appendChild(globalPopup);
-    }
-
-    globalPopupInner.innerHTML = '';
-    globalPopupInner.appendChild(contents);
-
-    globalPopup.style.display = "flex";
-}
-
-function extraNetworksShowMetadata(text){
-    elem = document.createElement('pre')
-    elem.classList.add('popup-metadata');
-    elem.textContent = text;
-
-    popup(elem);
-}
-
-function requestGet(url, data, handler, errorHandler){
-    var xhr = new XMLHttpRequest();
-    var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
-    xhr.open("GET", url + "?" + args, true);
-
-    xhr.onreadystatechange = function () {
-        if (xhr.readyState === 4) {
-            if (xhr.status === 200) {
-                try {
-                    var js = JSON.parse(xhr.responseText);
-                    handler(js)
-                } catch (error) {
-                    console.error(error);
-                    errorHandler()
-                }
-            } else{
-                errorHandler()
-            }
-        }
-    };
-    var js = JSON.stringify(data);
-    xhr.send(js);
-}
-
-function extraNetworksRequestMetadata(event, extraPage, cardName){
-    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
-
-    requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
-        if(data && data.metadata){
-            extraNetworksShowMetadata(data.metadata)
-        } else{
-            showError()
-        }
-    }, showError)
-
-    event.stopPropagation()
-}

javascript/hints.js

@@ -6,11 +6,10 @@ titles = {
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
 	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
-	"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
 	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 
 
-	"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
+	"Batch count": "How many batches of images to create",
-	"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
+	"Batch size": "How many image to create in a single batch",
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
     "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
@@ -18,10 +17,11 @@ titles = {
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
-    "\u{1f5d1}\ufe0f": "Clear prompt",
+    "\U0001F5D1": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
     "\u{1f4d2}": "Paste available values into the field",
-    "\u{1f3b4}": "Show/hide extra networks",
+    "\u{1f3b4}": "Show extra networks",
+
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
@@ -39,6 +39,7 @@ titles = {
     "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
 
     "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
+    "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
 
     "Skip": "Stop processing current image and continue processing.",
     "Interrupt": "Stop processing images and return any results accumulated so far.",
@@ -49,7 +50,7 @@ titles = {
 
     "None": "Do not do anything special",
     "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)",
-    "X/Y/Z plot": "Create grid(s) where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows",
+    "X/Y plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows",
     "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work",
 
     "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
@@ -65,14 +66,12 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
-    "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
+    "Loopback": "Process an image, use it as an input, repeat.",
-    "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
+    "Loops": "How many times to repeat processing an image and using it as input for the next iteration",
-    "Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
-    "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",
 
     "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",

javascript/imageviewer.js

@@ -11,7 +11,7 @@ function showModal(event) {
     if (modalImage.style.display === 'none') {
         lb.style.setProperty('background-image', 'url(' + source.src + ')');
     }
-    lb.style.display = "flex";
+    lb.style.display = "block";
     lb.focus()
 
     const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
@@ -32,7 +32,13 @@ function negmod(n, m) {
 function updateOnBackgroundChange() {
     const modalImage = gradioApp().getElementById("modalImage")
     if (modalImage && modalImage.offsetParent) {
-        let currentButton = selected_gallery_button();
+        let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        let currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })
 
         if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
@@ -44,10 +50,22 @@ function updateOnBackgroundChange() {
 }
 
 function modalImageSwitch(offset) {
-    var galleryButtons = all_gallery_buttons();
+    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+    var galleryButtons = []
+    allgalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            galleryButtons.push(elem);
+        }
+    })
 
     if (galleryButtons.length > 1) {
-        var currentButton = selected_gallery_button();
+        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        var currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })
 
         var result = -1
         galleryButtons.forEach(function(v, i) {
@@ -118,29 +136,37 @@ function modalKeyHandler(event) {
     }
 }
 
-function setupImageForLightbox(e) {
+function showGalleryImage() {
-	if (e.dataset.modded)
+    setTimeout(function() {
-		return;
+        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
 
-	e.dataset.modded = true;
+        if (fullImg_preview != null) {
-	e.style.cursor='pointer'
+            fullImg_preview.forEach(function function_name(e) {
-	e.style.userSelect='none'
+                if (e.dataset.modded)
+                    return;
+                e.dataset.modded = true;
+                if(e && e.parentElement.tagName == 'DIV'){
+                    e.style.cursor='pointer'
+                    e.style.userSelect='none'
 
-	var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
+                    var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
 
-	// For Firefox, listening on click first switched to next image then shows the lightbox.
+                    // For Firefox, listening on click first switched to next image then shows the lightbox.
-	// If you know how to fix this without switching to mousedown event, please.
+                    // If you know how to fix this without switching to mousedown event, please.
-	// For other browsers the event is click to make it possiblr to drag picture.
+                    // For other browsers the event is click to make it possiblr to drag picture.
-	var event = isFirefox ? 'mousedown' : 'click'
+                    var event = isFirefox ? 'mousedown' : 'click'
 
-	e.addEventListener(event, function (evt) {
+                    e.addEventListener(event, function (evt) {
-		if(!opts.js_modal_lightbox || evt.button != 0) return;
+                        if(!opts.js_modal_lightbox || evt.button != 0) return;
-
+                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
-		modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
+                        evt.preventDefault()
-		evt.preventDefault()
+                        showModal(evt)
-		showModal(evt)
+                    }, true);
-	}, true);
+                }
+            });
+        }
 
+    }, 100);
 }
 
 function modalZoomSet(modalImage, enable) {
@@ -173,21 +199,21 @@ function modalTileImageToggle(event) {
 }
 
 function galleryImageHandler(e) {
-    //if (e && e.parentElement.tagName == 'BUTTON') {
+    if (e && e.parentElement.tagName == 'BUTTON') {
         e.onclick = showGalleryImage;
-    //}
+    }
 }
 
 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
+    fullImg_preview = gradioApp().querySelectorAll('img.w-full')
     if (fullImg_preview != null) {
-        fullImg_preview.forEach(setupImageForLightbox);
+        fullImg_preview.forEach(galleryImageHandler);
     }
     updateOnBackgroundChange();
 })
 
 document.addEventListener("DOMContentLoaded", function() {
-    //const modalFragment = document.createDocumentFragment();
+    const modalFragment = document.createDocumentFragment();
     const modal = document.createElement('div')
     modal.onclick = closeModal;
     modal.id = "lightboxModal";
@@ -251,9 +277,9 @@ document.addEventListener("DOMContentLoaded", function() {
 
     modal.appendChild(modalNext)
 
-    gradioApp().appendChild(modal)
 
+    gradioApp().getRootNode().appendChild(modal)
 
-    document.body.appendChild(modal);
+    document.body.appendChild(modalFragment);
 
 });

javascript/notification.js

@@ -15,7 +15,7 @@ onUiUpdate(function(){
         }
     }
 
-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden');
 
     if (galleryPreviews == null) return;
 

javascript/progressbar.js

@@ -1,13 +1,78 @@
 // code related to showing and updating progressbar shown as the image is being made
 
-function rememberGallerySelection(id_gallery){
 
+galleries = {}
+storedGallerySelections = {}
+galleryObservers = {}
+
+function rememberGallerySelection(id_gallery){
+    storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery)
 }
 
 function getGallerySelectedIndex(id_gallery){
+    let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
+    let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
 
+     let currentlySelectedIndex = -1
+     galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } })
+
+     return currentlySelectedIndex
 }
 
+// this is a workaround for https://github.com/gradio-app/gradio/issues/2984
+function check_gallery(id_gallery){
+    let gallery = gradioApp().getElementById(id_gallery)
+    // if gallery has no change, no need to setting up observer again.
+    if (gallery && galleries[id_gallery] !== gallery){
+        galleries[id_gallery] = gallery;
+        if(galleryObservers[id_gallery]){
+            galleryObservers[id_gallery].disconnect();
+        }
+
+        storedGallerySelections[id_gallery] = -1
+
+        galleryObservers[id_gallery] = new MutationObserver(function (){
+            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
+            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
+            let currentlySelectedIndex = getGallerySelectedIndex(id_gallery)
+            prevSelectedIndex = storedGallerySelections[id_gallery]
+            storedGallerySelections[id_gallery] = -1
+
+            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
+                // automatically re-open previously selected index (if exists)
+                activeElement = gradioApp().activeElement;
+                let scrollX = window.scrollX;
+                let scrollY = window.scrollY;
+
+                galleryButtons[prevSelectedIndex].click();
+                showGalleryImage();
+
+                // When the gallery button is clicked, it gains focus and scrolls itself into view
+                // We need to scroll back to the previous position
+                setTimeout(function (){
+                    window.scrollTo(scrollX, scrollY);
+                }, 50);
+
+                if(activeElement){
+                    // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
+                    // if someone has a better solution please by all means
+                    setTimeout(function (){
+                        activeElement.focus({
+                            preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
+                        })
+                    }, 1);
+                }
+            }
+        })
+        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
+    }
+}
+
+onUiUpdate(function(){
+    check_gallery('txt2img_gallery')
+    check_gallery('img2img_gallery')
+})
+
 function request(url, data, handler, errorHandler){
     var xhr = new XMLHttpRequest();
     var url = url;
@@ -74,7 +139,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
 
     var divProgress = document.createElement('div')
     divProgress.className='progressDiv'
-    divProgress.style.display = opts.show_progressbar ? "block" : "none"
+    divProgress.style.display = opts.show_progressbar ? "" : "none"
     var divInner = document.createElement('div')
     divInner.className='progress'
 

javascript/ui.js

@@ -7,31 +7,9 @@ function set_theme(theme){
     }
 }
 
-function all_gallery_buttons() {
-    var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small');
-    var visibleGalleryButtons = [];
-    allGalleryButtons.forEach(function(elem) {
-        if (elem.parentElement.offsetParent) {
-            visibleGalleryButtons.push(elem);
-        }
-    })
-    return visibleGalleryButtons;
-}
-
-function selected_gallery_button() {
-    var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected');
-    var visibleCurrentButton = null;
-    allCurrentButtons.forEach(function(elem) {
-        if (elem.parentElement.offsetParent) {
-            visibleCurrentButton = elem;
-        }
-    })
-    return visibleCurrentButton;
-}
-
 function selected_gallery_index(){
-    var buttons = all_gallery_buttons();
+    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item')
-    var button = selected_gallery_button();
+    var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2')
 
     var result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })
@@ -40,18 +18,14 @@ function selected_gallery_index(){
 }
 
 function extract_image_from_gallery(gallery){
-    if (gallery.length == 0){
+    if(gallery.length == 1){
-        return [null];
+        return [gallery[0]]
-    }
-    if (gallery.length == 1){
-        return [gallery[0]];
     }
 
     index = selected_gallery_index()
 
     if (index < 0 || index >= gallery.length){
-        // Use the first image in the gallery as the default
+        return [null]
-        index = 0;
     }
 
     return [gallery[index]];
@@ -112,7 +86,7 @@ function get_tab_index(tabId){
     var res = 0
 
     gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
-        if(button.className.indexOf('selected') != -1)
+        if(button.className.indexOf('bg-white') != -1)
             res = i
     })
 
@@ -217,28 +191,6 @@ function confirm_clear_prompt(prompt, negative_prompt) {
     return [prompt, negative_prompt]
 }
 
-
-promptTokecountUpdateFuncs = {}
-
-function recalculatePromptTokens(name){
-    if(promptTokecountUpdateFuncs[name]){
-        promptTokecountUpdateFuncs[name]()
-    }
-}
-
-function recalculate_prompts_txt2img(){
-    recalculatePromptTokens('txt2img_prompt')
-    recalculatePromptTokens('txt2img_neg_prompt')
-    return args_to_array(arguments);
-}
-
-function recalculate_prompts_img2img(){
-    recalculatePromptTokens('img2img_prompt')
-    recalculatePromptTokens('img2img_neg_prompt')
-    return args_to_array(arguments);
-}
-
-
 opts = {}
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
@@ -280,11 +232,14 @@ onUiUpdate(function(){
             return
         }
 
+
         prompt.parentElement.insertBefore(counter, prompt)
+        counter.classList.add("token-counter")
         prompt.parentElement.style.position = "relative"
 
-		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
+		textarea.addEventListener("input", function(){
-		textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
+		    update_token_counter(id_button);
+		});
     }
 
     registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
@@ -318,7 +273,7 @@ onOptionsChanged(function(){
 
 let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
-let token_timeouts = {};
+let token_timeout;
 
 function update_txt2img_tokens(...args) {
 	update_token_counter("txt2img_token_button")
@@ -335,9 +290,9 @@ function update_img2img_tokens(...args) {
 }
 
 function update_token_counter(button_id) {
-	if (token_timeouts[button_id])
+	if (token_timeout)
-		clearTimeout(token_timeouts[button_id]);
+		clearTimeout(token_timeout);
-	token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
+	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
 function restart_reload(){
@@ -354,10 +309,3 @@ function updateInput(target){
 	Object.defineProperty(e, "target", {value: target})
 	target.dispatchEvent(e);
 }
-
-
-var desiredCheckpointName = null;
-function selectCheckpoint(name){
-    desiredCheckpointName = name;
-    gradioApp().getElementById('change_checkpoint').click()
-}

launch.py

@@ -5,56 +5,16 @@ import sys
 import importlib.util
 import shlex
 import platform
+import argparse
 import json
 
-from modules import cmd_args
+dir_repos = "repositories"
-from modules.paths_internal import script_path, extensions_dir
+dir_extensions = "extensions"
-
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-sys.argv += shlex.split(commandline_args)
-
-args, _ = cmd_args.parser.parse_known_args()
-
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 stored_commit_hash = None
 skip_install = False
-dir_repos = "repositories"
-
-if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
-    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
-
-
-def check_python_version():
-    is_windows = platform.system() == "Windows"
-    major = sys.version_info.major
-    minor = sys.version_info.minor
-    micro = sys.version_info.micro
-
-    if is_windows:
-        supported_minors = [10]
-    else:
-        supported_minors = [7, 8, 9, 10, 11]
-
-    if not (major == 3 and minor in supported_minors):
-        import modules.errors
-
-        modules.errors.print_error_explanation(f"""
-INCOMPATIBLE PYTHON VERSION
-
-This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
-If you encounter an error with "RuntimeError: Couldn't install torch." message,
-or any other error regarding unsuccessful package (library) installation,
-please downgrade (or upgrade) to the latest version of 3.10 Python
-and delete current Python and "venv" folder in WebUI's directory.
-
-You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
-
-{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
-
-Use --skip-python-version-check to suppress this warning.
-""")
 
 
 def commit_hash():
@@ -71,6 +31,23 @@ def commit_hash():
     return stored_commit_hash
 
 
+def extract_arg(args, name):
+    return [x for x in args if x != name], name in args
+
+
+def extract_opt(args, name):
+    opt = None
+    is_present = False
+    if name in args:
+        is_present = True
+        idx = args.index(name)
+        del args[idx]
+        if idx < len(args) and args[idx][0] != "-":
+            opt = args[idx]
+            del args[idx]
+    return args, is_present, opt
+
+
 def run(command, desc=None, errdesc=None, custom_env=None, live=False):
     if desc is not None:
         print(desc)
@@ -114,7 +91,7 @@ def is_installed(package):
 
 
 def repo_dir(name):
-    return os.path.join(script_path, dir_repos, name)
+    return os.path.join(dir_repos, name)
 
 
 def run_python(code, desc=None, errdesc=None):
@@ -154,16 +131,6 @@ def git_clone(url, dir, name, commithash=None):
         run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
 
         
-def git_pull_recursive(dir):
-    for subdir, _, _ in os.walk(dir):
-        if os.path.exists(os.path.join(subdir, '.git')):
-            try:
-                output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
-                print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
-            except subprocess.CalledProcessError as e:
-                print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
-
-
 def version_check(commit):
     try:
         import requests
@@ -206,20 +173,16 @@ def list_extensions(settings_file):
         print(e, file=sys.stderr)
 
     disabled_extensions = set(settings.get('disabled_extensions', []))
-    disable_all_extensions = settings.get('disable_all_extensions', 'none')
 
-    if disable_all_extensions != 'none':
+    return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
-        return []
-
-    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
 
 
 def run_extensions_installers(settings_file):
-    if not os.path.isdir(extensions_dir):
+    if not os.path.isdir(dir_extensions):
         return
 
     for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+        run_extension_installer(os.path.join(dir_extensions, dirname_extension))
 
 
 def prepare_environment():
@@ -227,8 +190,8 @@ def prepare_environment():
 
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+    commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -239,24 +202,37 @@ def prepare_environment():
     codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
-    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
     k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-    if not args.skip_python_version_check:
+    sys.argv += shlex.split(commandline_args)
-        check_python_version()
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
+    args, _ = parser.parse_known_args(sys.argv)
+
+    sys.argv, _ = extract_arg(sys.argv, '-f')
+    sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
+    sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
+    sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
+    sys.argv, update_check = extract_arg(sys.argv, '--update-check')
+    sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
+    sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
+    xformers = '--xformers' in sys.argv
+    ngrok = '--ngrok' in sys.argv
 
     commit = commit_hash()
 
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
 
-    if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+    if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
 
-    if not args.skip_torch_cuda_test:
+    if not skip_torch_cuda_test:
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
@@ -268,22 +244,22 @@ def prepare_environment():
     if not is_installed("open_clip"):
         run_pip(f"install {openclip_package}", "open_clip")
 
-    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
+    if (not is_installed("xformers") or reinstall_xformers) and xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+                run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
             else:
                 print("Installation of xformers is not supported in this version of Python.")
                 print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
                 if not is_installed("xformers"):
                     exit(0)
         elif platform.system() == "Linux":
-            run_pip(f"install {xformers_package}", "xformers")
+            run_pip("install xformers==0.0.16rc425", "xformers")
 
-    if not is_installed("pyngrok") and args.ngrok:
+    if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
-    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
+    os.makedirs(dir_repos, exist_ok=True)
 
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
     git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
@@ -292,26 +268,21 @@ def prepare_environment():
     git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
-        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
-    if not os.path.isfile(requirements_file):
+    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
-        requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 
-    if args.update_check:
+    if update_check:
         version_check(commit)
     
-    if args.update_all_extensions:
-        git_pull_recursive(extensions_dir)
-    
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
 
-    if args.tests and not args.no_tests:
+    if run_tests:
-        exitcode = tests(args.tests)
+        exitcode = tests(test_dir)
         exit(exitcode)
 
 
@@ -320,18 +291,16 @@ def tests(test_dir):
         sys.argv.append("--api")
     if "--ckpt" not in sys.argv:
         sys.argv.append("--ckpt")
-        sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
+        sys.argv.append("./test/test_files/empty.pt")
     if "--skip-torch-cuda-test" not in sys.argv:
         sys.argv.append("--skip-torch-cuda-test")
     if "--disable-nan-check" not in sys.argv:
         sys.argv.append("--disable-nan-check")
-    if "--no-tests" not in sys.argv:
-        sys.argv.append("--no-tests")
 
     print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
 
     os.environ['COMMANDLINE_ARGS'] = ""
-    with open(os.path.join(script_path, 'test/stdout.txt'), "w", encoding="utf8") as stdout, open(os.path.join(script_path, 'test/stderr.txt'), "w", encoding="utf8") as stderr:
+    with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
         proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
 
     import test.server_poll

modules/api/api.py

@@ -3,15 +3,11 @@ import io
 import time
 import datetime
 import uvicorn
-import gradio as gr
 from threading import Lock
 from io import BytesIO
 from gradio.processing_utils import decode_base64_to_file
-from fastapi import APIRouter, Depends, FastAPI, Request, Response
+from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
-from fastapi.exceptions import HTTPException
-from fastapi.responses import JSONResponse
-from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
@@ -22,8 +18,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
+from modules.sd_models import checkpoints_list, find_checkpoint_config
-from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -94,16 +89,6 @@ def encode_pil_to_base64(image):
     return base64.b64encode(bytes_data)
 
 def api_middleware(app: FastAPI):
-    rich_available = True
-    try:
-        import anyio # importing just so it can be placed on silent list
-        import starlette # importing just so it can be placed on silent list
-        from rich.console import Console
-        console = Console()
-    except:
-        import traceback
-        rich_available = False
-
     @app.middleware("http")
     async def log_and_time(req: Request, call_next):
         ts = time.time()
@@ -124,36 +109,6 @@ def api_middleware(app: FastAPI):
             ))
         return res
 
-    def handle_exception(request: Request, e: Exception):
-        err = {
-            "error": type(e).__name__,
-            "detail": vars(e).get('detail', ''),
-            "body": vars(e).get('body', ''),
-            "errors": str(e),
-        }
-        print(f"API error: {request.method}: {request.url} {err}")
-        if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
-            if rich_available:
-                console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
-            else:
-                traceback.print_exc()
-        return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
-
-    @app.middleware("http")
-    async def exception_handling(request: Request, call_next):
-        try:
-            return await call_next(request)
-        except Exception as e:
-            return handle_exception(request, e)
-
-    @app.exception_handler(Exception)
-    async def fastapi_exception_handler(request: Request, e: Exception):
-        return handle_exception(request, e)
-
-    @app.exception_handler(HTTPException)
-    async def http_exception_handler(request: Request, e: HTTPException):
-        return handle_exception(request, e)
-
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
@@ -194,12 +149,6 @@ class Api:
         self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
         self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
         self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
-        self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
-        self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
-        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
-
-        self.default_script_arg_txt2img = []
-        self.default_script_arg_img2img = []
 
     def add_api_route(self, path: str, endpoint, **kwargs):
         if shared.cmd_opts.api_auth:
@@ -213,111 +162,47 @@ class Api:
 
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
-    def get_selectable_script(self, script_name, script_runner):
+    def get_script(self, script_name, script_runner):
-        if script_name is None or script_name == "":
+        if script_name is None:
             return None, None
 
+        if not script_runner.scripts:
+            script_runner.initialize_scripts(False)
+            ui.create_ui()
+
         script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
         script = script_runner.selectable_scripts[script_idx]
         return script, script_idx
 
-    def get_scripts_list(self):
-        t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
-        i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
-
-        return ScriptsList(txt2img = t2ilist, img2img = i2ilist)  
-
-    def get_script(self, script_name, script_runner):
-        if script_name is None or script_name == "":
-            return None, None
-        
-        script_idx = script_name_to_index(script_name, script_runner.scripts)
-        return script_runner.scripts[script_idx]
-
-    def init_default_script_args(self, script_runner):
-        #find max idx from the scripts in runner and generate a none array to init script_args
-        last_arg_index = 1
-        for script in script_runner.scripts:
-            if last_arg_index < script.args_to:
-                last_arg_index = script.args_to
-        # None everywhere except position 0 to initialize script args
-        script_args = [None]*last_arg_index
-        script_args[0] = 0
-
-        # get default values
-        with gr.Blocks(): # will throw errors calling ui function without this
-            for script in script_runner.scripts:
-                if script.ui(script.is_img2img):
-                    ui_default_values = []
-                    for elem in script.ui(script.is_img2img):
-                        ui_default_values.append(elem.value)
-                    script_args[script.args_from:script.args_to] = ui_default_values
-        return script_args
-
-    def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
-        script_args = default_script_args.copy()
-        # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
-        if selectable_scripts:
-            script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
-            script_args[0] = selectable_idx + 1
-
-        # Now check for always on scripts
-        if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
-            for alwayson_script_name in request.alwayson_scripts.keys():
-                alwayson_script = self.get_script(alwayson_script_name, script_runner)
-                if alwayson_script == None:
-                    raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
-                # Selectable script in always on script param check
-                if alwayson_script.alwayson == False:
-                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
-                # always on script with no arg should always run so you don't really need to add them to the requests
-                if "args" in request.alwayson_scripts[alwayson_script_name]:
-                    script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
-        return script_args
-
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
-        script_runner = scripts.scripts_txt2img
+        script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img)
-        if not script_runner.scripts:
-            script_runner.initialize_scripts(False)
-            ui.create_ui()
-        if not self.default_script_arg_txt2img:
-            self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
-        selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
 
-        populate = txt2imgreq.copy(update={  # Override __init__ params
+        populate = txt2imgreq.copy(update={ # Override __init__ params
             "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
-            "do_not_save_samples": not txt2imgreq.save_images,
+            "do_not_save_samples": True,
-            "do_not_save_grid": not txt2imgreq.save_images,
+            "do_not_save_grid": True
-        })
+            }
+        )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
 
         args = vars(populate)
         args.pop('script_name', None)
-        args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
-        args.pop('alwayson_scripts', None)
-
-        script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
-
-        send_images = args.pop('send_images', True)
-        args.pop('save_images', None)
 
         with self.queue_lock:
             p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
-            p.scripts = script_runner
-            p.outpath_grids = opts.outdir_txt2img_grids
-            p.outpath_samples = opts.outdir_txt2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if script is not None:
-                p.script_args = script_args
+                p.outpath_grids = opts.outdir_txt2img_grids
-                processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                p.outpath_samples = opts.outdir_txt2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_txt2img.run(p, *p.script_args)
             else:
-                p.script_args = tuple(script_args) # Need to pass args as tuple here
                 processed = process_images(p)
             shared.state.end()
 
-        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
+        b64images = list(map(encode_pil_to_base64, processed.images))
 
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
@@ -326,55 +211,41 @@ class Api:
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
 
+        script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img)
+
         mask = img2imgreq.mask
         if mask:
             mask = decode_base64_to_image(mask)
 
-        script_runner = scripts.scripts_img2img
+        populate = img2imgreq.copy(update={ # Override __init__ params
-        if not script_runner.scripts:
-            script_runner.initialize_scripts(True)
-            ui.create_ui()
-        if not self.default_script_arg_img2img:
-            self.default_script_arg_img2img = self.init_default_script_args(script_runner)
-        selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
-
-        populate = img2imgreq.copy(update={  # Override __init__ params
             "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
-            "do_not_save_samples": not img2imgreq.save_images,
+            "do_not_save_samples": True,
-            "do_not_save_grid": not img2imgreq.save_images,
+            "do_not_save_grid": True,
-            "mask": mask,
+            "mask": mask
-        })
+            }
+        )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
         args.pop('script_name', None)
-        args.pop('script_args', None)  # will refeed them to the pipeline directly after initializing them
-        args.pop('alwayson_scripts', None)
-
-        script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
-
-        send_images = args.pop('send_images', True)
-        args.pop('save_images', None)
 
         with self.queue_lock:
             p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
             p.init_images = [decode_base64_to_image(x) for x in init_images]
-            p.scripts = script_runner
-            p.outpath_grids = opts.outdir_img2img_grids
-            p.outpath_samples = opts.outdir_img2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if script is not None:
-                p.script_args = script_args
+                p.outpath_grids = opts.outdir_img2img_grids
-                processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                p.outpath_samples = opts.outdir_img2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_img2img.run(p, *p.script_args)
             else:
-                p.script_args = tuple(script_args) # Need to pass args as tuple here
                 processed = process_images(p)
             shared.state.end()
 
-        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
+        b64images = list(map(encode_pil_to_base64, processed.images))
 
         if not img2imgreq.include_init_images:
             img2imgreq.init_images = None
@@ -476,16 +347,6 @@ class Api:
 
         return {}
 
-    def unloadapi(self):
-        unload_model_weights()
-
-        return {}
-
-    def reloadapi(self):
-        reload_model_weights()
-
-        return {}
-
     def skip(self):
         shared.state.skip()
 
@@ -526,7 +387,7 @@ class Api:
         ]
 
     def get_sd_models(self):
-        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
+        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -636,7 +497,7 @@ class Api:
             if not apply_optimizations:
                 sd_hijack.undo_optimizations()
             try:
-                hypernetwork, filename = train_hypernetwork(**args)
+                hypernetwork, filename = train_hypernetwork(*args)
             except Exception as e:
                 error = e
             finally:

modules/api/models.py

@@ -14,8 +14,8 @@ API_NOT_ALLOWED = [
     "outpath_samples",
     "outpath_grids",
     "sampler_index",
-    # "do_not_save_samples",
+    "do_not_save_samples",
-    # "do_not_save_grid",
+    "do_not_save_grid",
     "extra_generation_params",
     "overlay_images",
     "do_not_reload_embeddings",
@@ -100,31 +100,13 @@ class PydanticModelGenerator:
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img",
     StableDiffusionProcessingTxt2Img,
-    [
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
-        {"key": "sampler_index", "type": str, "default": "Euler"},
-        {"key": "script_name", "type": str, "default": None},
-        {"key": "script_args", "type": list, "default": []},
-        {"key": "send_images", "type": bool, "default": True},
-        {"key": "save_images", "type": bool, "default": False},
-        {"key": "alwayson_scripts", "type": dict, "default": {}},
-    ]
 ).generate_model()
 
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img",
     StableDiffusionProcessingImg2Img,
-    [
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
-        {"key": "sampler_index", "type": str, "default": "Euler"},
-        {"key": "init_images", "type": list, "default": None},
-        {"key": "denoising_strength", "type": float, "default": 0.75},
-        {"key": "mask", "type": str, "default": None},
-        {"key": "include_init_images", "type": bool, "default": False, "exclude" : True},
-        {"key": "script_name", "type": str, "default": None},
-        {"key": "script_args", "type": list, "default": []},
-        {"key": "send_images", "type": bool, "default": True},
-        {"key": "save_images", "type": bool, "default": False},
-        {"key": "alwayson_scripts", "type": dict, "default": {}},
-    ]
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
@@ -246,7 +228,7 @@ class SDModelItem(BaseModel):
     hash: Optional[str] = Field(title="Short hash")
     sha256: Optional[str] = Field(title="sha256 hash")
     filename: str = Field(title="Filename")
-    config: Optional[str] = Field(title="Config file")
+    config: str = Field(title="Config file")
 
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
@@ -285,7 +267,3 @@ class EmbeddingsResponse(BaseModel):
 class MemoryResponse(BaseModel):
     ram: dict = Field(title="RAM", description="System memory stats")
     cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
-
-class ScriptsList(BaseModel):
-    txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
-    img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")

modules/cmd_args.py

@@ -1,103 +0,0 @@
-import argparse
-import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS)  # allows running as root; implemented outside of webui
-parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
-parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
-parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
-parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
-parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
-parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
-parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
-parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
-parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
-parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
-parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
-parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
-parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
-parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
-parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
-parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
-parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
-parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
-parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
-parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
-parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
-parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
-parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
-parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
-parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
-parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
-parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
-parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
-parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
-parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
-parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
-parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
-parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
-parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
-parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
-parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
-parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
-parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
-parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
-parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
-parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
-parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
-parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
-parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
-parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
-parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
-parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)

modules/codeformer_model.py

@@ -8,7 +8,7 @@ import torch
 import modules.face_restoration
 import modules.shared
 from modules import shared, devices, modelloader
-from modules.paths import models_path
+from modules.paths import script_path, models_path
 
 # codeformer people made a choice to include modified basicsr library to their project which makes
 # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
@@ -55,7 +55,7 @@ def setup_model(dirname):
                 if self.net is not None and self.face_helper is not None:
                     self.net.to(devices.device_codeformer)
                     return self.net, self.face_helper
-                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
+                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
                 if len(model_paths) != 0:
                     ckpt_path = model_paths[0]
                 else:

modules/deepbooru_model.py

@@ -2,8 +2,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from modules import devices
-
 # see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
 
 
@@ -198,7 +196,7 @@ class DeepDanbooruModel(nn.Module):
         t_358, = inputs
         t_359 = t_358.permute(*[0, 3, 1, 2])
         t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
-        t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
+        t_360 = self.n_Conv_0(t_359_padded)
         t_361 = F.relu(t_360)
         t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
         t_362 = self.n_MaxPool_0(t_361)

modules/devices.py

@@ -1,17 +1,21 @@
-import sys
+import sys, os, shlex
 import contextlib
 import torch
 from modules import errors
-
+from packaging import version
-if sys.platform == "darwin":
-    from modules import mac_specific
 
 
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
+# check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if sys.platform != "darwin":
+    if not getattr(torch, 'has_mps', False):
         return False
-    else:
+    try:
-        return mac_specific.has_mps
+        torch.zeros(1).to(torch.device("mps"))
+        return True
+    except Exception:
+        return False
+
 
 def extract_device_id(args, name):
     for x in range(len(args)):
@@ -30,18 +34,14 @@ def get_cuda_device_string():
     return "cuda"
 
 
-def get_optimal_device_name():
+def get_optimal_device():
     if torch.cuda.is_available():
-        return get_cuda_device_string()
+        return torch.device(get_cuda_device_string())
 
     if has_mps():
-        return "mps"
+        return torch.device("mps")
 
-    return "cpu"
+    return cpu
-
-
-def get_optimal_device():
-    return torch.device(get_optimal_device_name())
 
 
 def get_device_for(task):
@@ -79,16 +79,6 @@ cpu = torch.device("cpu")
 device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
-dtype_unet = torch.float16
-unet_needs_upcast = False
-
-
-def cond_cast_unet(input):
-    return input.to(dtype_unet) if unet_needs_upcast else input
-
-
-def cond_cast_float(input):
-    return input.float() if unet_needs_upcast else input
 
 
 def randn(seed, shape):
@@ -116,10 +106,6 @@ def autocast(disable=False):
     return torch.autocast("cuda")
 
 
-def without_autocast(disable=False):
-    return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
-
-
 class NansException(Exception):
     pass
 
@@ -137,7 +123,7 @@ def test_for_nans(x, where):
         message = "A tensor with all NaNs was produced in Unet."
 
         if not shared.cmd_opts.no_half:
-            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
+            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."
 
     elif where == "vae":
         message = "A tensor with all NaNs was produced in VAE."
@@ -147,6 +133,60 @@ def test_for_nans(x, where):
     else:
         message = "A tensor with all NaNs was produced."
 
-    message += " Use --disable-nan-check commandline argument to disable this check."
-
     raise NansException(message)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+orig_tensor_to = torch.Tensor.to
+def tensor_to_fix(self, *args, **kwargs):
+    if self.device.type != 'mps' and \
+       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
+       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
+        self = self.contiguous()
+    return orig_tensor_to(self, *args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+orig_layer_norm = torch.nn.functional.layer_norm
+def layer_norm_fix(*args, **kwargs):
+    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
+        args = list(args)
+        args[0] = args[0].contiguous()
+    return orig_layer_norm(*args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+orig_tensor_numpy = torch.Tensor.numpy
+def numpy_fix(self, *args, **kwargs):
+    if self.requires_grad:
+        self = self.detach()
+    return orig_tensor_numpy(self, *args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+orig_cumsum = torch.cumsum
+orig_Tensor_cumsum = torch.Tensor.cumsum
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+    if input.device.type == 'mps':
+        output_dtype = kwargs.get('dtype', input.dtype)
+        if output_dtype == torch.int64:
+            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
+        elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
+            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
+    return cumsum_func(input, *args, **kwargs)
+
+
+if has_mps():
+    if version.parse(torch.__version__) < version.parse("1.13"):
+        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+        torch.Tensor.to = tensor_to_fix
+        torch.nn.functional.layer_norm = layer_norm_fix
+        torch.Tensor.numpy = numpy_fix
+    elif version.parse(torch.__version__) > version.parse("1.13.1"):
+        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
+        cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
+        torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
+        torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
+        orig_narrow = torch.narrow
+        torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
+

modules/esrgan_model_arch.py

@@ -1,6 +1,5 @@
 # this file is adapted from https://github.com/victorca25/iNNfer
 
-from collections import OrderedDict
 import math
 import functools
 import torch

modules/extensions.py

@@ -2,25 +2,17 @@ import os
 import sys
 import traceback
 
-import time
 import git
 
-from modules import shared
+from modules import paths, shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir
 
 extensions = []
-
+extensions_dir = os.path.join(paths.script_path, "extensions")
-if not os.path.exists(extensions_dir):
+extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
-    os.makedirs(extensions_dir)
 
 
 def active():
-    if shared.opts.disable_all_extensions == "all":
+    return [x for x in extensions if x.enabled]
-        return []
-    elif shared.opts.disable_all_extensions == "extra":
-        return [x for x in extensions if x.enabled and x.is_builtin]
-    else:
-        return [x for x in extensions if x.enabled]
 
 
 class Extension:
@@ -31,34 +23,21 @@ class Extension:
         self.status = ''
         self.can_update = False
         self.is_builtin = is_builtin
-        self.version = ''
-        self.remote = None
-        self.have_info_from_repo = False
-
-    def read_info_from_repo(self):
-        if self.have_info_from_repo:
-            return
-
-        self.have_info_from_repo = True
 
         repo = None
         try:
-            if os.path.exists(os.path.join(self.path, ".git")):
+            if os.path.exists(os.path.join(path, ".git")):
-                repo = git.Repo(self.path)
+                repo = git.Repo(path)
         except Exception:
-            print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
+            print(f"Error reading github repository info from {path}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
         if repo is None or repo.bare:
             self.remote = None
         else:
             try:
-                self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
-                head = repo.head.commit
+                self.status = 'unknown'
-                ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
-                self.version = f'{head.hexsha[:8]} ({ts})'
-
             except Exception:
                 self.remote = None
 
@@ -79,7 +58,7 @@ class Extension:
 
     def check_updates(self):
         repo = git.Repo(self.path)
-        for fetch in repo.remote().fetch(dry_run=True):
+        for fetch in repo.remote().fetch("--dry-run"):
             if fetch.flags != fetch.HEAD_UPTODATE:
                 self.can_update = True
                 self.status = "behind"
@@ -92,8 +71,8 @@ class Extension:
         repo = git.Repo(self.path)
         # Fix: `error: Your local changes to the following files would be overwritten by merge`,
         # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
-        repo.git.fetch(all=True)
+        repo.git.fetch('--all')
-        repo.git.reset('origin', hard=True)
+        repo.git.reset('--hard', 'origin')
 
 
 def list_extensions():
@@ -102,12 +81,7 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    if shared.opts.disable_all_extensions == "all":
+    paths = []
-        print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
-    elif shared.opts.disable_all_extensions == "extra":
-        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
-
-    extension_paths = []
     for dirname in [extensions_dir, extensions_builtin_dir]:
         if not os.path.isdir(dirname):
             return
@@ -117,8 +91,9 @@ def list_extensions():
             if not os.path.isdir(path):
                 continue
 
-            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+            paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
 
-    for dirname, path, is_builtin in extension_paths:
+    for dirname, path, is_builtin in paths:
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
         extensions.append(extension)
+

modules/extra_networks_hypernet.py

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks
 from modules.hypernetworks import hypernetwork
 
 
@@ -7,12 +7,6 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
         super().__init__('hypernet')
 
     def activate(self, p, params_list):
-        additional = shared.opts.sd_hypernetwork
-
-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
-            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
-            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
-
         names = []
         multipliers = []
         for params in params_list:

modules/extras.py

@@ -6,7 +6,7 @@ import shutil
 import torch
 import tqdm
 
-from modules import shared, images, sd_models, sd_vae, sd_models_config
+from modules import shared, images, sd_models, sd_vae
 from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
@@ -37,7 +37,7 @@ def run_pnginfo(image):
 
 def create_config(ckpt_result, config_source, a, b, c):
     def config(x):
-        res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
+        res = sd_models.find_checkpoint_config(x) if x else None
         return res if res != shared.sd_default_config else None
 
     if config_source == 0:
@@ -132,7 +132,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
 
     result_is_inpainting_model = False
-    result_is_instruct_pix2pix_model = False
 
     if theta_func2:
         shared.state.textinfo = f"Loading B"
@@ -186,16 +185,11 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
             if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
                 if a.shape[1] == 4 and b.shape[1] == 9:
                     raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
-                if a.shape[1] == 4 and b.shape[1] == 8:
-                    raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.")
 
-                if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model...
+                assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
-                    theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common.  Otherwise we get an error due to dimension mismatch.
+
-                    result_is_instruct_pix2pix_model = True
+                theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
-                else:
+                result_is_inpainting_model = True
-                    assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
-                    theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
-                    result_is_inpainting_model = True
             else:
                 theta_0[key] = theta_func2(a, b, multiplier)
 
@@ -232,7 +226,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
 
     filename = filename_generator() if custom_name == '' else custom_name
     filename += ".inpainting" if result_is_inpainting_model else ""
-    filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else ""
     filename += "." + checkpoint_format
 
     output_modelname = os.path.join(ckpt_dir, filename)

modules/generation_parameters_copypaste.py

@@ -1,5 +1,4 @@
 import base64
-import html
 import io
 import math
 import os
@@ -7,34 +6,24 @@ import re
 from pathlib import Path
 
 import gradio as gr
-from modules.paths import data_path
+from modules.shared import script_path
 from modules import shared, ui_tempdir, script_callbacks
 import tempfile
 from PIL import Image
 
-re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
+re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
-
 paste_fields = {}
-registered_param_bindings = []
+bind_list = []
-
-
-class ParamBinding:
-    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
-        self.paste_button = paste_button
-        self.tabname = tabname
-        self.source_text_component = source_text_component
-        self.source_image_component = source_image_component
-        self.source_tabname = source_tabname
-        self.override_settings_component = override_settings_component
-        self.paste_field_names = paste_field_names
 
 
 def reset():
     paste_fields.clear()
+    bind_list.clear()
 
 
 def quote(text):
@@ -75,8 +64,8 @@ def image_from_url_text(filedata):
     return image
 
 
-def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+def add_paste_fields(tabname, init_img, fields):
-    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
 
     # backwards compatibility for existing extensions
     import modules.ui
@@ -86,6 +75,26 @@ def add_paste_fields(tabname, init_img, fields, override_settings_component=None
         modules.ui.img2img_paste_fields = fields
 
 
+def integrate_settings_paste_fields(component_dict):
+    from modules import ui
+
+    settings_map = {
+        'CLIP_stop_at_last_layers': 'Clip skip',
+        'inpainting_mask_weight': 'Conditional mask weight',
+        'sd_model_checkpoint': 'Model hash',
+        'eta_noise_seed_delta': 'ENSD',
+        'initial_noise_multiplier': 'Noise multiplier',
+    }
+    settings_paste_fields = [
+        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
+        for k, v in settings_map.items()
+    ]
+
+    for tabname, info in paste_fields.items():
+        if info["fields"] is not None:
+            info["fields"] += settings_paste_fields
+
+
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
@@ -93,61 +102,9 @@ def create_buttons(tabs_list):
     return buttons
 
 
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
 def bind_buttons(buttons, send_image, send_generate_info):
-    """old function for backwards compatibility; do not use this, use register_paste_params_button"""
+    bind_list.append([buttons, send_image, send_generate_info])
-    for tabname, button in buttons.items():
-        source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
-        source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
-
-        register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
-
-
-def register_paste_params_button(binding: ParamBinding):
-    registered_param_bindings.append(binding)
-
-
-def connect_paste_params_buttons():
-    binding: ParamBinding
-    for binding in registered_param_bindings:
-        destination_image_component = paste_fields[binding.tabname]["init_img"]
-        fields = paste_fields[binding.tabname]["fields"]
-        override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
-
-        destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
-        destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
-
-        if binding.source_image_component and destination_image_component:
-            if isinstance(binding.source_image_component, gr.Gallery):
-                func = send_image_and_dimensions if destination_width_component else image_from_url_text
-                jsfunc = "extract_image_from_gallery"
-            else:
-                func = send_image_and_dimensions if destination_width_component else lambda x: x
-                jsfunc = None
-
-            binding.paste_button.click(
-                fn=func,
-                _js=jsfunc,
-                inputs=[binding.source_image_component],
-                outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
-            )
-
-        if binding.source_text_component is not None and fields is not None:
-            connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
-
-        if binding.source_tabname is not None and fields is not None:
-            paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
-            binding.paste_button.click(
-                fn=lambda *x: x,
-                inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
-                outputs=[field for field, name in fields if name in paste_field_names],
-            )
-
-        binding.paste_button.click(
-            fn=None,
-            _js=f"switch_to_{binding.tabname}",
-            inputs=None,
-            outputs=None,
-        )
 
 
 def send_image_and_dimensions(x):
@@ -166,6 +123,49 @@ def send_image_and_dimensions(x):
     return img, w, h
 
 
+def run_bind():
+    for buttons, source_image_component, send_generate_info in bind_list:
+        for tab in buttons:
+            button = buttons[tab]
+            destination_image_component = paste_fields[tab]["init_img"]
+            fields = paste_fields[tab]["fields"]
+
+            destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+            destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+            if source_image_component and destination_image_component:
+                if isinstance(source_image_component, gr.Gallery):
+                    func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                    jsfunc = "extract_image_from_gallery"
+                else:
+                    func = send_image_and_dimensions if destination_width_component else lambda x: x
+                    jsfunc = None
+
+                button.click(
+                    fn=func,
+                    _js=jsfunc,
+                    inputs=[source_image_component],
+                    outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                )
+
+            if send_generate_info and fields is not None:
+                if send_generate_info in paste_fields:
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
+                    button.click(
+                        fn=lambda *x: x,
+                        inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in fields if name in paste_field_names],
+                    )
+                else:
+                    connect_paste(button, fields, send_generate_info)
+
+            button.click(
+                fn=None,
+                _js=f"switch_to_{tab}",
+                inputs=None,
+                outputs=None,
+            )
+
 
 def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
@@ -243,7 +243,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     done_with_prompt = False
 
     *lines, lastline = x.strip().split("\n")
-    if len(re_param.findall(lastline)) < 3:
+    if not re_params.match(lastline):
         lines.append(lastline)
         lastline = ''
 
@@ -262,7 +262,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
-        v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
         m = re_imagesize.match(v)
         if m is not None:
             res[k+"-1"] = m.group(1)
@@ -287,59 +286,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     return res
 
 
-settings_map = {}
+def connect_paste(button, paste_fields, input_comp, jsfunc=None):
-
-
-
-infotext_to_setting_name_mapping = [
-    ('Clip skip', 'CLIP_stop_at_last_layers', ),
-    ('Conditional mask weight', 'inpainting_mask_weight'),
-    ('Model hash', 'sd_model_checkpoint'),
-    ('ENSD', 'eta_noise_seed_delta'),
-    ('Noise multiplier', 'initial_noise_multiplier'),
-    ('Eta', 'eta_ancestral'),
-    ('Eta DDIM', 'eta_ddim'),
-    ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
-    ('UniPC variant', 'uni_pc_variant'),
-    ('UniPC skip type', 'uni_pc_skip_type'),
-    ('UniPC order', 'uni_pc_order'),
-    ('UniPC lower order final', 'uni_pc_lower_order_final'),
-]
-
-
-def create_override_settings_dict(text_pairs):
-    """creates processing's override_settings parameters from gradio's multiselect
-
-    Example input:
-        ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
-
-    Example output:
-        {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
-    """
-
-    res = {}
-
-    params = {}
-    for pair in text_pairs:
-        k, v = pair.split(":", maxsplit=1)
-
-        params[k] = v.strip()
-
-    for param_name, setting_name in infotext_to_setting_name_mapping:
-        value = params.get(param_name, None)
-
-        if value is None:
-            continue
-
-        res[setting_name] = shared.opts.cast_value(setting_name, value)
-
-    return res
-
-
-def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
-            filename = os.path.join(data_path, "params.txt")
+            filename = os.path.join(script_path, "params.txt")
             if os.path.exists(filename):
                 with open(filename, "r", encoding="utf8") as file:
                     prompt = file.read()
@@ -373,42 +323,11 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
         return res
 
-    if override_settings_component is not None:
-        def paste_settings(params):
-            vals = {}
-
-            for param_name, setting_name in infotext_to_setting_name_mapping:
-                v = params.get(param_name, None)
-                if v is None:
-                    continue
-
-                if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
-                    continue
-
-                v = shared.opts.cast_value(setting_name, v)
-                current_value = getattr(shared.opts, setting_name, None)
-
-                if v == current_value:
-                    continue
-
-                vals[param_name] = v
-
-            vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
-
-            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
-
-        paste_fields = paste_fields + [(override_settings_component, paste_settings)]
-
     button.click(
         fn=paste_func,
+        _js=jsfunc,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
-    button.click(
-        fn=None,
-        _js=f"recalculate_prompts_{tabname}",
-        inputs=[],
-        outputs=[],
-    )
 
 

modules/gfpgan_model.py

@@ -6,11 +6,12 @@ import facexlib
 import gfpgan
 
 import modules.face_restoration
-from modules import paths, shared, devices, modelloader
+from modules import shared, devices, modelloader
+from modules.paths import models_path
 
 model_dir = "GFPGAN"
 user_path = None
-model_path = os.path.join(paths.models_path, model_dir)
+model_path = os.path.join(models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
 have_gfpgan = False
 loaded_gfpgan_model = None

modules/hashes.py

@@ -4,11 +4,8 @@ import os.path
 
 import filelock
 
-from modules import shared
-from modules.paths import data_path
 
-
+cache_filename = "cache.json"
-cache_filename = os.path.join(data_path, "cache.json")
 cache_data = None
 
 
@@ -69,9 +66,6 @@ def sha256(filename, title):
     if sha256_value is not None:
         return sha256_value
 
-    if shared.cmd_opts.no_hashing:
-        return None
-
     print(f"Calculating sha256 for {filename}: ", end='')
     sha256_value = calculate_sha256(filename)
     print(f"{sha256_value}")

modules/hypernetworks/hypernetwork.py

@@ -307,12 +307,12 @@ class Hypernetwork:
     def shorthash(self):
         sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
 
-        return sha256[0:10] if sha256 else None
+        return sha256[0:10]
 
 
 def list_hypernetworks(path):
     res = {}
-    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
@@ -380,8 +380,8 @@ def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
         layer.hyper_k = hypernetwork_layers[0]
         layer.hyper_v = hypernetwork_layers[1]
 
-    context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
+    context_k = hypernetwork_layers[0](context_k)
-    context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
+    context_v = hypernetwork_layers[1](context_v)
     return context_k, context_v
 
 
@@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
     shared.reload_hypernetworks()
 
 
-def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
     pin_memory = shared.opts.pin_memory
 
-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
 
     if shared.opts.save_training_settings_to_txt:
         saved_params = dict(
@@ -640,19 +640,13 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
-                    if use_weight:
-                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
                         shared.sd_model.cond_stage_model.to(devices.device)
                         c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                     else:
                         c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
-                    if use_weight:
+                    loss = shared.sd_model(x, c)[0] / gradient_step
-                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
-                        del w
-                    else:
-                        loss = shared.sd_model.forward(x, c)[0] / gradient_step
                     del x
                     del c
 

modules/images.py

@@ -16,9 +16,8 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 import json
-import hashlib
 
-from modules import sd_samplers, shared, script_callbacks, errors
+from modules import sd_samplers, shared, script_callbacks
 from modules.shared import opts, cmd_opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -37,8 +36,6 @@ def image_grid(imgs, batch_size=1, rows=None):
         else:
             rows = math.sqrt(len(imgs))
             rows = round(rows)
-    if rows > len(imgs):
-        rows = len(imgs)
 
     cols = math.ceil(len(imgs) / rows)
 
@@ -131,7 +128,7 @@ class GridAnnotation:
         self.size = None
 
 
-def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
     def wrap(drawing, text, font, line_length):
         lines = ['']
         for word in text.split():
@@ -195,35 +192,32 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
             line.allowed_width = allowed_width
 
     hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
-    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
+    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
+                        ver_texts]
 
-    pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
+    pad_top = max(hor_text_heights) + line_spacing * 2
 
-    result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
+    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
-
+    result.paste(im, (pad_left, pad_top))
-    for row in range(rows):
-        for col in range(cols):
-            cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
-            result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row))
 
     d = ImageDraw.Draw(result)
 
     for col in range(cols):
-        x = pad_left + (width + margin) * col + width / 2
+        x = pad_left + width * col + width / 2
         y = pad_top / 2 - hor_text_heights[col] / 2
 
         draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
 
     for row in range(rows):
         x = pad_left / 2
-        y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2
+        y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
 
         draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
 
     return result
 
 
-def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
+def draw_prompt_matrix(im, width, height, all_prompts):
     prompts = all_prompts[1:]
     boundary = math.ceil(len(prompts) / 2)
 
@@ -233,7 +227,7 @@ def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
     hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
     ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
 
-    return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin)
+    return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
 
 def resize_image(resize_mode, im, width, height, upscaler_name=None):
@@ -261,12 +255,9 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
 
         if scale > 1.0:
             upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
-            if len(upscalers) == 0:
+            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
-                upscaler = shared.sd_upscalers[0]
-                print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
-            else:
-                upscaler = upscalers[0]
 
+            upscaler = upscalers[0]
             im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
 
         if im.width != w or im.height != h:
@@ -347,7 +338,6 @@ class FilenameGenerator:
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-        'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
         'prompt': lambda self: sanitize_filename_part(self.prompt),
         'prompt_no_styles': lambda self: self.prompt_no_style(),
         'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
@@ -556,10 +546,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         elif extension.lower() in (".jpg", ".jpeg", ".webp"):
             if image_to_save.mode == 'RGBA':
                 image_to_save = image_to_save.convert("RGB")
-            elif image_to_save.mode == 'I;16':
-                image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
 
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
             if opts.enable_pnginfo and info is not None:
                 exif_bytes = piexif.dump({
@@ -576,28 +564,21 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         os.replace(temp_file_path, filename_without_extension + extension)
 
     fullfn_without_extension, extension = os.path.splitext(params.filename)
-    if hasattr(os, 'statvfs'):
-        max_name_len = os.statvfs(path).f_namemax
-        fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))]
-        params.filename = fullfn_without_extension + extension
-        fullfn = params.filename
     _atomically_save_image(image, fullfn_without_extension, extension)
 
     image.already_saved_as = fullfn
 
-    oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
+    target_side_length = 4000
-    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
+    oversize = image.width > target_side_length or image.height > target_side_length
+    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
         ratio = image.width / image.height
 
         if oversize and ratio > 1:
-            image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
+            image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
         elif oversize:
-            image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
+            image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
 
-        try:
+        _atomically_save_image(image, fullfn_without_extension, ".jpg")
-            _atomically_save_image(image, fullfn_without_extension, ".jpg")
-        except Exception as e:
-            errors.display(e, "saving image as downscaled JPG")
 
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
@@ -648,8 +629,6 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}
 
 
 def image_data(data):
-    import gradio as gr
-
     try:
         image = Image.open(io.BytesIO(data))
         textinfo, _ = read_info_from_image(image)
@@ -665,7 +644,7 @@ def image_data(data):
     except Exception:
         pass
 
-    return gr.update(), None
+    return '', None
 
 
 def flatten(img, bgcolor):

modules/img2img.py

@@ -7,7 +7,6 @@ import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
 
 from modules import devices, sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -17,18 +16,11 @@ import modules.images as images
 import modules.scripts
 
 
-def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
+def process_batch(p, input_dir, output_dir, args):
     processing.fix_seed(p)
 
     images = shared.listfiles(input_dir)
 
-    is_inpaint_batch = False
-    if inpaint_mask_dir:
-        inpaint_masks = shared.listfiles(inpaint_mask_dir)
-        is_inpaint_batch = len(inpaint_masks) > 0
-    if is_inpaint_batch:
-        print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
-
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
     save_normally = output_dir == ''
@@ -51,15 +43,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
         img = ImageOps.exif_transpose(img)
         p.init_images = [img] * p.batch_size
 
-        if is_inpaint_batch:
-            # try to find corresponding mask for an image using simple filename matching
-            mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
-            # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
-                mask_image_path = inpaint_masks[0]
-            mask_image = Image.open(mask_image_path)
-            p.image_mask = mask_image
-
         proc = modules.scripts.scripts_img2img.run(p, *args)
         if proc is None:
             proc = process_images(p)
@@ -73,14 +56,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
 
             if not save_normally:
                 os.makedirs(output_dir, exist_ok=True)
-                if processed_image.mode == 'RGBA':
-                    processed_image = processed_image.convert("RGB")
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
-    override_settings = create_override_settings_dict(override_settings_texts)
-
     is_batch = mode == 5
 
     if mode == 0:  # img2img
@@ -144,11 +123,9 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         inpainting_fill=inpainting_fill,
         resize_mode=resize_mode,
         denoising_strength=denoising_strength,
-        image_cfg_scale=image_cfg_scale,
         inpaint_full_res=inpaint_full_res,
         inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,
-        override_settings=override_settings,
     )
 
     p.scripts = modules.scripts.scripts_txt2img
@@ -157,13 +134,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
-    if mask:
+    p.extra_generation_params["Mask blur"] = mask_blur
-        p.extra_generation_params["Mask blur"] = mask_blur
 
     if is_batch:
         assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
 
-        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
+        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args)
 
         processed = Processed(p, [], p.seed, "")
     else:

modules/interrogate.py

@@ -12,7 +12,7 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths, shared, lowvram, modelloader, errors
+from modules import devices, paths, lowvram, modelloader, errors
 
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'

modules/lowvram.py

@@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
     if hasattr(sd_model.cond_stage_model, 'model'):
         sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
 
-    # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
+    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
     sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
 
     # register hooks for those the first three models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,8 +69,6 @@ def setup_for_low_vram(sd_model, use_medvram):
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     if sd_model.depth_model:
         sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
-    if sd_model.embedder:
-        sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if hasattr(sd_model.cond_stage_model, 'model'):

modules/mac_specific.py

@@ -1,59 +0,0 @@
-import torch
-import platform
-from modules import paths
-from modules.sd_hijack_utils import CondFunc
-from packaging import version
-
-
-# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
-# check `getattr` and try it for compatibility
-def check_for_mps() -> bool:
-    if not getattr(torch, 'has_mps', False):
-        return False
-    try:
-        torch.zeros(1).to(torch.device("mps"))
-        return True
-    except Exception:
-        return False
-has_mps = check_for_mps()
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
-def cumsum_fix(input, cumsum_func, *args, **kwargs):
-    if input.device.type == 'mps':
-        output_dtype = kwargs.get('dtype', input.dtype)
-        if output_dtype == torch.int64:
-            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
-        elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
-            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
-    return cumsum_func(input, *args, **kwargs)
-
-
-if has_mps:
-    # MPS fix for randn in torchsde
-    CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
-
-    if platform.mac_ver()[0].startswith("13.2."):
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
-        CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
-
-    if version.parse(torch.__version__) < version.parse("1.13"):
-        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
-
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-        CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
-                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
-        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
-                                                                                        lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
-        CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
-    elif version.parse(torch.__version__) > version.parse("1.13.1"):
-        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
-        cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
-        CondFunc('torch.cumsum', cumsum_fix_func, None)
-        CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
-        CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-    if version.parse(torch.__version__) == version.parse("2.0"):
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
-        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)

modules/memmon.py

@@ -23,16 +23,12 @@ class MemUsageMonitor(threading.Thread):
         self.data = defaultdict(int)
 
         try:
-            self.cuda_mem_get_info()
+            torch.cuda.mem_get_info()
             torch.cuda.memory_stats(self.device)
         except Exception as e:  # AMD or whatever
             print(f"Warning: caught exception '{e}', memory monitor disabled")
             self.disabled = True
 
-    def cuda_mem_get_info(self):
-        index = self.device.index if self.device.index is not None else torch.cuda.current_device()
-        return torch.cuda.mem_get_info(index)
-
     def run(self):
         if self.disabled:
             return
@@ -47,10 +43,10 @@ class MemUsageMonitor(threading.Thread):
                 self.run_flag.clear()
                 continue
 
-            self.data["min_free"] = self.cuda_mem_get_info()[0]
+            self.data["min_free"] = torch.cuda.mem_get_info()[0]
 
             while self.run_flag.is_set():
-                free, total = self.cuda_mem_get_info()
+                free, total = torch.cuda.mem_get_info()  # calling with self.device errors, torch bug?
                 self.data["min_free"] = min(self.data["min_free"], free)
 
                 time.sleep(1 / self.opts.memmon_poll_rate)
@@ -74,7 +70,7 @@ class MemUsageMonitor(threading.Thread):
 
     def read(self):
         if not self.disabled:
-            free, total = self.cuda_mem_get_info()
+            free, total = torch.cuda.mem_get_info()
             self.data["free"] = free
             self.data["total"] = total
 

modules/modelloader.py

@@ -4,8 +4,9 @@ import shutil
 import importlib
 from urllib.parse import urlparse
 
+from basicsr.utils.download_util import load_file_from_url
 from modules import shared
-from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
+from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
 
 
@@ -44,9 +45,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                     full_path = file
                     if os.path.isdir(full_path):
                         continue
-                    if os.path.islink(full_path) and not os.path.exists(full_path):
-                        print(f"Skipping broken symlink: {full_path}")
-                        continue
                     if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
                         continue
                     if len(ext_filter) != 0:
@@ -58,7 +56,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
-                from basicsr.utils.download_util import load_file_from_url
                 dl = load_file_from_url(model_url, model_path, True, download_name)
                 output.append(dl)
             else:
@@ -169,8 +166,4 @@ def load_upscalers():
         scaler = cls(commandline_options.get(cmd_name, None))
         datas += scaler.scalers
 
-    shared.sd_upscalers = sorted(
+    shared.sd_upscalers = datas
-        datas,
-        # Special case for UpscalerNone keeps it at the beginning of the list.
-        key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
-    )

modules/models/diffusion/uni_pc/__init__.py

@@ -1 +0,0 @@
-from .sampler import UniPCSampler

modules/models/diffusion/uni_pc/sampler.py

@@ -1,100 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-
-from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
-from modules import shared, devices
-
-
-class UniPCSampler(object):
-    def __init__(self, model, **kwargs):
-        super().__init__()
-        self.model = model
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
-        self.before_sample = None
-        self.after_sample = None
-        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != devices.device:
-                attr = attr.to(devices.device)
-        setattr(self, name, attr)
-
-    def set_hooks(self, before_sample, after_sample, after_update):
-        self.before_sample = before_sample
-        self.after_sample = after_sample
-        self.after_update = after_update
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                cbs = ctmp.shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            elif isinstance(conditioning, list):
-                for ctmp in conditioning:
-                    if ctmp.shape[0] != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        # print(f'Data shape for UniPC sampling is {size}')
-
-        device = self.model.betas.device
-        if x_T is None:
-            img = torch.randn(size, device=device)
-        else:
-            img = x_T
-
-        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
-
-        # SD 1.X is "noise", SD 2.X is "v"
-        model_type = "v" if self.model.parameterization == "v" else "noise"
-
-        model_fn = model_wrapper(
-            lambda x, t, c: self.model.apply_model(x, t, c),
-            ns,
-            model_type=model_type,
-            guidance_type="classifier-free",
-            #condition=conditioning,
-            #unconditional_condition=unconditional_conditioning,
-            guidance_scale=unconditional_guidance_scale,
-        )
-
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
-        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
-
-        return x.to(device), None

modules/models/diffusion/uni_pc/uni_pc.py

@@ -1,857 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-from tqdm.auto import trange
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    #condition=None,
-    #unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, None, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input, condition):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous, condition, unconditional_condition):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input, condition)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                if isinstance(condition, dict):
-                    assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
-                    for k in condition:
-                        if isinstance(condition[k], list):
-                            c_in[k] = [torch.cat([
-                                unconditional_condition[k][i],
-                                condition[k][i]]) for i in range(len(condition[k]))]
-                        else:
-                            c_in[k] = torch.cat([
-                                unconditional_condition[k],
-                                condition[k]])
-                elif isinstance(condition, list):
-                    c_in = list()
-                    assert isinstance(unconditional_condition, list)
-                    for i in range(len(condition)):
-                        c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
-                else:
-                    c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class UniPC:
-    def __init__(
-        self,
-        model_fn,
-        noise_schedule,
-        predict_x0=True,
-        thresholding=False,
-        max_val=1.,
-        variant='bh1',
-        condition=None,
-        unconditional_condition=None,
-        before_sample=None,
-        after_sample=None,
-        after_update=None
-    ):
-        """Construct a UniPC.
-
-        We support both data_prediction and noise_prediction.
-        """
-        self.model_fn_ = model_fn
-        self.noise_schedule = noise_schedule
-        self.variant = variant
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-        self.condition = condition
-        self.unconditional_condition = unconditional_condition
-        self.before_sample = before_sample
-        self.after_sample = after_sample
-        self.after_update = after_update
-
-    def dynamic_thresholding_fn(self, x0, t=None):
-        """
-        The dynamic thresholding method.
-        """
-        dims = x0.dim()
-        p = self.dynamic_thresholding_ratio
-        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
-        x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model(self, x, t):
-        cond = self.condition
-        uncond = self.unconditional_condition
-        if self.before_sample is not None:
-            x, t, cond, uncond = self.before_sample(x, t, cond, uncond)
-        res = self.model_fn_(x, t, cond, uncond)
-        if self.after_sample is not None:
-            x, t, cond, uncond, res = self.after_sample(x, t, cond, uncond, res)
-
-        if isinstance(res, tuple):
-            # (None, pred_x0)
-            res = res[1]
-
-        return res
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = steps
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
-        if len(t.shape) == 0:
-            t = t.view(-1)
-        if 'bh' in self.variant:
-            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-        else:
-            assert self.variant == 'vary_coeff'
-            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-
-    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_t = ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = (lambda_prev_i - lambda_prev_0) / h
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        K = len(rks)
-        # build C matrix
-        C = []
-
-        col = torch.ones_like(rks)
-        for k in range(1, K + 1):
-            C.append(col)
-            col = col * rks / (k + 1)
-        C = torch.stack(C, dim=1)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            C_inv_p = torch.linalg.inv(C[:-1, :-1])
-            A_p = C_inv_p
-
-        if use_corrector:
-            #print('using corrector')
-            C_inv = torch.linalg.inv(C)
-            A_c = C_inv
-
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)
-        h_phi_ks = []
-        factorial_k = 1
-        h_phi_k = h_phi_1
-        for k in range(1, K + 2):
-            h_phi_ks.append(h_phi_k)
-            h_phi_k = h_phi_k / hh - 1 / factorial_k
-            factorial_k *= (k + 1)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                sigma_t / sigma_prev_0 * x
-                - alpha_t * h_phi_1 * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        else:
-            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-            x_t_ = (
-                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
-                - (sigma_t * h_phi_1) * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        return x_t, model_t
-
-    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
-        #print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-        dims = x.dim()
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        R = []
-        b = []
-
-        hh = -h[0] if self.predict_x0 else h[0]
-        h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
-        h_phi_k = h_phi_1 / hh - 1
-
-        factorial_i = 1
-
-        if self.variant == 'bh1':
-            B_h = hh
-        elif self.variant == 'bh2':
-            B_h = torch.expm1(hh)
-        else:
-            raise NotImplementedError()
-
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= (i + 1)
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-
-        R = torch.stack(R)
-        b = torch.tensor(b, device=x.device)
-
-        # now predictor
-        use_predictor = len(D1s) > 0 and x_t is None
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            if x_t is None:
-                # for order 2, we use a simplified version
-                if order == 2:
-                    rhos_p = torch.tensor([0.5], device=b.device)
-                else:
-                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
-        else:
-            D1s = None
-
-        if use_corrector:
-            #print('using corrector')
-            # for order 1, we use a simplified version
-            if order == 1:
-                rhos_c = torch.tensor([0.5], device=b.device)
-            else:
-                rhos_c = torch.linalg.solve(R, b)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
-            )
-
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        else:
-            x_t_ = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
-            )
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        return x_t, model_t
-
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05, corrector=False,
-    ):
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'multistep':
-            assert steps >= order, "UniPC order must be < sampling steps"
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
-                    if model_x is None:
-                        model_x = self.model_fn(x, vec_t)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    model_prev_list.append(model_x)
-                    t_prev_list.append(vec_t)
-                for step in trange(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    #print('this step order:', step_order)
-                    if step == steps:
-                        #print('do not run corrector at the last step')
-                        use_corrector = False
-                    else:
-                        use_corrector = True
-                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        if model_x is None:
-                            model_x = self.model_fn(x, vec_t)
-                        model_prev_list[-1] = model_x
-        else:
-            raise NotImplementedError()
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]

modules/paths.py

@@ -1,11 +1,10 @@
+import argparse
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
-
 import modules.safe
 
-
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-# data_path = cmd_opts_pre.data
+models_path = os.path.join(script_path, "models")
 sys.path.insert(0, script_path)
 
 # search for directory of stable diffusion in following places

modules/paths_internal.py

@@ -1,22 +0,0 @@
-"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
-
-import argparse
-import os
-
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-
-sd_configs_path = os.path.join(script_path, "configs")
-sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
-sd_model_file = os.path.join(script_path, 'model.ckpt')
-default_sd_model_file = sd_model_file
-
-# Parse the --data-dir flag first so we can use it as a base for our other argument default values
-parser_pre = argparse.ArgumentParser(add_help=False)
-parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
-cmd_opts_pre = parser_pre.parse_known_args()[0]
-
-data_path = cmd_opts_pre.data_dir
-
-models_path = os.path.join(data_path, "models")
-extensions_dir = os.path.join(data_path, "extensions")
-extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")

modules/processing.py

@@ -13,11 +13,10 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
-import modules.paths as paths
 import modules.face_restoration
 import modules.images as images
 import modules.styles
@@ -78,28 +77,22 @@ def apply_overlay(image, paste_loc, index, overlays):
 
 
 def txt2img_image_conditioning(sd_model, x, width, height):
-    if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
-
+        # Dummy zero conditioning if we're not using inpainting model.
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-        return image_conditioning
-
-    elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
-
-        return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
-
-    else:
-        # Dummy zero conditioning if we're not using inpainting or unclip models.
         # Still takes up a bit of memory, but no encoder call.
         # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
+    # The "masked-image" in this case will just be all zeros since the entire image is masked.
+    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+    # Add the fake full 1s mask to the first dimension.
+    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+    image_conditioning = image_conditioning.to(x.dtype)
+
+    return image_conditioning
+
 
 class StableDiffusionProcessing:
     """
@@ -191,20 +184,7 @@ class StableDiffusionProcessing:
         conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
         return conditioning
 
-    def edit_image_conditioning(self, source_image):
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
-
-        return conditioning_image
-
-    def unclip_image_conditioning(self, source_image):
-        c_adm = self.sd_model.embedder(source_image)
-        if self.sd_model.noise_augmentor is not None:
-            noise_level = 0 # TODO: Allow other noise levels?
-            c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
-            c_adm = torch.cat((c_adm, noise_level_emb), 1)
-        return c_adm
-
-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -223,7 +203,7 @@ class StableDiffusionProcessing:
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -242,22 +222,14 @@ class StableDiffusionProcessing:
         return image_conditioning
 
     def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
-        source_image = devices.cond_cast_float(source_image)
-
         # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
         # identify itself with a field common to all models. The conditioning_key is also hybrid.
         if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
             return self.depth2img_image_conditioning(source_image)
 
-        if self.sd_model.cond_stage_key == "edit":
-            return self.edit_image_conditioning(source_image)
-
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
 
-        if self.sampler.conditioning_key == "crossattn-adm":
-            return self.unclip_image_conditioning(source_image)
-
         # Dummy zero conditioning if we're not using inpainting or depth model.
         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
@@ -285,7 +257,6 @@ class Processed:
         self.height = p.height
         self.sampler_name = p.sampler_name
         self.cfg_scale = p.cfg_scale
-        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
         self.steps = p.steps
         self.batch_size = p.batch_size
         self.restore_faces = p.restore_faces
@@ -463,17 +434,19 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Steps": p.steps,
         "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
-        "Image CFG scale": getattr(p, 'image_cfg_scale', None),
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Batch size": (None if p.batch_size < 2 else p.batch_size),
+        "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
     }
@@ -560,6 +533,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
+    _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
+
     if p.scripts is not None:
         p.scripts.process(p)
 
@@ -593,14 +568,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         with devices.autocast():
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
-            # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
+            if not p.disable_extra_networks:
-            if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
+                extra_networks.activate(p, extra_network_data)
-                sd_vae_approx.model()
+
+        with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+            processed = Processed(p, [], p.seed, "")
+            file.write(processed.infotext(p, 0))
 
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-        extra_network_data = None
         for n in range(p.n_iter):
             p.iteration = n
 
@@ -615,30 +592,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
-            if p.scripts is not None:
-                p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
-
             if len(prompts) == 0:
                 break
 
-            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+            prompts, _ = extra_networks.parse_prompts(prompts)
-
-            if not p.disable_extra_networks:
-                with devices.autocast():
-                    extra_networks.activate(p, extra_network_data)
 
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
-            # params.txt should be saved after scripts.process_batch, since the
-            # infotext could be modified by that callback
-            # Example: a wildcard processed by process_batch sets an extra model
-            # strength, which is saved as "Model Strength: 1.0" in the infotext
-            if n == 0:
-                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
-                    processed = Processed(p, [], p.seed, "")
-                    file.write(processed.infotext(p, 0))
-
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
 
@@ -649,7 +610,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
+            with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
@@ -684,11 +645,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 image = Image.fromarray(x_sample)
 
-                if p.scripts is not None:
-                    pp = scripts.PostprocessImageArgs(image)
-                    p.scripts.postprocess_image(p, pp)
-                    image = pp.image
-
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
@@ -706,22 +662,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
-                    image_mask = p.mask_for_overlay.convert('RGB')
-                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
-
-                    if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
-
-                    if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
-
-                    if opts.return_mask:
-                        output_images.append(image_mask)
-                    
-                    if opts.return_mask_composite:
-                        output_images.append(image_mask_composite)
-
             del x_samples_ddim
 
             devices.torch_gc()
@@ -746,7 +686,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
-    if not p.disable_extra_networks and extra_network_data:
+    if not p.disable_extra_networks:
         extra_networks.deactivate(p, extra_network_data)
 
     devices.torch_gc()
@@ -925,9 +865,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        img2img_sampler_name = self.sampler_name
+        img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM'  # PLMS does not support img2img so we just silently switch ot DDIM
-        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
-            img2img_sampler_name = 'DDIM'
         self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@@ -946,13 +884,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
         self.resize_mode: int = resize_mode
         self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
         self.init_latent = None
         self.image_mask = mask
         self.latent_mask = None

modules/realesrgan_model.py

@@ -46,7 +46,7 @@ class UpscalerRealESRGAN(Upscaler):
             scale=info.scale,
             model_path=info.local_data_path,
             model=info.model(),
-            half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
+            half=not cmd_opts.no_half,
             tile=opts.ESRGAN_tile,
             tile_pad=opts.ESRGAN_tile_overlap,
         )

modules/script_callbacks.py

@@ -29,7 +29,7 @@ class ImageSaveParams:
 
 
 class CFGDenoiserParams:
-    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
         self.x = x
         """Latent image representation in the process of being denoised"""
         
@@ -45,24 +45,6 @@ class CFGDenoiserParams:
         self.total_sampling_steps = total_sampling_steps
         """Total number of sampling steps planned"""
 
-        self.text_cond = text_cond
-        """ Encoder hidden states of text conditioning from prompt"""
-        
-        self.text_uncond = text_uncond
-        """ Encoder hidden states of text conditioning from negative prompt"""
-
-
-class CFGDenoisedParams:
-    def __init__(self, x, sampling_step, total_sampling_steps):
-        self.x = x
-        """Latent image representation in the process of being denoised"""
-
-        self.sampling_step = sampling_step
-        """Current Sampling step number"""
-
-        self.total_sampling_steps = total_sampling_steps
-        """Total number of sampling steps planned"""
-
 
 class UiTrainTabParams:
     def __init__(self, txt2img_preview_params):
@@ -86,7 +68,6 @@ callback_map = dict(
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
-    callbacks_cfg_denoised=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
     callbacks_image_grid=[],
@@ -169,14 +150,6 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
             report_exception(c, 'cfg_denoiser_callback')
 
 
-def cfg_denoised_callback(params: CFGDenoisedParams):
-    for c in callback_map['callbacks_cfg_denoised']:
-        try:
-            c.callback(params)
-        except Exception:
-            report_exception(c, 'cfg_denoised_callback')
-
-
 def before_component_callback(component, **kwargs):
     for c in callback_map['callbacks_before_component']:
         try:
@@ -310,14 +283,6 @@ def on_cfg_denoiser(callback):
     add_callback(callback_map['callbacks_cfg_denoiser'], callback)
 
 
-def on_cfg_denoised(callback):
-    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
-    The callback is called with one argument:
-        - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
-    """
-    add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
 def on_before_component(callback):
     """register a function to be called before a component is created.
     The callback is called with arguments:

modules/script_loading.py

@@ -1,14 +1,16 @@
 import os
 import sys
 import traceback
-import importlib.util
 from types import ModuleType
 
 
 def load_module(path):
-    module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
+    with open(path, "r", encoding="utf8") as file:
-    module = importlib.util.module_from_spec(module_spec)
+        text = file.read()
-    module_spec.loader.exec_module(module)
+
+    compiled = compile(text, path, 'exec')
+    module = ModuleType(os.path.basename(path))
+    exec(compiled, module.__dict__)
 
     return module
 

modules/scripts.py

@@ -6,16 +6,12 @@ from collections import namedtuple
 
 import gradio as gr
 
+from modules.processing import StableDiffusionProcessing
 from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
 
 AlwaysVisible = object()
 
 
-class PostprocessImageArgs:
-    def __init__(self, image):
-        self.image = image
-
-
 class Script:
     filename = None
     args_from = None
@@ -33,11 +29,6 @@ class Script:
     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
     """
 
-    paste_field_names = None
-    """if set in ui(), this is a list of names of infotext fields; the fields will be sent through the
-    various "Send to <X>" buttons when clicked
-    """
-
     def title(self):
         """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
 
@@ -74,7 +65,7 @@ class Script:
         args contains all values returned by components from ui()
         """
 
-        pass
+        raise NotImplementedError()
 
     def process(self, p, *args):
         """
@@ -85,20 +76,6 @@ class Script:
 
         pass
 
-    def before_process_batch(self, p, *args, **kwargs):
-        """
-        Called before extra networks are parsed from the prompt, so you can add
-        new extra network keywords to the prompt with this callback.
-
-        **kwargs will have those items:
-          - batch_number - index of current batch, from 0 to number of batches-1
-          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
-          - seeds - list of seeds for current batch
-          - subseeds - list of subseeds for current batch
-        """
-
-        pass
-
     def process_batch(self, p, *args, **kwargs):
         """
         Same as process(), but called for every batch.
@@ -123,13 +100,6 @@ class Script:
 
         pass
 
-    def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
-        """
-        Called for every image after it has been generated.
-        """
-
-        pass
-
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -239,15 +209,7 @@ def load_scripts():
             elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                 postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
 
-    def orderby(basedir):
+    for scriptfile in sorted(scripts_list):
-        # 1st webui, 2nd extensions-builtin, 3rd extensions
-        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
-        for key in priority:
-            if basedir.startswith(key):
-                return priority[key]
-        return 9999
-
-    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
         try:
             if scriptfile.basedir != paths.script_path:
                 sys.path = [scriptfile.basedir] + sys.path
@@ -283,18 +245,13 @@ class ScriptRunner:
         self.alwayson_scripts = []
         self.titles = []
         self.infotext_fields = []
-        self.paste_field_names = []
 
     def initialize_scripts(self, is_img2img):
-        from modules import scripts_auto_postprocessing
-
         self.scripts.clear()
         self.alwayson_scripts.clear()
         self.selectable_scripts.clear()
 
-        auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
+        for script_class, path, basedir, script_module in scripts_data:
-
-        for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
             script = script_class()
             script.filename = path
             script.is_txt2img = not is_img2img
@@ -332,9 +289,6 @@ class ScriptRunner:
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
 
-            if script.paste_field_names is not None:
-                self.paste_field_names += script.paste_field_names
-
             inputs += controls
             inputs_alwayson += [script.alwayson for _ in controls]
             script.args_to = len(inputs)
@@ -376,23 +330,9 @@ class ScriptRunner:
             outputs=[script.group for script in self.selectable_scripts]
         )
 
-        self.script_load_ctr = 0
-        def onload_script_visibility(params):
-            title = params.get('Script', None)
-            if title:
-                title_index = self.titles.index(title)
-                visibility = title_index == self.script_load_ctr
-                self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
-                return gr.update(visible=visibility)
-            else:
-                return gr.update(visible=False)
-
-        self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
-        self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
-
         return inputs
 
-    def run(self, p, *args):
+    def run(self, p: StableDiffusionProcessing, *args):
         script_index = args[0]
 
         if script_index == 0:
@@ -419,15 +359,6 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def before_process_batch(self, p, **kwargs):
-        for script in self.alwayson_scripts:
-            try:
-                script_args = p.script_args[script.args_from:script.args_to]
-                script.before_process_batch(p, *script_args, **kwargs)
-            except Exception:
-                print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-
     def process_batch(self, p, **kwargs):
         for script in self.alwayson_scripts:
             try:
@@ -455,15 +386,6 @@ class ScriptRunner:
                 print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def postprocess_image(self, p, pp: PostprocessImageArgs):
-        for script in self.alwayson_scripts:
-            try:
-                script_args = p.script_args[script.args_from:script.args_to]
-                script.postprocess_image(p, pp, *script_args)
-            except Exception:
-                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-
     def before_component(self, component, **kwargs):
         for script in self.scripts:
             try:
@@ -521,18 +443,6 @@ def reload_scripts():
     scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 
 
-def add_classes_to_gradio_component(comp):
-    """
-    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
-    """
-
-    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
-
-    if getattr(comp, 'multiselect', False):
-        comp.elem_classes.append('multiselect')
-
-
-
 def IOComponent_init(self, *args, **kwargs):
     if scripts_current is not None:
         scripts_current.before_component(self, **kwargs)
@@ -541,8 +451,6 @@ def IOComponent_init(self, *args, **kwargs):
 
     res = original_IOComponent_init(self, *args, **kwargs)
 
-    add_classes_to_gradio_component(self)
-
     script_callbacks.after_component_callback(self, **kwargs)
 
     if scripts_current is not None:
@@ -553,15 +461,3 @@ def IOComponent_init(self, *args, **kwargs):
 
 original_IOComponent_init = gr.components.IOComponent.__init__
 gr.components.IOComponent.__init__ = IOComponent_init
-
-
-def BlockContext_init(self, *args, **kwargs):
-    res = original_BlockContext_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init

modules/scripts_auto_postprocessing.py

@@ -1,42 +0,0 @@
-from modules import scripts, scripts_postprocessing, shared
-
-
-class ScriptPostprocessingForMainUI(scripts.Script):
-    def __init__(self, script_postproc):
-        self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
-        self.postprocessing_controls = None
-
-    def title(self):
-        return self.script.name
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        self.postprocessing_controls = self.script.ui()
-        return self.postprocessing_controls.values()
-
-    def postprocess_image(self, p, script_pp, *args):
-        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
-
-        pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
-        pp.info = {}
-        self.script.process(pp, **args_dict)
-        p.extra_generation_params.update(pp.info)
-        script_pp.image = pp.image
-
-
-def create_auto_preprocessing_script_data():
-    from modules import scripts
-
-    res = []
-
-    for name in shared.opts.postprocessing_enable_in_main_ui:
-        script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
-        if script is None:
-            continue
-
-        constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())
-        res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
-
-    return res

modules/scripts_postprocessing.py

@@ -46,8 +46,6 @@ class ScriptPostprocessing:
         pass
 
 
-
-
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
         res = func(*args, **kwargs)
@@ -70,9 +68,6 @@ class ScriptPostprocessingRunner:
             script: ScriptPostprocessing = script_class()
             script.filename = path
 
-            if script.name == "Simple Upscale":
-                continue
-
             self.scripts.append(script)
 
     def create_script_ui(self, script, inputs):
@@ -92,11 +87,12 @@ class ScriptPostprocessingRunner:
             import modules.scripts
             self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
 
-        scripts_order = shared.opts.postprocessing_operation_order
+        scripts_order = [x.lower().strip() for x in shared.opts.postprocessing_scipts_order.split(",")]
 
         def script_score(name):
+            name = name.lower()
             for i, possible_match in enumerate(scripts_order):
-                if possible_match == name:
+                if possible_match in name:
                     return i
 
             return len(self.scripts)
@@ -109,7 +105,7 @@ class ScriptPostprocessingRunner:
         inputs = []
 
         for script in self.scripts_in_preferred_order():
-            with gr.Row() as group:
+            with gr.Box() as group:
                 self.create_script_ui(script, inputs)
 
             script.group = group
@@ -149,4 +145,3 @@ class ScriptPostprocessingRunner:
     def image_changed(self):
         for script in self.scripts_in_preferred_order():
             script.image_changed()
-

modules/sd_disable_initialization.py

@@ -20,9 +20,8 @@ class DisableInitialization:
     ```
     """
 
-    def __init__(self, disable_clip=True):
+    def __init__(self):
         self.replaced = []
-        self.disable_clip = disable_clip
 
     def replace(self, obj, field, func):
         original = getattr(obj, field, None)
@@ -76,14 +75,12 @@ class DisableInitialization:
         self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
         self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
         self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
-
+        self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
-        if self.disable_clip:
+        self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
-            self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+        self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
-            self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+        self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
-            self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+        self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
-            self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+        self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
-            self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
-            self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         for obj, field, original in self.replaced:

modules/sd_hijack.py

@@ -1,6 +1,5 @@
 import torch
 from torch.nn.functional import silu
-from types import MethodType
 
 import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
@@ -37,23 +36,11 @@ def apply_optimizations():
     
     optimization_method = None
 
-    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
-
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
         optimization_method = 'xformers'
-    elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
-        print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
-        optimization_method = 'sdp-no-mem'
-    elif cmd_opts.opt_sdp_attention and can_use_sdp:
-        print("Applying scaled dot product cross attention optimization.")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
-        optimization_method = 'sdp'
     elif cmd_opts.opt_sub_quad_attention:
         print("Applying sub-quadratic cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
@@ -89,54 +76,6 @@ def fix_checkpoint():
     pass
 
 
-def weighted_loss(sd_model, pred, target, mean=True):
-    #Calculate the weight normally, but ignore the mean
-    loss = sd_model._old_get_loss(pred, target, mean=False)
-    
-    #Check if we have weights available
-    weight = getattr(sd_model, '_custom_loss_weight', None)
-    if weight is not None:
-        loss *= weight
-    
-    #Return the loss, as mean if specified
-    return loss.mean() if mean else loss
-
-def weighted_forward(sd_model, x, c, w, *args, **kwargs):
-    try:
-        #Temporarily append weights to a place accessible during loss calc
-        sd_model._custom_loss_weight = w
-        
-        #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
-        #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
-        if not hasattr(sd_model, '_old_get_loss'):
-            sd_model._old_get_loss = sd_model.get_loss
-        sd_model.get_loss = MethodType(weighted_loss, sd_model)
-
-        #Run the standard forward function, but with the patched 'get_loss'
-        return sd_model.forward(x, c, *args, **kwargs)
-    finally:
-        try:
-            #Delete temporary weights if appended
-            del sd_model._custom_loss_weight
-        except AttributeError as e:
-            pass
-            
-        #If we have an old loss function, reset the loss function to the original one
-        if hasattr(sd_model, '_old_get_loss'):
-            sd_model.get_loss = sd_model._old_get_loss
-            del sd_model._old_get_loss
-
-def apply_weighted_forward(sd_model):
-    #Add new function 'weighted_forward' that can be called to calc weighted loss
-    sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
-
-def undo_weighted_forward(sd_model):
-    try:
-        del sd_model.weighted_forward
-    except AttributeError as e:
-        pass
-
-
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -165,10 +104,6 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
-        apply_weighted_forward(m)
-        if m.cond_stage_key == "edit":
-            sd_hijack_unet.hijack_ddpm_edit()
-
         self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model
@@ -196,9 +131,6 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
             m.cond_stage_model = m.cond_stage_model.wrapped
 
-        undo_optimizations()
-        undo_weighted_forward(m)
-
         self.apply_circular(False)
         self.layers = None
         self.clip = None
@@ -239,7 +171,7 @@ class EmbeddingsWithFixes(torch.nn.Module):
         vecs = []
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
-                emb = devices.cond_cast_unet(embedding.vec)
+                emb = embedding.vec
                 emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                 tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 

modules/sd_hijack_inpainting.py

@@ -11,7 +11,6 @@ import ldm.models.diffusion.plms
 from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
 
 
 @torch.no_grad()
@@ -97,6 +96,15 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     return x_prev, pred_x0, e_t
 
 
+def should_hijack_inpainting(checkpoint_info):
+    from modules import sd_models
+
+    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
+    return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
+
+
 def do_inpainting_hijack():
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
 

modules/sd_hijack_ip2p.py

@@ -1,13 +0,0 @@
-import collections
-import os.path
-import sys
-import gc
-import time
-
-def should_hijack_ip2p(checkpoint_info):
-    from modules import sd_models_config
-
-    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
-
-    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename

modules/sd_hijack_optimizations.py

@@ -9,7 +9,7 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared, errors, devices
+from modules import shared, errors
 from modules.hypernetworks import hypernetwork
 
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -52,25 +52,18 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
-    dtype = q.dtype
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    if shared.opts.upcast_attn:
+    for i in range(0, q.shape[0], 2):
-        q, k, v = q.float(), k.float(), v.float()
+        end = i + 2
+        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+        s1 *= self.scale
 
-    with devices.without_autocast(disable=not shared.opts.upcast_attn):
+        s2 = s1.softmax(dim=-1)
-        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        del s1
-        for i in range(0, q.shape[0], 2):
-            end = i + 2
-            s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-            s1 *= self.scale
 
-            s2 = s1.softmax(dim=-1)
+        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-            del s1
+        del s2
-    
+    del q, k, v
-            r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-            del s2
-        del q, k, v
-
-    r1 = r1.to(dtype)
 
     r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
     del r1
@@ -89,52 +82,45 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    dtype = q_in.dtype
+    k_in *= self.scale
-    if shared.opts.upcast_attn:
-        q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
 
-    with devices.without_autocast(disable=not shared.opts.upcast_attn):
+    del context, x
-        k_in = k_in * self.scale
 
-        del context, x
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
 
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        del q_in, k_in, v_in
 
-        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    mem_free_total = get_available_vram()
 
-        mem_free_total = get_available_vram()
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
 
-        gb = 1024 ** 3
+    if mem_required > mem_free_total:
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
-        modifier = 3 if q.element_size() == 2 else 2.5
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        mem_required = tensor_size * modifier
+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-        steps = 1
 
-        if mem_required > mem_free_total:
+    if steps > 64:
-            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-            # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-            #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
 
-        if steps > 64:
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+    for i in range(0, q.shape[1], slice_size):
-            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+        end = i + slice_size
-                               f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
 
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        s2 = s1.softmax(dim=-1, dtype=q.dtype)
-        for i in range(0, q.shape[1], slice_size):
+        del s1
-            end = i + slice_size
-            s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
 
-            s2 = s1.softmax(dim=-1, dtype=q.dtype)
+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-            del s1
+        del s2
 
-            r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+    del q, k, v
-            del s2
-    
-        del q, k, v
-
-    r1 = r1.to(dtype)
 
     r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
     del r1
@@ -218,20 +204,12 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
     context = default(context, x)
 
     context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
-    k = self.to_k(context_k)
+    k = self.to_k(context_k) * self.scale
     v = self.to_v(context_v)
     del context, context_k, context_v, x
 
-    dtype = q.dtype
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-    if shared.opts.upcast_attn:
+    r = einsum_op(q, k, v)
-        q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
-
-    with devices.without_autocast(disable=not shared.opts.upcast_attn):
-        k = k * self.scale
-    
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-        r = einsum_op(q, k, v)
-    r = r.to(dtype)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
 
 # -- End of code from https://github.com/invoke-ai/InvokeAI --
@@ -256,14 +234,8 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
     k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
     v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
 
-    dtype = q.dtype
-    if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
-
     x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
 
-    x = x.to(dtype)
-
     x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
 
     out_proj, dropout = self.to_out
@@ -296,16 +268,15 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
         query_chunk_size = q_tokens
         kv_chunk_size = k_tokens
 
-    with devices.without_autocast(disable=q.dtype == v.dtype):
+    return efficient_dot_product_attention(
-        return efficient_dot_product_attention(
+        q,
-            q,
+        k,
-            k,
+        v,
-            v,
+        query_chunk_size=q_chunk_size,
-            query_chunk_size=q_chunk_size,
+        kv_chunk_size=kv_chunk_size,
-            kv_chunk_size=kv_chunk_size,
+        kv_chunk_size_min = kv_chunk_size_min,
-            kv_chunk_size_min = kv_chunk_size_min,
+        use_checkpoint=use_checkpoint,
-            use_checkpoint=use_checkpoint,
+    )
-        )
 
 
 def get_xformers_flash_attention_op(q, k, v):
@@ -335,63 +306,11 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
-    dtype = q.dtype
-    if shared.opts.upcast_attn:
-        q, k, v = q.float(), k.float(), v.float()
-
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
 
-    out = out.to(dtype)
-
     out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
 
-# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
-# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
-def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
-    batch_size, sequence_length, inner_dim = x.shape
-
-    if mask is not None:
-        mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
-        mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
-
-    h = self.heads
-    q_in = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
-    k_in = self.to_k(context_k)
-    v_in = self.to_v(context_v)
-
-    head_dim = inner_dim // h
-    q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-    k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-    v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-    
-    del q_in, k_in, v_in
-
-    dtype = q.dtype
-    if shared.opts.upcast_attn:
-        q, k, v = q.float(), k.float(), v.float()
-
-    # the output of sdp = (batch, num_heads, seq_len, head_dim)
-    hidden_states = torch.nn.functional.scaled_dot_product_attention(
-        q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
-    )
-
-    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
-    hidden_states = hidden_states.to(dtype)
-
-    # linear proj
-    hidden_states = self.to_out[0](hidden_states)
-    # dropout
-    hidden_states = self.to_out[1](hidden_states)
-    return hidden_states
-
-def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
-    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
-        return scaled_dot_product_attention_forward(self, x, context, mask)
-
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
@@ -459,44 +378,16 @@ def xformers_attnblock_forward(self, x):
         v = self.v(h_)
         b, c, h, w = q.shape
         q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
-        dtype = q.dtype
-        if shared.opts.upcast_attn:
-            q, k = q.float(), k.float()
         q = q.contiguous()
         k = k.contiguous()
         v = v.contiguous()
         out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
-        out = out.to(dtype)
         out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
 
-def sdp_attnblock_forward(self, x):
-    h_ = x
-    h_ = self.norm(h_)
-    q = self.q(h_)
-    k = self.k(h_)
-    v = self.v(h_)
-    b, c, h, w = q.shape
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
-    dtype = q.dtype
-    if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
-    q = q.contiguous()
-    k = k.contiguous()
-    v = v.contiguous()
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
-    out = out.to(dtype)
-    out = rearrange(out, 'b (h w) c -> b c h w', h=h)
-    out = self.proj_out(out)
-    return x + out
-
-def sdp_no_mem_attnblock_forward(self, x):
-    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
-        return sdp_attnblock_forward(self, x)
-
 def sub_quad_attnblock_forward(self, x):
     h_ = x
     h_ = self.norm(h_)

modules/sd_hijack_unet.py

@@ -1,8 +1,4 @@
 import torch
-from packaging import version
-
-from modules import devices
-from modules.sd_hijack_utils import CondFunc
 
 
 class TorchHijackForUnet:
@@ -32,48 +28,3 @@ class TorchHijackForUnet:
 
 
 th = TorchHijackForUnet()
-
-
-# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
-def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
-
-    if isinstance(cond, dict):
-        for y in cond.keys():
-            cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
-
-    with devices.autocast():
-        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
-
-
-class GELUHijack(torch.nn.GELU, torch.nn.Module):
-    def __init__(self, *args, **kwargs):
-        torch.nn.GELU.__init__(self, *args, **kwargs)
-    def forward(self, x):
-        if devices.unet_needs_upcast:
-            return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
-        else:
-            return torch.nn.GELU.forward(self, x)
-
-
-ddpm_edit_hijack = None
-def hijack_ddpm_edit():
-    global ddpm_edit_hijack
-    if not ddpm_edit_hijack:
-        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
-        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-
-
-unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
-    CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
-    CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
-    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
-
-first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
-first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)

modules/sd_hijack_utils.py

@@ -1,28 +0,0 @@
-import importlib
-
-class CondFunc:
-    def __new__(cls, orig_func, sub_func, cond_func):
-        self = super(CondFunc, cls).__new__(cls)
-        if isinstance(orig_func, str):
-            func_path = orig_func.split('.')
-            for i in range(len(func_path)-1, -1, -1):
-                try:
-                    resolved_obj = importlib.import_module('.'.join(func_path[:i]))
-                    break
-                except ImportError:
-                    pass
-            for attr_name in func_path[i:-1]:
-                resolved_obj = getattr(resolved_obj, attr_name)
-            orig_func = getattr(resolved_obj, func_path[-1])
-            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
-        self.__init__(orig_func, sub_func, cond_func)
-        return lambda *args, **kwargs: self(*args, **kwargs)
-    def __init__(self, orig_func, sub_func, cond_func):
-        self.__orig_func = orig_func
-        self.__sub_func = sub_func
-        self.__cond_func = cond_func
-    def __call__(self, *args, **kwargs):
-        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
-            return self.__sub_func(self.__orig_func, *args, **kwargs)
-        else:
-            return self.__orig_func(*args, **kwargs)

modules/sd_models.py

@@ -2,6 +2,8 @@ import collections
 import os.path
 import sys
 import gc
+import time
+from collections import namedtuple
 import torch
 import re
 import safetensors.torch
@@ -12,13 +14,12 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
 from modules.paths import models_path
-from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
-from modules.timer import Timer
 
 model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
 checkpoints_list = {}
 checkpoint_alisases = {}
@@ -41,7 +42,6 @@ class CheckpointInfo:
             name = name[1:]
 
         self.name = name
-        self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
 
@@ -59,17 +59,13 @@ class CheckpointInfo:
 
     def calculate_shorthash(self):
         self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
-        if self.sha256 is None:
-            return
-
         self.shorthash = self.sha256[0:10]
 
         if self.shorthash not in self.ids:
-            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
+            self.ids += [self.shorthash, self.sha256]
+            self.register()
 
-        checkpoints_list.pop(self.title)
         self.title = f'{self.name} [{self.shorthash}]'
-        self.register()
 
         return self.shorthash
 
@@ -102,18 +98,23 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
 
 
+def find_checkpoint_config(info):
+    if info is None:
+        return shared.cmd_opts.config
+
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return shared.cmd_opts.config
+
+
 def list_models():
     checkpoints_list.clear()
     checkpoint_alisases.clear()
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
 
     cmd_ckpt = shared.cmd_opts.ckpt
-    if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
-        model_url = None
-    else:
-        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
-
-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
-
     if os.path.exists(cmd_ckpt):
         checkpoint_info = CheckpointInfo(cmd_ckpt)
         checkpoint_info.register()
@@ -122,7 +123,7 @@ def list_models():
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
 
-    for filename in sorted(model_list, key=str.lower):
+    for filename in model_list:
         checkpoint_info = CheckpointInfo(filename)
         checkpoint_info.register()
 
@@ -168,7 +169,7 @@ def select_checkpoint():
         print(f" - directory {model_path}", file=sys.stderr)
         if shared.cmd_opts.ckpt_dir is not None:
             print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
+        print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
     checkpoint_info = next(iter(checkpoints_list.values()))
@@ -178,7 +179,7 @@ def select_checkpoint():
     return checkpoint_info
 
 
-checkpoint_dict_replacements = {
+chckpoint_dict_replacements = {
     'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
     'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
     'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -186,7 +187,7 @@ checkpoint_dict_replacements = {
 
 
 def transform_checkpoint_dict_key(k):
-    for text, replacement in checkpoint_dict_replacements.items():
+    for text, replacement in chckpoint_dict_replacements.items():
         if k.startswith(text):
             k = replacement + k[len(text):]
 
@@ -210,34 +211,12 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
-def read_metadata_from_safetensors(filename):
-    import json
-
-    with open(filename, mode="rb") as file:
-        metadata_len = file.read(8)
-        metadata_len = int.from_bytes(metadata_len, "little")
-        json_start = file.read(2)
-
-        assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
-        json_data = json_start + file.read(metadata_len-2)
-        json_obj = json.loads(json_data)
-
-        res = {}
-        for k, v in json_obj.get("__metadata__", {}).items():
-            res[k] = v
-            if isinstance(v, str) and v[0:1] == '{':
-                try:
-                    res[k] = json.loads(v)
-                except Exception as e:
-                    pass
-
-        return res
-
-
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
+        device = map_location or shared.weight_load_location
+        if device is None:
+            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
         pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@@ -249,72 +228,52 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     return sd
 
 
-def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
+def load_model_weights(model, checkpoint_info: CheckpointInfo):
+    title = checkpoint_info.title
     sd_model_hash = checkpoint_info.calculate_shorthash()
-    timer.record("calculate hash")
+    if checkpoint_info.title != title:
+        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
 
-    if checkpoint_info in checkpoints_loaded:
+    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+
+    if cache_enabled and checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
         print(f"Loading weights [{sd_model_hash}] from cache")
-        return checkpoints_loaded[checkpoint_info]
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+    else:
+        # load from file
+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
 
-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+        sd = read_state_dict(checkpoint_info.filename)
-    res = read_state_dict(checkpoint_info.filename)
+        model.load_state_dict(sd, strict=False)
-    timer.record("load weights from disk")
+        del sd
         
-    return res
+        if cache_enabled:
+            # cache newly loaded model
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
 
+        if shared.cmd_opts.opt_channelslast:
+            model.to(memory_format=torch.channels_last)
 
-def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
+        if not shared.cmd_opts.no_half:
-    sd_model_hash = checkpoint_info.calculate_shorthash()
+            vae = model.first_stage_model
-    timer.record("calculate hash")
 
-    shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
+            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+            if shared.cmd_opts.no_half_vae:
+                model.first_stage_model = None
 
-    if state_dict is None:
+            model.half()
-        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+            model.first_stage_model = vae
 
-    model.load_state_dict(state_dict, strict=False)
+        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    del state_dict
+        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
-    timer.record("apply weights to model")
 
-    if shared.opts.sd_checkpoint_cache > 0:
+        model.first_stage_model.to(devices.dtype_vae)
-        # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-
-    if shared.cmd_opts.opt_channelslast:
-        model.to(memory_format=torch.channels_last)
-        timer.record("apply channels_last")
-
-    if not shared.cmd_opts.no_half:
-        vae = model.first_stage_model
-        depth_model = getattr(model, 'depth_model', None)
-
-        # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
-        if shared.cmd_opts.no_half_vae:
-            model.first_stage_model = None
-        # with --upcast-sampling, don't convert the depth model weights to float16
-        if shared.cmd_opts.upcast_sampling and depth_model:
-            model.depth_model = None
-
-        model.half()
-        model.first_stage_model = vae
-        if depth_model:
-            model.depth_model = depth_model
-
-        timer.record("apply half()")
-
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
-    devices.dtype_unet = model.model.diffusion_model.dtype
-    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
-
-    model.first_stage_model.to(devices.dtype_vae)
-    timer.record("apply dtype to VAE")
 
     # clean up cache if limit is reached
-    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+    if cache_enabled:
-        checkpoints_loaded.popitem(last=False)
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
+            checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_info.filename
@@ -327,7 +286,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     sd_vae.clear_loaded_vae()
     vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
     sd_vae.load_vae(model, vae_file, vae_source)
-    timer.record("load VAE")
 
 
 def enable_midas_autodownload():
@@ -340,7 +298,7 @@ def enable_midas_autodownload():
     location automatically.
     """
 
-    midas_path = os.path.join(paths.models_path, 'midas')
+    midas_path = os.path.join(models_path, 'midas')
 
     # stable-diffusion-stability-ai hard-codes the midas model path to
     # a location that differs from where other scripts using this model look.
@@ -373,31 +331,24 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
-def repair_config(sd_config):
+class Timer:
+    def __init__(self):
+        self.start = time.time()
 
-    if not hasattr(sd_config.model.params, "use_ema"):
+    def elapsed(self):
-        sd_config.model.params.use_ema = False
+        end = time.time()
-
+        res = end - self.start
-    if shared.cmd_opts.no_half:
+        self.start = end
-        sd_config.model.params.unet_config.params.use_fp16 = False
+        return res
-    elif shared.cmd_opts.upcast_sampling:
-        sd_config.model.params.unet_config.params.use_fp16 = True
-
-    if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
-        sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
-
-    # For UnCLIP-L, override the hardcoded karlo directory
-    if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
-        karlo_path = os.path.join(paths.models_path, 'karlo')
-        sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
 
 
-sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
+def load_model(checkpoint_info=None):
-sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
-
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
+    checkpoint_config = find_checkpoint_config(checkpoint_info)
+
+    if checkpoint_config != shared.cmd_opts.config:
+        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -405,30 +356,29 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
         gc.collect()
         devices.torch_gc()
 
+    sd_config = OmegaConf.load(checkpoint_config)
+    
+    if should_hijack_inpainting(checkpoint_info):
+        # Hardcoded config for now...
+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.unet_config.params.in_channels = 9
+        sd_config.model.params.finetune_keys = None
+
+    if not hasattr(sd_config.model.params, "use_ema"):
+        sd_config.model.params.use_ema = False
+
     do_inpainting_hijack()
 
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+
     timer = Timer()
 
-    if already_loaded_state_dict is not None:
-        state_dict = already_loaded_state_dict
-    else:
-        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-    clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
-
-    timer.record("find config")
-
-    sd_config = OmegaConf.load(checkpoint_config)
-    repair_config(sd_config)
-
-    timer.record("load config")
-
-    print(f"Creating model from config: {checkpoint_config}")
-
     sd_model = None
+
     try:
-        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
+        with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)
     except Exception as e:
         pass
@@ -437,35 +387,29 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
         print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
         sd_model = instantiate_from_config(sd_config.model)
 
-    sd_model.used_config = checkpoint_config
+    elapsed_create = timer.elapsed()
 
-    timer.record("create model")
+    load_model_weights(sd_model, checkpoint_info)
 
-    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+    elapsed_load_weights = timer.elapsed()
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
     else:
         sd_model.to(shared.device)
 
-    timer.record("move model to device")
-
     sd_hijack.model_hijack.hijack(sd_model)
 
-    timer.record("hijack")
-
     sd_model.eval()
     shared.sd_model = sd_model
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
 
-    timer.record("load textual inversion embeddings")
-
     script_callbacks.model_loaded_callback(sd_model)
 
-    timer.record("scripts callbacks")
+    elapsed_the_rest = timer.elapsed()
 
-    print(f"Model loaded in {timer.summary()}.")
+    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
 
     return sd_model
 
@@ -476,7 +420,6 @@ def reload_model_weights(sd_model=None, info=None):
 
     if not sd_model:
         sd_model = shared.sd_model
-
     if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
     else:
@@ -484,64 +427,38 @@ def reload_model_weights(sd_model=None, info=None):
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
             return
 
-        if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
-            lowvram.send_everything_to_cpu()
-        else:
-            sd_model.to(devices.cpu)
 
-        sd_hijack.model_hijack.undo_hijack(sd_model)
+    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+        del sd_model
+        checkpoints_loaded.clear()
+        load_model(checkpoint_info)
+        return shared.sd_model
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
 
     timer = Timer()
 
-    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
-
-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-
-    timer.record("find config")
-
-    if sd_model is None or checkpoint_config != sd_model.used_config:
-        del sd_model
-        checkpoints_loaded.clear()
-        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
-        return shared.sd_model
-
     try:
-        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+        load_model_weights(sd_model, checkpoint_info)
     except Exception as e:
         print("Failed to load checkpoint, restoring previous")
-        load_model_weights(sd_model, current_checkpoint_info, None, timer)
+        load_model_weights(sd_model, current_checkpoint_info)
         raise
     finally:
         sd_hijack.model_hijack.hijack(sd_model)
-        timer.record("hijack")
-
         script_callbacks.model_loaded_callback(sd_model)
-        timer.record("script callbacks")
 
         if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
             sd_model.to(devices.device)
-            timer.record("move model to device")
 
-    print(f"Weights loaded in {timer.summary()}.")
+    elapsed = timer.elapsed()
-
+
-    return sd_model
+    print(f"Weights loaded in {elapsed:.1f}s.")
-
-def unload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
-    timer = Timer()
-
-    if shared.sd_model:
-
-        # shared.sd_model.cond_stage_model.to(devices.cpu)
-        # shared.sd_model.first_stage_model.to(devices.cpu)
-        shared.sd_model.to(devices.cpu)
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
-        shared.sd_model = None
-        sd_model = None
-        gc.collect()
-        devices.torch_gc()
-        torch.cuda.empty_cache()
-
-    print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model

modules/sd_models_config.py

@@ -1,119 +0,0 @@
-import re
-import os
-
-import torch
-
-from modules import shared, paths, sd_disable_initialization
-
-sd_configs_path = shared.sd_configs_path
-sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
-
-
-config_default = shared.sd_default_config
-config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
-config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
-config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
-config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
-config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
-config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
-config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
-config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
-config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
-
-
-def is_using_v_parameterization_for_sd2(state_dict):
-    """
-    Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
-    """
-
-    import ldm.modules.diffusionmodules.openaimodel
-    from modules import devices
-
-    device = devices.cpu
-
-    with sd_disable_initialization.DisableInitialization():
-        unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
-            use_checkpoint=True,
-            use_fp16=False,
-            image_size=32,
-            in_channels=4,
-            out_channels=4,
-            model_channels=320,
-            attention_resolutions=[4, 2, 1],
-            num_res_blocks=2,
-            channel_mult=[1, 2, 4, 4],
-            num_head_channels=64,
-            use_spatial_transformer=True,
-            use_linear_in_transformer=True,
-            transformer_depth=1,
-            context_dim=1024,
-            legacy=False
-        )
-        unet.eval()
-
-    with torch.no_grad():
-        unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
-        unet.load_state_dict(unet_sd, strict=True)
-        unet.to(device=device, dtype=torch.float)
-
-        test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
-        x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
-
-        out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
-
-    return out < -1
-
-
-def guess_model_config_from_state_dict(sd, filename):
-    sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
-    diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
-    sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
-
-    if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
-        return config_depth_model
-    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
-        return config_unclip
-    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
-        return config_unopenclip
-
-    if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
-        if diffusion_model_input.shape[1] == 9:
-            return config_sd2_inpainting
-        elif is_using_v_parameterization_for_sd2(sd):
-            return config_sd2v
-        else:
-            return config_sd2
-
-    if diffusion_model_input is not None:
-        if diffusion_model_input.shape[1] == 9:
-            return config_inpainting
-        if diffusion_model_input.shape[1] == 8:
-            return config_instruct_pix2pix
-
-    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
-        return config_alt_diffusion
-
-    return config_default
-
-
-def find_checkpoint_config(state_dict, info):
-    if info is None:
-        return guess_model_config_from_state_dict(state_dict, "")
-
-    config = find_checkpoint_config_near_filename(info)
-    if config is not None:
-        return config
-
-    return guess_model_config_from_state_dict(state_dict, info.filename)
-
-
-def find_checkpoint_config_near_filename(info):
-    if info is None:
-        return None
-
-    config = os.path.splitext(info.filename)[0] + ".yaml"
-    if os.path.exists(config):
-        return config
-
-    return None
-

modules/sd_samplers.py

@@ -1,11 +1,53 @@
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
+from collections import namedtuple, deque
+import numpy as np
+from math import floor
+import torch
+import tqdm
+from PIL import Image
+import inspect
+import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+from modules import prompt_parser, devices, processing, images, sd_vae_approx
 
-# imports for functions that previously were here and are used by other modules
+from modules.shared import opts, cmd_opts, state
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+]
+
+samplers_data_k_diffusion = [
+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
+    if hasattr(k_diffusion.sampling, funcname)
+]
 
 all_samplers = [
-    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
+    *samplers_data_k_diffusion,
-    *sd_samplers_compvis.samplers_data_compvis,
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
 all_samplers_map = {x.name: x for x in all_samplers}
 
@@ -31,8 +73,8 @@ def create_sampler(name, model):
 def set_samplers():
     global samplers, samplers_for_img2img
 
-    hidden = set(shared.opts.hide_samplers)
+    hidden = set(opts.hide_samplers)
-    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])
+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
 
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -45,3 +87,466 @@ def set_samplers():
 
 
 set_samplers()
+
+sampler_extra_params = {
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+
+def setup_img2img_steps(p, steps=None):
+    if opts.img2img_fix_steps or steps is not None:
+        requested_steps = (steps or p.steps)
+        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+        t_enc = requested_steps - 1
+    else:
+        steps = p.steps
+        t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+    return steps, t_enc
+
+
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+    if approximation == 2:
+        x_sample = sd_vae_approx.cheap_approximation(sample)
+    elif approximation == 1:
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
+    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+    x_sample = x_sample.astype(np.uint8)
+    return Image.fromarray(x_sample)
+
+
+def sample_to_image(samples, index=0, approximation=None):
+    return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def store_latent(decoded):
+    state.current_latent = decoded
+
+    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+        if not shared.parallel_processing_allowed:
+            shared.state.assign_current_image(sample_to_image(decoded))
+
+
+class InterruptedException(BaseException):
+    pass
+
+
+class VanillaStableDiffusionSampler:
+    def __init__(self, constructor, sd_model):
+        self.sampler = constructor(sd_model)
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.sampler_noises = None
+        self.step = 0
+        self.stop_at = None
+        self.eta = None
+        self.default_eta = 0.0
+        self.config = None
+        self.last_latent = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def number_of_needed_noises(self, p):
+        return 0
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
+    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise InterruptedException
+
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+
+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        cond = tensor
+
+        # for DDIM, shapes must match, we can't just process cond and uncond independently;
+        # filling unconditional_conditioning with repeats of the last vector to match length is
+        # not 100% correct but should work well enough
+        if unconditional_conditioning.shape[1] < cond.shape[1]:
+            last_vector = unconditional_conditioning[:, -1:]
+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+        elif unconditional_conditioning.shape[1] > cond.shape[1]:
+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
+        if self.mask is not None:
+            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
+            x_dec = img_orig * self.mask + self.nmask * x_dec
+
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+
+        if self.mask is not None:
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
+        else:
+            self.last_latent = res[1]
+
+        store_latent(self.last_latent)
+
+        self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
+        return res
+
+    def initialize(self, p):
+        self.eta = p.eta if p.eta is not None else opts.eta_ddim
+
+        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+            if hasattr(self.sampler, fieldname):
+                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+
+        self.mask = p.mask if hasattr(p, 'mask') else None
+        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == floor(valid_step):
+                return int(valid_step) + 1
+        
+        return num_steps
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+        steps = self.adjust_steps_if_invalid(p, steps)
+        self.initialize(p)
+
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
+        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+        self.init_latent = x
+        self.last_latent = x
+        self.step = 0
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        self.initialize(p)
+
+        self.init_latent = None
+        self.last_latent = x
+        self.step = 0
+
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
+        if image_conditioning is not None:
+            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+
+        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+
+        return samples_ddim
+
+
+class CFGDenoiser(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+
+        if tensor.shape[1] == uncond.shape[1]:
+            cond_in = torch.cat([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
+
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            store_latent(x_out[0:uncond.shape[0]])
+        elif opts.live_preview_content == "Negative prompt":
+            store_latent(x_out[-uncond.shape[0]:])
+
+        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        self.step += 1
+
+        return denoised
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
+
+
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+    if device.type == 'mps':
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+    else:
+        generator = torch.Generator(device).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+class KDiffusionSampler:
+    def __init__(self, funcname, sd_model):
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+        self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.extra_params = sampler_extra_params.get(funcname, [])
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.default_eta = 1.0
+        self.config = None
+        self.last_latent = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def callback_state(self, d):
+        step = d['i']
+        latent = d["denoised"]
+        if opts.live_preview_content == "Combined":
+            store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p):
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap.step = 0
+        self.eta = p.eta or opts.eta_ancestral
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    def get_sigmas(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        if p.sampler_noise_scheduler_override:
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        else:
+            sigmas = self.model_wrap.get_sigmas(steps)
+
+        if discard_next_to_last_sigma:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+        return sigmas
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+        xi = x + noise * sigma_sched[0]
+        
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
+        steps = steps or p.steps
+
+        sigmas = self.get_sigmas(p, steps)
+
+        x = x * sigmas[0]
+
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+            if 'n' in inspect.signature(self.func).parameters:
+                extra_params_kwargs['n'] = steps
+        else:
+            extra_params_kwargs['sigmas'] = sigmas
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+

modules/sd_samplers_common.py

@@ -1,62 +0,0 @@
-from collections import namedtuple
-import numpy as np
-import torch
-from PIL import Image
-from modules import devices, processing, images, sd_vae_approx
-
-from modules.shared import opts, state
-import modules.shared as shared
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-
-def setup_img2img_steps(p, steps=None):
-    if opts.img2img_fix_steps or steps is not None:
-        requested_steps = (steps or p.steps)
-        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
-        t_enc = requested_steps - 1
-    else:
-        steps = p.steps
-        t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
-    return steps, t_enc
-
-
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
-
-
-def single_sample_to_image(sample, approximation=None):
-    if approximation is None:
-        approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
-    if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample)
-    elif approximation == 1:
-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
-    else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-    x_sample = x_sample.astype(np.uint8)
-    return Image.fromarray(x_sample)
-
-
-def sample_to_image(samples, index=0, approximation=None):
-    return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
-    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def store_latent(decoded):
-    state.current_latent = decoded
-
-    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
-        if not shared.parallel_processing_allowed:
-            shared.state.assign_current_image(sample_to_image(decoded))
-
-
-class InterruptedException(BaseException):
-    pass

modules/sd_samplers_compvis.py

@@ -1,220 +0,0 @@
-import math
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-
-import numpy as np
-import torch
-
-from modules.shared import state
-from modules import sd_samplers_common, prompt_parser, shared
-import modules.models.diffusion.uni_pc
-
-
-samplers_data_compvis = [
-    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
-    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
-    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
-]
-
-
-class VanillaStableDiffusionSampler:
-    def __init__(self, constructor, sd_model):
-        self.sampler = constructor(sd_model)
-        self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
-        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
-        self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
-        self.orig_p_sample_ddim = None
-        if self.is_plms:
-            self.orig_p_sample_ddim = self.sampler.p_sample_plms
-        elif self.is_ddim:
-            self.orig_p_sample_ddim = self.sampler.p_sample_ddim
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.sampler_noises = None
-        self.step = 0
-        self.stop_at = None
-        self.eta = None
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def number_of_needed_noises(self, p):
-        return 0
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
-
-        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
-
-        x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
-
-        return res
-
-    def before_sample(self, x, ts, cond, unconditional_conditioning):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        if self.stop_at is not None and self.step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        # Have to unwrap the inpainting conditioning here to perform pre-processing
-        image_conditioning = None
-        uc_image_conditioning = None
-        if isinstance(cond, dict):
-            if self.conditioning_key == "crossattn-adm":
-                image_conditioning = cond["c_adm"]
-                uc_image_conditioning = unconditional_conditioning["c_adm"]
-            else:
-                image_conditioning = cond["c_concat"][0]
-            cond = cond["c_crossattn"][0]
-            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
-        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
-        cond = tensor
-
-        # for DDIM, shapes must match, we can't just process cond and uncond independently;
-        # filling unconditional_conditioning with repeats of the last vector to match length is
-        # not 100% correct but should work well enough
-        if unconditional_conditioning.shape[1] < cond.shape[1]:
-            last_vector = unconditional_conditioning[:, -1:]
-            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
-            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
-        elif unconditional_conditioning.shape[1] > cond.shape[1]:
-            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
-        if self.mask is not None:
-            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
-            x = img_orig * self.mask + self.nmask * x
-
-        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
-        # Note that they need to be lists because it just concatenates them later.
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
-            else:
-                cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        return x, ts, cond, unconditional_conditioning
-
-    def update_step(self, last_latent):
-        if self.mask is not None:
-            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
-        else:
-            self.last_latent = last_latent
-
-        sd_samplers_common.store_latent(self.last_latent)
-
-        self.step += 1
-        state.sampling_step = self.step
-        shared.total_tqdm.update()
-
-    def after_sample(self, x, ts, cond, uncond, res):
-        if not self.is_unipc:
-            self.update_step(res[1])
-
-        return x, ts, cond, uncond, res
-
-    def unipc_after_update(self, x, model_x):
-        self.update_step(x)
-
-    def initialize(self, p):
-        self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
-        if self.eta != 0.0:
-            p.extra_generation_params["Eta DDIM"] = self.eta
-
-        if self.is_unipc:
-            keys = [
-                ('UniPC variant', 'uni_pc_variant'),
-                ('UniPC skip type', 'uni_pc_skip_type'),
-                ('UniPC order', 'uni_pc_order'),
-                ('UniPC lower order final', 'uni_pc_lower_order_final'),
-            ]
-
-            for name, key in keys:
-                v = getattr(shared.opts, key)
-                if v != shared.opts.get_default(key):
-                    p.extra_generation_params[name] = v
-
-        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
-            if hasattr(self.sampler, fieldname):
-                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-        if self.is_unipc:
-            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
-
-        self.mask = p.mask if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
-
-    def adjust_steps_if_invalid(self, p, num_steps):
-        if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
-            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
-                num_steps = shared.opts.uni_pc_order
-            valid_step = 999 / (1000 // num_steps)
-            if valid_step == math.floor(valid_step):
-                return int(valid_step) + 1
-
-        return num_steps
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-        steps = self.adjust_steps_if_invalid(p, steps)
-        self.initialize(p)
-
-        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
-        self.init_latent = x
-        self.last_latent = x
-        self.step = 0
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
-            else:
-                conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        self.initialize(p)
-
-        self.init_latent = None
-        self.last_latent = x
-        self.step = 0
-
-        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
-        if image_conditioning is not None:
-            if self.conditioning_key == "crossattn-adm":
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
-            else:
-                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
-                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
-        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
-        return samples_ddim

modules/sd_samplers_kdiffusion.py

@@ -1,366 +0,0 @@
-from collections import deque
-import torch
-import inspect
-import einops
-import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
-
-from modules.shared import opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-
-samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
-    ('Euler', 'sample_euler', ['k_euler'], {}),
-    ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
-]
-
-samplers_data_k_diffusion = [
-    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
-]
-
-sampler_extra_params = {
-    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-
-class CFGDenoiser(torch.nn.Module):
-    """
-    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
-    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
-    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
-    negative prompt.
-    """
-
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-        self.image_cfg_scale = None
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def combine_denoised_for_edit_model(self, x_out, cond_scale):
-        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
-        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
-        # so is_edit_model is set to False to support AND composition.
-        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        if shared.sd_model.model.conditioning_key == "crossattn-adm":
-            image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
-        else:
-            image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
-
-        if not is_edit_model:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
-        else:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-        tensor = denoiser_params.text_cond
-        uncond = denoiser_params.text_uncond
-
-        if tensor.shape[1] == uncond.shape[1]:
-            if not is_edit_model:
-                cond_in = torch.cat([tensor, uncond])
-            else:
-                cond_in = torch.cat([tensor, uncond, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-
-                if not is_edit_model:
-                    c_crossattn = [tensor[a:b]]
-                else:
-                    c_crossattn = torch.cat([tensor[a:b]], uncond)
-
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
-
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
-        if not is_edit_model:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-        else:
-            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        self.step += 1
-
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
-        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
-
-    def get_sigmas(self, p, steps):
-        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
-        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
-            discard_next_to_last_sigma = True
-            p.extra_generation_params["Discard penultimate sigma"] = True
-
-        steps += 1 if discard_next_to_last_sigma else 0
-
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
-
-        if discard_next_to_last_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        return sigmas
-
-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-
-        sigmas = self.get_sigmas(p, steps)
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-        
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
-
-        if self.funcname == 'sample_dpmpp_sde':
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-        extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale,
-        }
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps = steps or p.steps
-
-        sigmas = self.get_sigmas(p, steps)
-
-        x = x * sigmas[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
-                extra_params_kwargs['n'] = steps
-        else:
-            extra_params_kwargs['sigmas'] = sigmas
-
-        if self.funcname == 'sample_dpmpp_sde':
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-

modules/sd_vae.py

@@ -3,12 +3,13 @@ import safetensors.torch
 import os
 import collections
 from collections import namedtuple
-from modules import paths, shared, devices, script_callbacks, sd_models
+from modules import shared, devices, script_callbacks, sd_models
+from modules.paths import models_path
 import glob
 from copy import deepcopy
 
 
-vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
+vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 vae_dict = {}
 

modules/sd_vae_approx.py

@@ -35,11 +35,8 @@ def model():
     global sd_vae_approx_model
 
     if sd_vae_approx_model is None:
-        model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
         sd_vae_approx_model = VAEApprox()
-        if not os.path.exists(model_path):
+        sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt"), map_location='cpu' if devices.device.type != 'cuda' else None))
-            model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
-        sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
         sd_vae_approx_model.eval()
         sd_vae_approx_model.to(devices.device, devices.dtype)
 

modules/shared.py

@@ -13,21 +13,101 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
+from modules import localization, sd_vae, extensions, script_loading, errors, ui_components
-from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
+from modules.paths import models_path, script_path, sd_path
+
 
 demo = None
 
-parser = cmd_args.parser
+sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml")
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
 
-script_loading.preload_extensions(extensions_dir, parser)
+parser = argparse.ArgumentParser()
-script_loading.preload_extensions(extensions_builtin_dir, parser)
+parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
+parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
+parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
+parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
+parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
+parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
+parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
+parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
+parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
+parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
 
-if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
-    cmd_opts = parser.parse_args()
-else:
-    cmd_opts, _ = parser.parse_known_args()
 
+script_loading.preload_extensions(extensions.extensions_dir, parser)
+script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
+
+cmd_opts = parser.parse_args()
 
 restricted_opts = {
     "samples_filename_pattern",
@@ -44,13 +124,12 @@ restricted_opts = {
 ui_reorder_categories = [
     "inpaint",
     "sampler",
-    "checkboxes",
-    "hires_fix",
     "dimensions",
     "cfg",
     "seed",
+    "checkboxes",
+    "hires_fix",
     "batch",
-    "override_settings",
     "scripts",
 ]
 
@@ -184,6 +263,12 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+
+def realesrgan_models_names():
+    import modules.realesrgan_model
+    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
+
+
 class OptionInfo:
     def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
         self.default = default
@@ -218,7 +303,6 @@ def list_samplers():
 
 
 hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-tab_names = []
 
 options_templates = {}
 
@@ -240,16 +324,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
-    "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
-    "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
+    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
-    "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
-    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
-    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
-    "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
 
-    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
+    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
@@ -271,22 +349,22 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
 }))
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
+    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
@@ -318,7 +396,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
+    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
@@ -330,13 +408,12 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
-    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
-    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
     "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
 }))
 
@@ -356,22 +433,15 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 }))
 
 options_templates.update(options_section(('extra_networks', "Extra Networks"), {
-    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
+    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
-    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
-    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
-    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
-    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
@@ -383,7 +453,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
     "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
     "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
     "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
@@ -409,21 +478,15 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
-    'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
-    'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
-    'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
-    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
-    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
-    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
 }))
 
 options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "disabled_extensions": OptionInfo([], "Disable those extensions"),
-    "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
 
@@ -488,15 +551,6 @@ class Options:
 
         return True
 
-    def get_default(self, key):
-        """returns the default value for the key"""
-
-        data_label = self.data_labels.get(key)
-        if data_label is None:
-            return None
-
-        return data_label.default
-
     def save(self, filename):
         assert not cmd_opts.freeze_settings, "saving settings is disabled"
 
@@ -551,37 +605,11 @@ class Options:
 
         self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
 
-    def cast_value(self, key, value):
-        """casts an arbitrary to the same type as this setting's value with key
-        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
-        """
-
-        if value is None:
-            return None
-
-        default_value = self.data_labels[key].default
-        if default_value is None:
-            default_value = getattr(self, key, None)
-        if default_value is None:
-            return None
-
-        expected_type = type(default_value)
-        if expected_type == bool and value == "False":
-            value = False
-        else:
-            value = expected_type(value)
-
-        return value
-
-
 
 opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
-settings_components = None
-"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
-
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},
@@ -629,7 +657,6 @@ class TotalTQDM:
 
     def clear(self):
         if self._tqdm is not None:
-            self._tqdm.refresh()
             self._tqdm.close()
             self._tqdm = None
 
@@ -641,7 +668,7 @@ mem_mon.start()
 
 
 def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
     return [file for file in filenames if os.path.isfile(file)]
 
 

modules/shared_items.py

@@ -1,23 +0,0 @@
-
-
-def realesrgan_models_names():
-    import modules.realesrgan_model
-    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
-
-
-def postprocessing_scripts():
-    import modules.scripts
-
-    return modules.scripts.scripts_postproc.scripts
-
-
-def sd_vae_items():
-    import modules.sd_vae
-
-    return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
-
-
-def refresh_vae_list():
-    import modules.sd_vae
-
-    modules.sd_vae.refresh_vae_list()

modules/sub_quadratic_attention.py

@@ -67,7 +67,7 @@ def _summarize_chunk(
     max_score, _ = torch.max(attn_weights, -1, keepdim=True)
     max_score = max_score.detach()
     exp_weights = torch.exp(attn_weights - max_score)
-    exp_values = torch.bmm(exp_weights, value) if query.device.type == 'mps' else torch.bmm(exp_weights, value.to(exp_weights.dtype)).to(value.dtype)
+    exp_values = torch.bmm(exp_weights, value)
     max_score = max_score.squeeze(-1)
     return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
 
@@ -129,7 +129,7 @@ def _get_attention_scores_no_kv_chunking(
     )
     attn_probs = attn_scores.softmax(dim=-1)
     del attn_scores
-    hidden_states_slice = torch.bmm(attn_probs, value) if query.device.type == 'mps' else torch.bmm(attn_probs, value.to(attn_probs.dtype)).to(value.dtype)
+    hidden_states_slice = torch.bmm(attn_probs, value)
     return hidden_states_slice
 
 

modules/textual_inversion/dataset.py

@@ -19,10 +19,9 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")
 
 
 class DatasetEntry:
-    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
         self.filename = filename
         self.filename_text = filename_text
-        self.weight = weight
         self.latent_dist = latent_dist
         self.latent_sample = latent_sample
         self.cond = cond
@@ -31,7 +30,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -57,16 +56,10 @@ class PersonalizedBase(Dataset):
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
-            alpha_channel = None
             if shared.state.interrupted:
                 raise Exception("interrupted")
             try:
-                image = Image.open(path)
+                image = Image.open(path).convert('RGB')
-                #Currently does not work for single color transparency
-                #We would need to read image.info['transparency'] for that
-                if use_weight and 'A' in image.getbands():
-                    alpha_channel = image.getchannel('A')
-                image = image.convert('RGB')
                 if not varsize:
                     image = image.resize((width, height), PIL.Image.BICUBIC)
             except Exception:
@@ -94,35 +87,17 @@ class PersonalizedBase(Dataset):
             with devices.autocast():
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
 
-            #Perform latent sampling, even for random sampling.
+            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
-            #We need the sample dimensions for the weights
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-            if latent_sampling_method == "deterministic":
+                latent_sampling_method = "once"
-                if isinstance(latent_dist, DiagonalGaussianDistribution):
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-                    # Works only for DiagonalGaussianDistribution
+            elif latent_sampling_method == "deterministic":
-                    latent_dist.std = 0
+                # Works only for DiagonalGaussianDistribution
-                else:
+                latent_dist.std = 0
-                    latent_sampling_method = "once"
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-
+            elif latent_sampling_method == "random":
-            if use_weight and alpha_channel is not None:
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
-                channels, *latent_size = latent_sample.shape
-                weight_img = alpha_channel.resize(latent_size)
-                npweight = np.array(weight_img).astype(np.float32)
-                #Repeat for every channel in the latent sample
-                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
-                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
-                weight -= weight.min()
-                weight /= weight.mean()
-            elif use_weight:
-                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
-                weight = torch.ones(latent_sample.shape)
-            else:
-                weight = None
-            
-            if latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
-            else:
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
 
             if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
@@ -135,7 +110,6 @@ class PersonalizedBase(Dataset):
             del torchdata
             del latent_dist
             del latent_sample
-            del weight
 
         self.length = len(self.dataset)
         self.groups = list(groups.values())
@@ -221,10 +195,6 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
-        if all(entry.weight is not None for entry in data):
-            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
-        else:
-            self.weight = None
         #self.emb_index = [entry.emb_index for entry in data]
         #print(self.latent_sample.device)
 

modules/textual_inversion/preprocess.py

@@ -6,7 +6,8 @@ import sys
 import tqdm
 import time
 
-from modules import paths, shared, images, deepbooru
+from modules import shared, images, deepbooru
+from modules.paths import models_path
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 
@@ -198,7 +199,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
             dnn_model_path = None
             try:
-                dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
             except Exception as e:
                 print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
 

modules/textual_inversion/textual_inversion.py

@@ -112,7 +112,6 @@ class EmbeddingDatabase:
         self.skipped_embeddings = {}
         self.expected_shape = -1
         self.embedding_dirs = {}
-        self.previously_displayed_embeddings = ()
 
     def add_embedding_dir(self, path):
         self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
@@ -152,11 +151,7 @@ class EmbeddingDatabase:
                 name = data.get('name', name)
             else:
                 data = extract_image_data_embed(embed_image)
-                if data:
+                name = data.get('name', name)
-                    name = data.get('name', name)
-                else:
-                    # if data is None, means this is not an embeding, just a preview image
-                    return
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:
@@ -199,7 +194,7 @@ class EmbeddingDatabase:
         if not os.path.isdir(embdir.path):
             return
 
-        for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+        for root, dirs, fns in os.walk(embdir.path):
             for fn in fns:
                 try:
                     fullfn = os.path.join(root, fn)
@@ -233,12 +228,9 @@ class EmbeddingDatabase:
             self.load_from_dir(embdir)
             embdir.update()
 
-        displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
+        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
-        if self.previously_displayed_embeddings != displayed_embeddings:
+        if len(self.skipped_embeddings) > 0:
-            self.previously_displayed_embeddings = displayed_embeddings
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
-            print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
-            if len(self.skipped_embeddings) > 0:
-                print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
@@ -355,7 +347,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"
 
 
-def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     template_file = textual_inversion_templates.get(template_filename, None)
@@ -414,7 +406,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
     pin_memory = shared.opts.pin_memory
 
-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
 
     if shared.opts.save_training_settings_to_txt:
         save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
@@ -484,8 +476,6 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
-                    if use_weight:
-                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
 
                     if is_training_inpainting_model:
@@ -496,11 +486,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                     else:
                         cond = c
 
-                    if use_weight:
+                    loss = shared.sd_model(x, cond)[0] / gradient_step
-                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
-                        del w
-                    else:
-                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step
                     del x
 
                     _loss_step += loss.item()

modules/timer.py

@@ -1,38 +0,0 @@
-import time
-
-
-class Timer:
-    def __init__(self):
-        self.start = time.time()
-        self.records = {}
-        self.total = 0
-
-    def elapsed(self):
-        end = time.time()
-        res = end - self.start
-        self.start = end
-        return res
-
-    def record(self, category, extra_time=0):
-        e = self.elapsed()
-        if category not in self.records:
-            self.records[category] = 0
-
-        self.records[category] += e + extra_time
-        self.total += e + extra_time
-
-    def summary(self):
-        res = f"{self.total:.1f}s"
-
-        additions = [x for x in self.records.items() if x[1] >= 0.1]
-        if not additions:
-            return res
-
-        res += " ("
-        res += ", ".join([f"{category}: {time_taken:.1f}s" for category, time_taken in additions])
-        res += ")"
-
-        return res
-
-    def reset(self):
-        self.__init__()

modules/txt2img.py

@@ -1,6 +1,5 @@
 import modules.scripts
 from modules import sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -9,9 +8,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):
-    override_settings = create_override_settings_dict(override_settings_texts)
-
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -41,7 +38,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         hr_second_pass_steps=hr_second_pass_steps,
         hr_resize_x=hr_resize_x,
         hr_resize_y=hr_resize_y,
-        override_settings=override_settings,
     )
 
     p.scripts = modules.scripts.scripts_txt2img

modules/ui.py

@@ -20,8 +20,8 @@ from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
-from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path, data_path
+from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
 
@@ -70,6 +70,17 @@ def gr_show(visible=True):
 sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
 sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.wrap.cover-bg .z-20::before { content:"" }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
 # Using constants for these since the variation selector isn't visible.
 # Important that they exactly match script.js for tooltip to work.
 random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
@@ -78,9 +89,8 @@ paste_symbol = '\u2199\ufe0f'  # ↙
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
+clear_prompt_symbol = '\U0001F5D1'  # 🗑️
 extra_networks_symbol = '\U0001F3B4'  # 🎴
-switch_values_symbol = '\U000021C5' # ⇅
 
 
 def plaintext_to_html(text):
@@ -168,13 +178,14 @@ def interrogate_deepbooru(image):
 
 
 def create_seed_inputs(target_interface):
-    with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
+    with FormRow(elem_id=target_interface + '_seed_row'):
         seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
         seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
+        random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+        reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
 
-        seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+        with gr.Group(elem_id=target_interface + '_subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
@@ -183,8 +194,8 @@ def create_seed_inputs(target_interface):
         seed_extras.append(seed_extra_row_1)
         subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
         subseed.style(container=False)
-        random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
+        random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
-        reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
         subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
 
     with FormRow(visible=False) as seed_extra_row_2:
@@ -279,19 +290,19 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
 
         button_interrogate = None
         button_deepbooru = None
         if is_img2img:
-            with gr.Column(scale=1, elem_classes="interrogate-col"):
+            with gr.Column(scale=1, elem_id="interrogate_col"):
                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
                 button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+            with gr.Row(elem_id=f"{id_part}_generate_box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
                 submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
 
                 skip.click(
@@ -313,9 +324,9 @@ def create_toprow(is_img2img):
                 prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
                 save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
 
-                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
                 token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                negative_token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_negative_token_counter")
                 negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
 
                 clear_prompt_button.click(
@@ -368,7 +379,6 @@ def apply_setting(key, value):
     opts.save(shared.config_filename)
     return getattr(opts, key)
 
-
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -422,18 +432,6 @@ def get_value_for_setting(key):
     return gr.update(value=value, **args)
 
 
-def create_override_settings_dropdown(tabname, row):
-    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
-
-    dropdown.change(
-        fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
-        inputs=[dropdown],
-        outputs=[dropdown],
-    )
-
-    return dropdown
-
-
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -467,9 +465,6 @@ def create_ui():
                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
-                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
-
                             if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="txt2img_column_batch"):
                                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
@@ -482,7 +477,7 @@ def create_ui():
                         seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
 
                     elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                        with FormRow(elem_id="txt2img_checkboxes", variant="compact"):
                             restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                             tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
                             enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
@@ -506,10 +501,6 @@ def create_ui():
                                 batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                 batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
-                    elif category == "override_settings":
-                        with FormRow(elem_id="txt2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('txt2img', row)
-
                     elif category == "scripts":
                         with FormGroup(elem_id="txt2img_script_container"):
                             custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
@@ -531,6 +522,7 @@ def create_ui():
                 )
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -561,7 +553,6 @@ def create_ui():
                     hr_second_pass_steps,
                     hr_resize_x,
                     hr_resize_y,
-                    override_settings,
                 ] + custom_inputs,
 
                 outputs=[
@@ -576,8 +567,6 @@ def create_ui():
             txt2img_prompt.submit(**txt2img_args)
             submit.click(**txt2img_args)
 
-            res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
-
             txt_prompt_img.change(
                 fn=modules.images.image_data,
                 inputs=[
@@ -621,10 +610,7 @@ def create_ui():
                 (hr_resize_y, "Hires resize-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
-            ))
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -705,15 +691,9 @@ def create_ui():
 
                     with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(
+                        gr.HTML(f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                            f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
-                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
-                            f"{hidden}</p>"
-                        )
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
-                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
 
                 def copy_image(img):
                     if isinstance(img, dict) and 'image' in img:
@@ -747,9 +727,6 @@ def create_ui():
                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
-                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
-
                             if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="img2img_column_batch"):
                                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
@@ -757,16 +734,14 @@ def create_ui():
 
                     elif category == "cfg":
                         with FormGroup():
-                            with FormRow():
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
                             denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
                     elif category == "seed":
                         seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
                     elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                        with FormRow(elem_id="img2img_checkboxes", variant="compact"):
                             restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
                             tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
 
@@ -776,10 +751,6 @@ def create_ui():
                                 batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                 batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
-                    elif category == "override_settings":
-                        with FormRow(elem_id="img2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('img2img', row)
-
                     elif category == "scripts":
                         with FormGroup(elem_id="img2img_script_container"):
                             custom_inputs = modules.scripts.scripts_img2img.setup_ui()
@@ -814,6 +785,7 @@ def create_ui():
                                 )
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -855,7 +827,6 @@ def create_ui():
                     batch_count,
                     batch_size,
                     cfg_scale,
-                    image_cfg_scale,
                     denoising_strength,
                     seed,
                     subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
@@ -867,8 +838,6 @@ def create_ui():
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
-                    img2img_batch_inpaint_mask_dir,
-                    override_settings,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -896,7 +865,6 @@ def create_ui():
 
             img2img_prompt.submit(**img2img_args)
             submit.click(**img2img_args)
-            res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
 
             img2img_interrogate.click(
                 fn=lambda *args: process_interrogate(interrogate, *args),
@@ -931,7 +899,7 @@ def create_ui():
                 )
 
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
 
             ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
 
@@ -942,7 +910,6 @@ def create_ui():
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
                 (cfg_scale, "CFG scale"),
-                (image_cfg_scale, "Image CFG scale"),
                 (seed, "Seed"),
                 (width, "Size-1"),
                 (height, "Size-2"),
@@ -955,11 +922,8 @@ def create_ui():
                 (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
-            ))
 
     modules.scripts.scripts_current = None
 
@@ -977,11 +941,7 @@ def create_ui():
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-                for tabname, button in buttons.items():
-                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
-                    ))
 
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
@@ -1183,8 +1143,6 @@ def create_ui():
                         create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
                         save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
 
-                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
-
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
 
@@ -1298,7 +1256,6 @@ def create_ui():
                 shuffle_tags,
                 tag_drop_out,
                 latent_sampling_method,
-                use_weight,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1332,7 +1289,6 @@ def create_ui():
                 shuffle_tags,
                 tag_drop_out,
                 latent_sampling_method,
-                use_weight,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1394,7 +1350,6 @@ def create_ui():
 
     components = []
     component_dict = {}
-    shared.settings_components = component_dict
 
     script_callbacks.ui_settings_callback()
     opts.reorder()
@@ -1483,34 +1438,12 @@ def create_ui():
                 request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                 reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
-                with gr.Row():
-                    unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
-                    reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
 
             with gr.TabItem("Licenses"):
                 gr.HTML(shared.html("licenses.html"), elem_id="licenses")
 
             gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
 
-
-        def unload_sd_weights():
-            modules.sd_models.unload_model_weights()
-
-        def reload_sd_weights():
-            modules.sd_models.reload_model_weights()
-
-        unload_sd_model.click(
-            fn=unload_sd_weights,
-            inputs=[],
-            outputs=[]
-        )
-
-        reload_sd_model.click(
-            fn=reload_sd_weights,
-            inputs=[],
-            outputs=[]
-        )
-
         request_notifications.click(
             fn=lambda: None,
             inputs=[],
@@ -1555,28 +1488,39 @@ def create_ui():
         (train_interface, "Train", "ti"),
     ]
 
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
     interfaces += script_callbacks.ui_tabs_callback()
     interfaces += [(settings_interface, "Settings", "settings")]
 
     extensions_interface = ui_extensions.create_ui()
     interfaces += [(extensions_interface, "Extensions", "extensions")]
 
-    shared.tab_names = []
+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-    for _interface, label, _ifid in interfaces:
-        shared.tab_names.append(label)
-
-    with gr.Blocks(analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings", variant="compact"):
             for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        parameters_copypaste.connect_paste_params_buttons()
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
 
         with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
-                if label in shared.opts.hidden_tabs:
-                    continue
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
 
@@ -1596,29 +1540,13 @@ def create_ui():
 
         for i, k, item in quicksettings_list:
             component = component_dict[k]
-            info = opts.data_labels[k]
 
             component.change(
                 fn=lambda value, k=k: run_settings_single(value, key=k),
                 inputs=[component],
                 outputs=[component, text_settings],
-                show_progress=info.refresh is not None,
             )
 
-        text_settings.change(
-            fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
-            inputs=[],
-            outputs=[image_cfg_scale],
-        )
-
-        button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
-        button_set_checkpoint.click(
-            fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
-            _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
-            inputs=[component_dict['sd_model_checkpoint'], dummy_component],
-            outputs=[component_dict['sd_model_checkpoint'], text_settings],
-        )
-
         component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
 
         def get_settings_values():
@@ -1628,7 +1556,6 @@ def create_ui():
             fn=get_settings_values,
             inputs=[],
             outputs=[component_dict[k] for k in component_keys],
-            queue=False,
         )
 
         def modelmerger(*args):
@@ -1751,60 +1678,21 @@ def create_ui():
     return demo
 
 
-def webpath(fn):
+def reload_javascript():
-    if fn.startswith(script_path):
+    head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
-        web_path = os.path.relpath(fn, script_path).replace('\\', '/')
-    else:
-        web_path = os.path.abspath(fn)
-
-    return f'file={web_path}?{os.path.getmtime(fn)}'
-
-
-def javascript_html():
-    script_js = os.path.join(script_path, "script.js")
-    head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
 
     inline = f"{localization.localization_js(shared.opts.localization)};"
     if cmd_opts.theme is not None:
         inline += f"set_theme('{cmd_opts.theme}');"
 
     for script in modules.scripts.list_scripts("javascript", ".js"):
-        head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
+        head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
-
-    for script in modules.scripts.list_scripts("javascript", ".mjs"):
-        head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
 
     head += f'<script type="text/javascript">{inline}</script>\n'
 
-    return head
-
-
-def css_html():
-    head = ""
-
-    def stylesheet(fn):
-        return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        head += stylesheet(cssfile)
-
-    if os.path.exists(os.path.join(data_path, "user.css")):
-        head += stylesheet(os.path.join(data_path, "user.css"))
-
-    return head
-
-
-def reload_javascript():
-    js = javascript_html()
-    css = css_html()
-
     def template_response(*args, **kwargs):
         res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+        res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
-        res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
         res.init_headers()
         return res
 
@@ -1832,7 +1720,7 @@ def versions_html():
     return f"""
 python: <span title="{sys.version}">{python_version}</span>
  • 
-torch: {getattr(torch, '__long_version__',torch.__version__)}
+torch: {torch.__version__}
  • 
 xformers: {xformers_version}
  • 

modules/ui_common.py

@@ -129,8 +129,8 @@ Requested path was: {f}
 
         generation_info = None
         with gr.Column():
-            with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+            with gr.Row(elem_id=f"image_buttons_{tabname}"):
-                open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
+                open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
 
                 if tabname != "extras":
                     save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -145,10 +145,11 @@ Requested path was: {f}
             )
 
             if tabname != "extras":
-                download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+                with gr.Row():
+                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
 
                 with gr.Group():
-                    html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+                    html_info = gr.HTML(elem_id=f'html_info_{tabname}')
                     html_log = gr.HTML(elem_id=f'html_log_{tabname}')
 
                     generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
@@ -159,7 +160,6 @@ Requested path was: {f}
                             _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
                             inputs=[generation_info, html_info, html_info],
                             outputs=[html_info, html_info],
-                            show_progress=False,
                         )
 
                     save.click(
@@ -195,19 +195,8 @@ Requested path was: {f}
 
             else:
                 html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
-                html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+                html_info = gr.HTML(elem_id=f'html_info_{tabname}')
                 html_log = gr.HTML(elem_id=f'html_log_{tabname}')
 
-            paste_field_names = []
+            parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-            if tabname == "txt2img":
-                paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
-            elif tabname == "img2img":
-                paste_field_names = modules.scripts.scripts_img2img.paste_field_names
-
-            for paste_tabname, paste_button in buttons.items():
-                parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                    paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery,
-                    paste_field_names=paste_field_names
-                ))
-
             return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log

modules/ui_components.py

@@ -1,64 +1,50 @@
 import gradio as gr
 
 
-class FormComponent:
+class ToolButton(gr.Button, gr.components.FormComponent):
-    def get_expected_parent(self):
-        return gr.components.Form
-
-
-gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
-
-
-class ToolButton(FormComponent, gr.Button):
     """Small button with single emoji as text, fits inside gradio forms"""
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, **kwargs):
-        classes = kwargs.pop("elem_classes", [])
+        super().__init__(variant="tool", **kwargs)
-        super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
 
     def get_block_name(self):
         return "button"
 
 
-class FormRow(FormComponent, gr.Row):
+class ToolButtonTop(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool-top", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+
+class FormRow(gr.Row, gr.components.FormComponent):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "row"
 
 
-class FormColumn(FormComponent, gr.Column):
+class FormGroup(gr.Group, gr.components.FormComponent):
-    """Same as gr.Column but fits inside gradio forms"""
-
-    def get_block_name(self):
-        return "column"
-
-
-class FormGroup(FormComponent, gr.Group):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "group"
 
 
-class FormHTML(FormComponent, gr.HTML):
+class FormHTML(gr.HTML, gr.components.FormComponent):
     """Same as gr.HTML but fits inside gradio forms"""
 
     def get_block_name(self):
         return "html"
 
 
-class FormColorPicker(FormComponent, gr.ColorPicker):
+class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
     """Same as gr.ColorPicker but fits inside gradio forms"""
 
     def get_block_name(self):
         return "colorpicker"
 
-
-class DropdownMulti(FormComponent, gr.Dropdown):
-    """Same as gr.Dropdown but always multiselect"""
-    def __init__(self, **kwargs):
-        super().__init__(multiselect=True, **kwargs)
-
-    def get_block_name(self):
-        return "dropdown"

modules/ui_extensions.py

@@ -1,5 +1,6 @@
 import json
 import os.path
+import shutil
 import sys
 import time
 import traceback
@@ -12,7 +13,7 @@ import shutil
 import errno
 
 from modules import extensions, shared, paths
-from modules.call_queue import wrap_gradio_gpu_call
+
 
 available_extensions = {"extensions": []}
 
@@ -21,7 +22,7 @@ def check_access():
     assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
 
 
-def apply_and_restart(disable_list, update_list, disable_all):
+def apply_and_restart(disable_list, update_list):
     check_access()
 
     disabled = json.loads(disable_list)
@@ -43,37 +44,26 @@ def apply_and_restart(disable_list, update_list, disable_all):
             print(traceback.format_exc(), file=sys.stderr)
 
     shared.opts.disabled_extensions = disabled
-    shared.opts.disable_all_extensions = disable_all
     shared.opts.save(shared.config_filename)
 
     shared.state.interrupt()
     shared.state.need_restart = True
 
 
-def check_updates(id_task, disable_list):
+def check_updates():
     check_access()
 
-    disabled = json.loads(disable_list)
+    for ext in extensions.extensions:
-    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+        if ext.remote is None:
-
+            continue
-    exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
-    shared.state.job_count = len(exts)
-
-    for ext in exts:
-        shared.state.textinfo = ext.name
 
         try:
             ext.check_updates()
-        except FileNotFoundError as e:
-            if 'FETCH_HEAD' not in str(e):
-                raise
         except Exception:
             print(f"Error checking updates for {ext.name}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
-        shared.state.nextjob()
+    return extension_table()
-
-    return extension_table(), ""
 
 
 def extension_table():
@@ -83,7 +73,6 @@ def extension_table():
             <tr>
                 <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
                 <th>URL</th>
-                <th><abbr title="Extension version">Version</abbr></th>
                 <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
             </tr>
         </thead>
@@ -91,24 +80,21 @@ def extension_table():
     """
 
     for ext in extensions.extensions:
-        ext.read_info_from_repo()
+        remote = ""
-
+        if ext.is_builtin:
-        remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+            remote = "built-in"
+        elif ext.remote:
+            remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
 
         if ext.can_update:
             ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
         else:
             ext_status = ext.status
 
-        style = ""
-        if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
-            style = ' style="color: var(--primary-400)"'
-
         code += f"""
             <tr>
-                <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
                 <td>{remote}</td>
-                <td>{ext.version}</td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
@@ -146,24 +132,26 @@ def install_extension_from_url(dirname, url):
     normalized_url = normalize_git_url(url)
     assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
 
-    tmpdir = os.path.join(paths.data_path, "tmp", dirname)
+    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
 
     try:
         shutil.rmtree(tmpdir, True)
-        with git.Repo.clone_from(url, tmpdir) as repo:
+
-            repo.remote().fetch()
+        repo = git.Repo.clone_from(url, tmpdir)
-            for submodule in repo.submodules:
+        repo.remote().fetch()
-                submodule.update()
+
         try:
             os.rename(tmpdir, target_dir)
         except OSError as err:
+            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
+            # Shouldn't cause any new issues at least but we probably want to handle it there too.
             if err.errno == errno.EXDEV:
                 # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
                 # Since we can't use a rename, do the slower but more versitile shutil.move()
                 shutil.move(tmpdir, target_dir)
             else:
                 # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
-                raise err
+                raise(err)
 
         import launch
         launch.run_extension_installer(target_dir)
@@ -174,12 +162,12 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)
 
 
-def install_extension_from_index(url, hide_tags, sort_column, filter_text):
+def install_extension_from_index(url, hide_tags, sort_column):
     ext_table, message = install_extension_from_url(None, url)
 
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
 
-    return code, ext_table, message, ''
+    return code, ext_table, message
 
 
 def refresh_available_extensions(url, hide_tags, sort_column):
@@ -193,17 +181,11 @@ def refresh_available_extensions(url, hide_tags, sort_column):
 
     code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
 
-    return url, code, gr.CheckboxGroup.update(choices=tags), '', ''
+    return url, code, gr.CheckboxGroup.update(choices=tags), ''
 
 
-def refresh_available_extensions_for_tags(hide_tags, sort_column, filter_text):
+def refresh_available_extensions_for_tags(hide_tags, sort_column):
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
-
-    return code, ''
-
-
-def search_extensions(filter_text, hide_tags, sort_column):
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
 
     return code, ''
 
@@ -218,7 +200,7 @@ sort_ordering = [
 ]
 
 
-def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
+def refresh_available_extensions_from_data(hide_tags, sort_column):
     extlist = available_extensions["extensions"]
     installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
 
@@ -257,12 +239,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
             hidden += 1
             continue
 
-        if filter_text and filter_text.strip():
+        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
-            if filter_text.lower() not in html.escape(name).lower() and filter_text.lower() not in html.escape(description).lower():
-                hidden += 1
-                continue
-
-        install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""
 
         tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
 
@@ -296,41 +273,32 @@ def create_ui():
         with gr.Tabs(elem_id="tabs_extensions") as tabs:
             with gr.TabItem("Installed"):
 
-                with gr.Row(elem_id="extensions_installed_top"):
+                with gr.Row():
                     apply = gr.Button(value="Apply and restart UI", variant="primary")
                     check = gr.Button(value="Check for updates")
-                    extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
                     extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
                     extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
 
-                html = ""
-                if shared.opts.disable_all_extensions != "none":
-                    html = """
-<span style="color: var(--primary-400);">
-    "Disable all extensions" was set, change it to "none" to load all extensions again
-</span>
-                    """
-                info = gr.HTML(html)
                 extensions_table = gr.HTML(lambda: extension_table())
 
                 apply.click(
                     fn=apply_and_restart,
                     _js="extensions_apply",
-                    inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
+                    inputs=[extensions_disabled_list, extensions_update_list],
                     outputs=[],
                 )
 
                 check.click(
-                    fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
+                    fn=check_updates,
                     _js="extensions_check",
-                    inputs=[info, extensions_disabled_list],
+                    inputs=[],
-                    outputs=[extensions_table, info],
+                    outputs=[extensions_table],
                 )
 
             with gr.TabItem("Available"):
                 with gr.Row():
                     refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
-                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json", label="Extension index URL").style(container=False)
+                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
@@ -338,39 +306,30 @@ def create_ui():
                     hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
                     sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
 
-                with gr.Row(): 
-                    search_extensions_text = gr.Text(label="Search").style(container=False)
-                   
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
 
                 refresh_available_extensions_button.click(
                     fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
                     inputs=[available_extensions_index, hide_tags, sort_column],
-                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
                 )
 
                 install_extension_button.click(
                     fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[extension_to_install, hide_tags, sort_column, search_extensions_text],
+                    inputs=[extension_to_install, hide_tags, sort_column],
                     outputs=[available_extensions_table, extensions_table, install_result],
                 )
 
-                search_extensions_text.change(
-                    fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
-                    inputs=[search_extensions_text, hide_tags, sort_column],
-                    outputs=[available_extensions_table, install_result],
-                )
-
                 hide_tags.change(
                     fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column, search_extensions_text],
+                    inputs=[hide_tags, sort_column],
                     outputs=[available_extensions_table, install_result]
                 )
 
                 sort_column.change(
                     fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column, search_extensions_text],
+                    inputs=[hide_tags, sort_column],
                     outputs=[available_extensions_table, install_result]
                 )
 

modules/ui_extra_networks.py

@@ -1,11 +1,6 @@
-import glob
 import os.path
-import urllib.parse
-from pathlib import Path
-from PIL import PngImagePlugin
 
 from modules import shared
-from modules.images import read_info_from_image
 import gradio as gr
 import json
 import html
@@ -13,48 +8,12 @@ import html
 from modules.generation_parameters_copypaste import image_from_url_text
 
 extra_pages = []
-allowed_dirs = set()
 
 
 def register_page(page):
     """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
 
     extra_pages.append(page)
-    allowed_dirs.clear()
-    allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
-
-
-def fetch_file(filename: str = ""):
-    from starlette.responses import FileResponse
-
-    if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
-        raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
-
-    ext = os.path.splitext(filename)[1].lower()
-    if ext not in (".png", ".jpg", ".webp"):
-        raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
-
-    # would profit from returning 304
-    return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
-
-
-def get_metadata(page: str = "", item: str = ""):
-    from starlette.responses import JSONResponse
-
-    page = next(iter([x for x in extra_pages if x.name == page]), None)
-    if page is None:
-        return JSONResponse({})
-
-    metadata = page.metadata.get(item)
-    if metadata is None:
-        return JSONResponse({})
-
-    return JSONResponse({"metadata": metadata})
-
-
-def add_pages_to_demo(app):
-    app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
-    app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
 
 
 class ExtraNetworksPage:
@@ -63,73 +22,23 @@ class ExtraNetworksPage:
         self.name = title.lower()
         self.card_page = shared.html("extra-networks-card.html")
         self.allow_negative_prompt = False
-        self.metadata = {}
 
     def refresh(self):
         pass
 
-    def link_preview(self, filename):
-        return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
-
-    def search_terms_from_path(self, filename, possible_directories=None):
-        abspath = os.path.abspath(filename)
-
-        for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
-            parentdir = os.path.abspath(parentdir)
-            if abspath.startswith(parentdir):
-                return abspath[len(parentdir):].replace('\\', '/')
-
-        return ""
-
     def create_html(self, tabname):
         view = shared.opts.extra_networks_default_view
         items_html = ''
 
-        self.metadata = {}
-
-        subdirs = {}
-        for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
-            for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
-                if not os.path.isdir(x):
-                    continue
-
-                subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
-                while subdir.startswith("/"):
-                    subdir = subdir[1:]
-
-                is_empty = len(os.listdir(x)) == 0
-                if not is_empty and not subdir.endswith("/"):
-                    subdir = subdir + "/"
-
-                subdirs[subdir] = 1
-
-        if subdirs:
-            subdirs = {"": 1, **subdirs}
-
-        subdirs_html = "".join([f"""
-<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
-{html.escape(subdir if subdir!="" else "all")}
-</button>
-""" for subdir in subdirs])
-
         for item in self.list_items():
-            metadata = item.get("metadata")
-            if metadata:
-                self.metadata[item["name"]] = metadata
-
             items_html += self.create_html_for_item(item, tabname)
 
         if items_html == '':
             dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
             items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
 
-        self_name_id = self.name.replace(" ", "_")
-
         res = f"""
-<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
+<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
-{subdirs_html}
-</div>
-<div id='{tabname}_{self_name_id}_cards' class='extra-network-{view}'>
 {items_html}
 </div>
 """
@@ -145,62 +54,18 @@ class ExtraNetworksPage:
     def create_html_for_item(self, item, tabname):
         preview = item.get("preview", None)
 
-        onclick = item.get("onclick", None)
-        if onclick is None:
-            onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
-
-        height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
-        width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
-        background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
-        metadata_button = ""
-        metadata = item.get("metadata")
-        if metadata:
-            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
-
         args = {
-            "style": f"'{height}{width}{background_image}'",
+            "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
-            "prompt": item.get("prompt", None),
+            "prompt": item["prompt"],
             "tabname": json.dumps(tabname),
             "local_preview": json.dumps(item["local_preview"]),
             "name": item["name"],
-            "description": (item.get("description") or ""),
+            "card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
-            "card_clicked": onclick,
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
-            "search_term": item.get("search_term", ""),
-            "metadata_button": metadata_button,
         }
 
         return self.card_page.format(**args)
 
-    def find_preview(self, path):
-        """
-        Find a preview PNG for a given path (without extension) and call link_preview on it.
-        """
-
-        preview_extensions = ["png", "jpg", "webp"]
-        if shared.opts.samples_format not in preview_extensions:
-            preview_extensions.append(shared.opts.samples_format)
-
-        potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
-
-        for file in potential_files:
-            if os.path.isfile(file):
-                return self.link_preview(file)
-
-        return None
-
-    def find_description(self, path):
-        """
-        Find and read a description file for a given path (without extension).
-        """
-        for file in [f"{path}.txt", f"{path}.description.txt"]:
-            try:
-                with open(file, "r", encoding="utf-8", errors="replace") as f:
-                    return f.read()
-            except OSError:
-                pass
-        return None
-
 
 def intialize():
     extra_pages.clear()
@@ -242,22 +107,18 @@ def create_ui(container, button, tabname):
     with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
         for page in ui.stored_extra_pages:
             with gr.Tab(page.title):
-
                 page_elem = gr.HTML(page.create_html(ui.tabname))
                 ui.pages.append(page_elem)
 
     filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
     button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
+    button_close = gr.Button('Close', elem_id=tabname+"_extra_close")
 
     ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
     ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
 
-    def toggle_visibility(is_visible):
+    button.click(fn=lambda: gr.update(visible=True), inputs=[], outputs=[container])
-        is_visible = not is_visible
+    button_close.click(fn=lambda: gr.update(visible=False), inputs=[], outputs=[container])
-        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
-
-    state_visible = gr.State(value=False)
-    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
 
     def refresh():
         res = []
@@ -277,7 +138,7 @@ def path_is_parent(parent_path, child_path):
     parent_path = os.path.abspath(parent_path)
     child_path = os.path.abspath(child_path)
 
-    return child_path.startswith(parent_path)
+    return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path])
 
 
 def setup_ui(ui, gallery):
@@ -292,7 +153,6 @@ def setup_ui(ui, gallery):
 
         img_info = images[index if index >= 0 else 0]
         image = image_from_url_text(img_info)
-        geninfo, items = read_info_from_image(image)
 
         is_allowed = False
         for extra_page in ui.stored_extra_pages:
@@ -302,19 +162,13 @@ def setup_ui(ui, gallery):
 
         assert is_allowed, f'writing to {filename} is not allowed'
 
-        if geninfo:
+        image.save(filename)
-            pnginfo_data = PngImagePlugin.PngInfo()
-            pnginfo_data.add_text('parameters', geninfo)
-            image.save(filename, pnginfo=pnginfo_data)
-        else:
-            image.save(filename)
 
         return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
 
     ui.button_save_preview.click(
         fn=save_preview,
-        _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
+        _js="function(x, y, z){console.log(x, y, z); return [selected_gallery_index(), y, z]}",
         inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
         outputs=[*ui.pages]
     )
-

modules/ui_extra_networks_checkpoints.py

@@ -1,31 +0,0 @@
-import html
-import json
-import os
-
-from modules import shared, ui_extra_networks, sd_models
-
-
-class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
-    def __init__(self):
-        super().__init__('Checkpoints')
-
-    def refresh(self):
-        shared.refresh_checkpoints()
-
-    def list_items(self):
-        checkpoint: sd_models.CheckpointInfo
-        for name, checkpoint in sd_models.checkpoints_list.items():
-            path, ext = os.path.splitext(checkpoint.filename)
-            yield {
-                "name": checkpoint.name_for_extra,
-                "filename": path,
-                "preview": self.find_preview(path),
-                "description": self.find_description(path),
-                "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
-                "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
-                "local_preview": f"{path}.{shared.opts.samples_format}",
-            }
-
-    def allowed_directories_for_previews(self):
-        return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
-

modules/ui_extra_networks_hypernets.py

@@ -14,15 +14,20 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for name, path in shared.hypernetworks.items():
             path, ext = os.path.splitext(path)
+            previews = [path + ".png", path + ".preview.png"]
+
+            preview = None
+            for file in previews:
+                if os.path.isfile(file):
+                    preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
+                    break
 
             yield {
                 "name": name,
                 "filename": path,
-                "preview": self.find_preview(path),
+                "preview": preview,
-                "description": self.find_description(path),
-                "search_term": self.search_terms_from_path(path),
                 "prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
-                "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+                "local_preview": path + ".png",
             }
 
     def allowed_directories_for_previews(self):

modules/ui_extra_networks_textual_inversion.py

@@ -1,7 +1,7 @@
 import json
 import os
 
-from modules import ui_extra_networks, sd_hijack, shared
+from modules import ui_extra_networks, sd_hijack
 
 
 class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
@@ -15,14 +15,18 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
             path, ext = os.path.splitext(embedding.filename)
+            preview_file = path + ".preview.png"
+
+            preview = None
+            if os.path.isfile(preview_file):
+                preview = "./file=" + preview_file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(preview_file))
+
             yield {
                 "name": embedding.name,
                 "filename": embedding.filename,
-                "preview": self.find_preview(path),
+                "preview": preview,
-                "description": self.find_description(path),
-                "search_term": self.search_terms_from_path(embedding.filename),
                 "prompt": json.dumps(embedding.name),
-                "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+                "local_preview": path + ".preview.png",
             }
 
     def allowed_directories_for_previews(self):

modules/upscaler.py

@@ -11,6 +11,7 @@ from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
+from modules.paths import models_path
 
 
 class Upscaler:
@@ -38,7 +39,7 @@ class Upscaler:
         self.mod_scale = None
 
         if self.model_path is None and self.name:
-            self.model_path = os.path.join(shared.models_path, self.name)
+            self.model_path = os.path.join(models_path, self.name)
         if self.model_path and create_dirs:
             os.makedirs(self.model_path, exist_ok=True)
 

requirements.txt

@@ -4,7 +4,7 @@ basicsr
 fonts
 font-roboto
 gfpgan
-gradio==3.23
+gradio==3.16.2
 invisible-watermark
 numpy
 omegaconf
@@ -16,7 +16,7 @@ pytorch_lightning==1.7.7
 realesrgan
 scikit-image>=0.19
 timm==0.4.12
-transformers==4.25.1
+transformers==4.19.2
 torch
 einops
 jsonmerge
@@ -30,4 +30,3 @@ GitPython
 torchsde
 safetensors
 psutil
-rich

requirements_versions.txt

@@ -1,15 +1,15 @@
 blendmodes==2022
-transformers==4.25.1
+transformers==4.19.2
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.23
+gradio==3.16.2
 numpy==1.23.3
 Pillow==9.4.0
 realesrgan==0.3.0
 torch
 omegaconf==2.2.3
-pytorch_lightning==1.9.4
+pytorch_lightning==1.7.6
 scikit-image==0.19.2
 fonts
 font-roboto
@@ -23,8 +23,7 @@ torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
 inflection==0.5.1
-GitPython==3.1.30
+GitPython==3.1.27
 torchsde==0.2.5
-safetensors==0.3.0
+safetensors==0.2.7
 httpcore<=0.15
-fastapi==0.94.0

script.js

@@ -1,9 +1,7 @@
 function gradioApp() {
     const elems = document.getElementsByTagName('gradio-app')
-    const elem = elems.length == 0 ? document : elems[0]
+    const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
-
+    return !!gradioShadowRoot ? gradioShadowRoot : document;
-    if (elem !== document) elem.getElementById = function(id){ return document.getElementById(id) }
-    return elem.shadowRoot ? elem.shadowRoot : elem
 }
 
 function get_uiCurrentTab() {

scripts/img2imgalt.py

@@ -6,21 +6,23 @@ from tqdm import trange
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import processing, shared, sd_samplers, sd_samplers_common
+from modules import processing, shared, sd_samplers, prompt_parser
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
 
 import torch
 import k_diffusion as K
 
+from PIL import Image
+from torch import autocast
+from einops import rearrange, repeat
+
+
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
-    if shared.sd_model.parameterization == "v":
+    dnw = K.external.CompVisDenoiser(shared.sd_model)
-        dnw = K.external.CompVisVDenoiser(shared.sd_model)
-        skip = 1
-    else:
-        dnw = K.external.CompVisDenoiser(shared.sd_model)
-        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -35,7 +37,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
         t = dnw.sigma_to_t(sigma_in)
 
         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@@ -48,7 +50,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
         x = x + d * dt
 
-        sd_samplers_common.store_latent(x)
+        sd_samplers.store_latent(x)
 
         # This shouldn't be necessary, but solved some VRAM issues
         del x_in, sigma_in, cond_in, c_out, c_in, t,
@@ -67,12 +69,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
-    if shared.sd_model.parameterization == "v":
+    dnw = K.external.CompVisDenoiser(shared.sd_model)
-        dnw = K.external.CompVisVDenoiser(shared.sd_model)
-        skip = 1
-    else:
-        dnw = K.external.CompVisDenoiser(shared.sd_model)
-        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -87,7 +84,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
 
         if i == 1:
             t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
@@ -107,7 +104,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         dt = sigmas[i] - sigmas[i - 1]
         x = x + d * dt
 
-        sd_samplers_common.store_latent(x)
+        sd_samplers.store_latent(x)
 
         # This shouldn't be necessary, but solved some VRAM issues
         del x_in, sigma_in, cond_in, c_out, c_in, t,
@@ -216,3 +213,4 @@ class Script(scripts.Script):
         processed = processing.process_images(p)
 
         return processed
+

scripts/loopback.py

@@ -1,10 +1,13 @@
-import math
+import numpy as np
+from tqdm import trange
 
-import gradio as gr
 import modules.scripts as scripts
-from modules import deepbooru, images, processing, shared
+import gradio as gr
+
+from modules import processing, shared, sd_samplers, images
 from modules.processing import Processed
-from modules.shared import opts, state
+from modules.sd_samplers import samplers
+from modules.shared import opts, cmd_opts, state
 
 
 class Script(scripts.Script):
@@ -16,65 +19,37 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):        
         loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
-        final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+        denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
-        denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
-        append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
 
-        return [loops, final_denoising_strength, denoising_curve, append_interrogation]
+        return [loops, denoising_strength_change_factor]
 
-    def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
+    def run(self, p, loops, denoising_strength_change_factor):
         processing.fix_seed(p)
         batch_count = p.n_iter
         p.extra_generation_params = {
-            "Final denoising strength": final_denoising_strength,
+            "Denoising strength change factor": denoising_strength_change_factor,
-            "Denoising curve": denoising_curve
         }
 
         p.batch_size = 1
         p.n_iter = 1
 
-        info = None
+        output_images, info = None, None
         initial_seed = None
         initial_info = None
-        initial_denoising_strength = p.denoising_strength
 
         grids = []
         all_images = []
         original_init_image = p.init_images
-        original_prompt = p.prompt
-        original_inpainting_fill = p.inpainting_fill
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
 
-        def calculate_denoising_strength(loop):
-            strength = initial_denoising_strength
-
-            if loops == 1:
-                return strength
-
-            progress = loop / (loops - 1)
-            if denoising_curve == "Aggressive":
-                strength = math.sin((progress) * math.pi * 0.5)
-            elif denoising_curve == "Lazy":
-                strength = 1 - math.cos((progress) * math.pi * 0.5)
-            else:
-                strength = progress
-
-            change = (final_denoising_strength - initial_denoising_strength) * strength
-            return initial_denoising_strength + change
-
-        history = []
-
         for n in range(batch_count):
+            history = []
+
             # Reset to original init image at the start of each batch
             p.init_images = original_init_image
 
-            # Reset to original denoising strength
-            p.denoising_strength = initial_denoising_strength
-
-            last_image = None
-
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1
@@ -83,57 +58,30 @@ class Script(scripts.Script):
                 if opts.img2img_color_correction:
                     p.color_corrections = initial_color_corrections
 
-                if append_interrogation != "None":
-                    p.prompt = original_prompt + ", " if original_prompt != "" else ""
-                    if append_interrogation == "CLIP":
-                        p.prompt += shared.interrogator.interrogate(p.init_images[0])
-                    elif append_interrogation == "DeepBooru":
-                        p.prompt += deepbooru.model.tag(p.init_images[0])
-
                 state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
 
                 processed = processing.process_images(p)
 
-                # Generation cancelled.
-                if state.interrupted:
-                    break
-
                 if initial_seed is None:
                     initial_seed = processed.seed
                     initial_info = processed.info
 
+                init_img = processed.images[0]
+
+                p.init_images = [init_img]
                 p.seed = processed.seed + 1
-                p.denoising_strength = calculate_denoising_strength(i + 1)
+                p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
+                history.append(processed.images[0])
 
-                if state.skipped:
-                    break
-
-                last_image = processed.images[0]
-                p.init_images = [last_image]
-                p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
-
-                if batch_count == 1:
-                    history.append(last_image)
-                    all_images.append(last_image)
-
-            if batch_count > 1 and not state.skipped and not state.interrupted:
-                history.append(last_image)
-                all_images.append(last_image)
-
-            p.inpainting_fill = original_inpainting_fill
-                
-            if state.interrupted:
-                    break
-
-        if len(history) > 1:
             grid = images.image_grid(history, rows=1)
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
 
-            if opts.return_grid:
+            grids.append(grid)
-                grids.append(grid)
+            all_images += history
 
-        all_images = grids + all_images
+        if opts.return_grid:
+            all_images = grids + all_images
 
         processed = Processed(p, all_images, initial_seed, initial_info)
 

scripts/postprocessing_upscale.py

@@ -17,24 +17,22 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
     def ui(self):
         selected_tab = gr.State(value=0)
 
-        with gr.Column():
+        with gr.Tabs(elem_id="extras_resize_mode"):
-            with FormRow():
+            with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
-                with gr.Tabs(elem_id="extras_resize_mode"):
+                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
-                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
 
-                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+            with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
-                        with FormRow():
+                with FormRow():
-                            upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                    upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
-                            upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                    upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                    upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
-            with FormRow():
+        with FormRow():
-                extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+            extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
 
-            with FormRow():
+        with FormRow():
-                extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+            extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-                extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+            extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
 
         tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
         tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
@@ -106,28 +104,3 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
 
     def image_changed(self):
         upscale_cache.clear()
-
-
-class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
-    name = "Simple Upscale"
-    order = 900
-
-    def ui(self):
-        with FormRow():
-            upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-            upscale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Upscale by", value=2)
-
-        return {
-            "upscale_by": upscale_by,
-            "upscaler_name": upscaler_name,
-        }
-
-    def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
-        if upscaler_name is None or upscaler_name == "None":
-            return
-
-        upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)
-        assert upscaler1, f'could not find upscaler named {upscaler_name}'
-
-        pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
-        pp.info[f"Postprocess upscaler"] = upscaler1.name

scripts/prompt_matrix.py

@@ -45,33 +45,15 @@ class Script(scripts.Script):
         return "Prompt matrix"
 
     def ui(self, is_img2img):        
-        gr.HTML('<br />')
+        put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
-        with gr.Row():
+        different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
-            with gr.Column():
-                put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
-                different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
-            with gr.Column():
-                prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
-                variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
-            with gr.Column():
-                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
 
-        return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
+        return [put_at_start, different_seeds]
 
-    def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size):
+    def run(self, p, put_at_start, different_seeds):
         modules.processing.fix_seed(p)
-        # Raise error if promp type is not positive or negative
-        if prompt_type not in ["positive", "negative"]:
-            raise ValueError(f"Unknown prompt type {prompt_type}")
-        # Raise error if variations delimiter is not comma or space
-        if variations_delimiter not in ["comma", "space"]:
-            raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
 
-        prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
+        original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
-        original_prompt = prompt[0] if type(prompt) == list else prompt
-        positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
-
-        delimiter = ", " if variations_delimiter == "comma" else " "
 
         all_prompts = []
         prompt_matrix_parts = original_prompt.split("|")
@@ -84,23 +66,20 @@ class Script(scripts.Script):
             else:
                 selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
 
-            all_prompts.append(delimiter.join(selected_prompts))
+            all_prompts.append(", ".join(selected_prompts))
 
         p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
         p.do_not_save_grid = True
 
         print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
 
-        if prompt_type == "positive":
+        p.prompt = all_prompts
-            p.prompt = all_prompts
-        else:
-            p.negative_prompt = all_prompts
         p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
-        p.prompt_for_display = positive_prompt
+        p.prompt_for_display = original_prompt
         processed = process_images(p)
 
         grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
-        grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[0].height, prompt_matrix_parts, margin_size)
+        grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
         processed.images.insert(0, grid)
         processed.index_of_first_image = 1
         processed.infotexts.insert(0, processed.infotexts[0])

scripts/xy_grid.py

@@ -25,8 +25,6 @@ from modules.ui_components import ToolButton
 
 fill_values_symbol = "\U0001f4d2"  # 📒
 
-AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
-
 
 def apply_field(field):
     def fun(p, x, xs):
@@ -125,25 +123,7 @@ def apply_vae(p, x, xs):
 
 
 def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
-    p.styles.extend(x.split(','))
+    p.styles = x.split(',')
-
-
-def apply_uni_pc_order(p, x, xs):
-    opts.data["uni_pc_order"] = min(x, p.steps - 1)
-
-
-def apply_face_restore(p, opt, x):
-    opt = opt.lower()
-    if opt == 'codeformer':
-        is_active = True
-        p.face_restoration_model = 'CodeFormer'
-    elif opt == 'gfpgan':
-        is_active = True
-        p.face_restoration_model = 'GFPGAN'
-    else:
-        is_active = opt in ('true', 'yes', 'y', '1')
-
-    p.restore_faces = is_active
 
 
 def format_value_add_label(p, opt, x):
@@ -206,7 +186,6 @@ axis_options = [
     AxisOption("Steps", int, apply_field("steps")),
     AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
     AxisOption("CFG Scale", float, apply_field("cfg_scale")),
-    AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
     AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
     AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
     AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
@@ -223,119 +202,69 @@ axis_options = [
     AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
     AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
     AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
-    AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
-    AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
 ]
 
 
-def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images, swap_axes_processing_order):
-    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
-    title_texts = [[images.GridAnnotation(z)] for z in z_labels]
+    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
 
-    list_size = (len(xs) * len(ys) * len(zs))
+    # Temporary list of all the images that are generated to be populated into the grid.
+    # Will be filled with empty images for any individual step that fails to process properly
+    image_cache = [None] * (len(xs) * len(ys))
 
     processed_result = None
+    cell_mode = "P"
+    cell_size = (1, 1)
 
-    state.job_count = list_size * p.n_iter
+    state.job_count = len(xs) * len(ys) * p.n_iter
 
-    def process_cell(x, y, z, ix, iy, iz):
+    def process_cell(x, y, ix, iy):
-        nonlocal processed_result
+        nonlocal image_cache, processed_result, cell_mode, cell_size
 
-        def index(ix, iy, iz):
+        state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
-            return ix + iy * len(xs) + iz * len(xs) * len(ys)
 
-        state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
+        processed: Processed = cell(x, y)
 
-        processed: Processed = cell(x, y, z, ix, iy, iz)
+        try:
+            # this dereference will throw an exception if the image was not processed
+            # (this happens in cases such as if the user stops the process from the UI)
+            processed_image = processed.images[0]
 
-        if processed_result is None:
+            if processed_result is None:
-            # Use our first processed result object as a template container to hold our full results
+                # Use our first valid processed result as a template container to hold our full results
-            processed_result = copy(processed)
+                processed_result = copy(processed)
-            processed_result.images = [None] * list_size
+                cell_mode = processed_image.mode
-            processed_result.all_prompts = [None] * list_size
+                cell_size = processed_image.size
-            processed_result.all_seeds = [None] * list_size
+                processed_result.images = [Image.new(cell_mode, cell_size)]
-            processed_result.infotexts = [None] * list_size
-            processed_result.index_of_first_image = 1
 
-        idx = index(ix, iy, iz)
+            image_cache[ix + iy * len(xs)] = processed_image
-        if processed.images:
+            if include_lone_images:
-            # Non-empty list indicates some degree of success.
+                processed_result.images.append(processed_image)
-            processed_result.images[idx] = processed.images[0]
+                processed_result.all_prompts.append(processed.prompt)
-            processed_result.all_prompts[idx] = processed.prompt
+                processed_result.all_seeds.append(processed.seed)
-            processed_result.all_seeds[idx] = processed.seed
+                processed_result.infotexts.append(processed.infotexts[0])
-            processed_result.infotexts[idx] = processed.infotexts[0]
+        except:
-        else:
+            image_cache[ix + iy * len(xs)] = Image.new(cell_mode, cell_size)
-            cell_mode = "P"
-            cell_size = (processed_result.width, processed_result.height)
-            if processed_result.images[0] is not None:
-                cell_mode = processed_result.images[0].mode
-                #This corrects size in case of batches:
-                cell_size = processed_result.images[0].size
-            processed_result.images[idx] = Image.new(cell_mode, cell_size)
 
-
+    if swap_axes_processing_order:
-    if first_axes_processed == 'x':
         for ix, x in enumerate(xs):
-            if second_axes_processed == 'y':
+            for iy, y in enumerate(ys):
-                for iy, y in enumerate(ys):
+                process_cell(x, y, ix, iy)
-                    for iz, z in enumerate(zs):
+    else:
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iz, z in enumerate(zs):
-                    for iy, y in enumerate(ys):
-                        process_cell(x, y, z, ix, iy, iz)
-    elif first_axes_processed == 'y':
         for iy, y in enumerate(ys):
-            if second_axes_processed == 'x':
+            for ix, x in enumerate(xs):
-                for ix, x in enumerate(xs):
+                process_cell(x, y, ix, iy)
-                    for iz, z in enumerate(zs):
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iz, z in enumerate(zs):
-                    for ix, x in enumerate(xs):
-                        process_cell(x, y, z, ix, iy, iz)
-    elif first_axes_processed == 'z':
-        for iz, z in enumerate(zs):
-            if second_axes_processed == 'x':
-                for ix, x in enumerate(xs):
-                    for iy, y in enumerate(ys):
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iy, y in enumerate(ys):
-                    for ix, x in enumerate(xs):
-                        process_cell(x, y, z, ix, iy, iz)
 
     if not processed_result:
-        # Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
+        print("Unexpected error: draw_xy_grid failed to return even a single processed image")
-        print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
-        return Processed(p, [])
-    elif not any(processed_result.images):
-        print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
         return Processed(p, [])
 
-    z_count = len(zs)
+    grid = images.image_grid(image_cache, rows=len(ys))
-    sub_grids = [None] * z_count
-    for i in range(z_count):
-        start_index = (i * len(xs) * len(ys)) + i
-        end_index = start_index + len(xs) * len(ys)
-        grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
-        if draw_legend:
-            grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
-        processed_result.images.insert(i, grid)
-        processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
-        processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
-        processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
-
-    sub_grid_size = processed_result.images[0].size
-    z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
     if draw_legend:
-        z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
+        grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
-    processed_result.images.insert(0, z_grid)
+
-    #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+    processed_result.images[0] = grid
-    #processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-    #processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
-    processed_result.infotexts.insert(0, processed_result.infotexts[0])
 
     return processed_result
 
@@ -344,11 +273,9 @@ class SharedSettingsStackHelper(object):
     def __enter__(self):
         self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
         self.vae = opts.sd_vae
-        self.uni_pc_order = opts.uni_pc_order
   
     def __exit__(self, exc_type, exc_value, tb):
         opts.data["sd_vae"] = self.vae
-        opts.data["uni_pc_order"] = self.uni_pc_order
         modules.sd_models.reload_model_weights()
         modules.sd_vae.reload_vae_weights()
 
@@ -364,7 +291,7 @@ re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+
 
 class Script(scripts.Script):
     def title(self):
-        return "X/Y/Z plot"
+        return "X/Y plot"
 
     def ui(self, is_img2img):
         self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
@@ -374,42 +301,24 @@ class Script(scripts.Script):
                 with gr.Row():
                     x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                     x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
-                    fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
+                    fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_x_tool_button", visible=False)
 
                 with gr.Row():
                     y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
-                    fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
+                    fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_y_tool_button", visible=False)
-
-                with gr.Row():
-                    z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
-                    z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
-                    fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
 
         with gr.Row(variant="compact", elem_id="axis_options"):
-            with gr.Column():
+            draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
-                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
+            include_lone_images = gr.Checkbox(label='Include Separate Images', value=False, elem_id=self.elem_id("include_lone_images"))
-                no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
+            no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
-            with gr.Column():
+            swap_axes_button = gr.Button(value="Swap axes", elem_id="xy_grid_swap_axes_button")
-                include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
-                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
-            with gr.Column():
-                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
 
-        with gr.Row(variant="compact", elem_id="swap_axes"):
+        def swap_axes(x_type, x_values, y_type, y_values):
-            swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
+            return self.current_axis_options[y_type].label, y_values, self.current_axis_options[x_type].label, x_values
-            swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
-            swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
 
-        def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
+        swap_args = [x_type, x_values, y_type, y_values]
-            return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+        swap_axes_button.click(swap_axes, inputs=swap_args, outputs=swap_args)
-
-        xy_swap_args = [x_type, x_values, y_type, y_values]
-        swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
-        yz_swap_args = [y_type, y_values, z_type, z_values]
-        swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
-        xz_swap_args = [x_type, x_values, z_type, z_values]
-        swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
 
         def fill(x_type):
             axis = self.current_axis_options[x_type]
@@ -417,27 +326,16 @@ class Script(scripts.Script):
 
         fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
         fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
 
         def select_axis(x_type):
             return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
 
         x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
         y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
-        z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
 
-        self.infotext_fields = (
+        return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
-            (x_type, "X Type"),
-            (x_values, "X Values"),
-            (y_type, "Y Type"),
-            (y_values, "Y Values"),
-            (z_type, "Z Type"),
-            (z_values, "Z Values"),
-        )
 
-        return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
-
-    def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
@@ -448,7 +346,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
 
             if opt.type == int:
                 valslist_ext = []
@@ -511,14 +409,6 @@ class Script(scripts.Script):
         y_opt = self.current_axis_options[y_type]
         ys = process_axis(y_opt, y_values)
 
-        z_opt = self.current_axis_options[z_type]
-        zs = process_axis(z_opt, z_values)
-
-        # this could be moved to common code, but unlikely to be ever triggered anywhere else
-        Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
-        grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
-        assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
-
         def fix_axis_seeds(axis_opt, axis_list):
             if axis_opt.label in ['Seed', 'Var. seed']:
                 return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
@@ -528,26 +418,21 @@ class Script(scripts.Script):
         if not no_fixed_seeds:
             xs = fix_axis_seeds(x_opt, xs)
             ys = fix_axis_seeds(y_opt, ys)
-            zs = fix_axis_seeds(z_opt, zs)
 
         if x_opt.label == 'Steps':
-            total_steps = sum(xs) * len(ys) * len(zs)
+            total_steps = sum(xs) * len(ys)
         elif y_opt.label == 'Steps':
-            total_steps = sum(ys) * len(xs) * len(zs)
+            total_steps = sum(ys) * len(xs)
-        elif z_opt.label == 'Steps':
-            total_steps = sum(zs) * len(xs) * len(ys)
         else:
-            total_steps = p.steps * len(xs) * len(ys) * len(zs)
+            total_steps = p.steps * len(xs) * len(ys)
 
         if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
             if x_opt.label == "Hires steps":
-                total_steps += sum(xs) * len(ys) * len(zs)
+                total_steps += sum(xs) * len(ys)
             elif y_opt.label == "Hires steps":
-                total_steps += sum(ys) * len(xs) * len(zs)
+                total_steps += sum(ys) * len(xs)
-            elif z_opt.label == "Hires steps":
-                total_steps += sum(zs) * len(xs) * len(ys)
             elif p.hr_second_pass_steps:
-                total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
+                total_steps += p.hr_second_pass_steps * len(xs) * len(ys)
             else:
                 total_steps *= 2
 
@@ -555,57 +440,28 @@ class Script(scripts.Script):
 
         image_cell_count = p.n_iter * p.batch_size
         cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
-        plural_s = 's' if len(zs) > 1 else ''
+        print(f"X/Y plot will create {len(xs) * len(ys) * image_cell_count} images on a {len(xs)}x{len(ys)} grid{cell_console_text}. (Total steps to process: {total_steps})")
-        print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
         shared.total_tqdm.updateTotal(total_steps)
 
-        state.xyz_plot_x = AxisInfo(x_opt, xs)
+        grid_infotext = [None]
-        state.xyz_plot_y = AxisInfo(y_opt, ys)
-        state.xyz_plot_z = AxisInfo(z_opt, zs)
 
         # If one of the axes is very slow to change between (like SD model
         # checkpoint), then make sure it is in the outer iteration of the nested
         # `for` loop.
-        first_axes_processed = 'z'
+        swap_axes_processing_order = x_opt.cost > y_opt.cost
-        second_axes_processed = 'y'
-        if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
-            first_axes_processed = 'x'
-            if y_opt.cost > z_opt.cost:
-                second_axes_processed = 'y'
-            else:
-                second_axes_processed = 'z'
-        elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
-            first_axes_processed = 'y'
-            if x_opt.cost > z_opt.cost:
-                second_axes_processed = 'x'
-            else:
-                second_axes_processed = 'z'
-        elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
-            first_axes_processed = 'z'
-            if x_opt.cost > y_opt.cost:
-                second_axes_processed = 'x'
-            else:
-                second_axes_processed = 'y'
 
-        grid_infotext = [None] * (1 + len(zs))
+        def cell(x, y):
-
-        def cell(x, y, z, ix, iy, iz):
             if shared.state.interrupted:
                 return Processed(p, [], p.seed, "")
 
             pc = copy(p)
-            pc.styles = pc.styles[:]
             x_opt.apply(pc, x, xs)
             y_opt.apply(pc, y, ys)
-            z_opt.apply(pc, z, zs)
 
             res = process_images(pc)
 
-            # Sets subgrid infotexts
+            if grid_infotext[0] is None:
-            subgrid_index = 1 + iz
-            if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
                 pc.extra_generation_params = copy(pc.extra_generation_params)
-                pc.extra_generation_params['Script'] = self.title()
 
                 if x_opt.label != 'Nothing':
                     pc.extra_generation_params["X Type"] = x_opt.label
@@ -619,67 +475,24 @@ class Script(scripts.Script):
                     if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                         pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
 
-                grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
-
-            # Sets main grid infotext
-            if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
-                pc.extra_generation_params = copy(pc.extra_generation_params)
-
-                if z_opt.label != 'Nothing':
-                    pc.extra_generation_params["Z Type"] = z_opt.label
-                    pc.extra_generation_params["Z Values"] = z_values
-                    if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
-                        pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
-
                 grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
 
             return res
 
         with SharedSettingsStackHelper():
-            processed = draw_xyz_grid(
+            processed = draw_xy_grid(
                 p,
                 xs=xs,
                 ys=ys,
-                zs=zs,
                 x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
                 y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
-                z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
                 cell=cell,
                 draw_legend=draw_legend,
                 include_lone_images=include_lone_images,
-                include_sub_grids=include_sub_grids,
+                swap_axes_processing_order=swap_axes_processing_order
-                first_axes_processed=first_axes_processed,
-                second_axes_processed=second_axes_processed,
-                margin_size=margin_size
             )
 
-        if not processed.images:
-            # It broke, no further handling needed.
-            return processed
-
-        z_count = len(zs)
-
-        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
-        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
-
-        if not include_lone_images:
-            # Don't need sub-images anymore, drop from list:
-            processed.images = processed.images[:z_count+1]
-
         if opts.grid_save:
-            # Auto-save main and sub-grids:
+            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
-            grid_count = z_count + 1 if z_count > 1 else 1
-            for g in range(grid_count):
-                #TODO: See previous comment about intentional data misalignment.
-                adj_g = g-1 if g > 0 else g
-                images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
-
-        if not include_sub_grids:
-            # Done with sub-grids, drop all related information:
-            for sg in range(z_count):
-                del processed.images[1]
-                del processed.all_prompts[1]
-                del processed.all_seeds[1]
-                del processed.infotexts[1]
 
         return processed

test/basic_features/extras_test.py

@@ -1,9 +1,7 @@
-import os
 import unittest
 import requests
 from gradio.processing_utils import encode_pil_to_base64
 from PIL import Image
-from modules.paths import script_path
 
 class TestExtrasWorking(unittest.TestCase):
     def setUp(self):
@@ -21,7 +19,7 @@ class TestExtrasWorking(unittest.TestCase):
             "upscaler_1": "None",
             "upscaler_2": "None",
             "extras_upscaler_2_visibility": 0,
-            "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))
+            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
             }
 
     def test_simple_upscaling_performed(self):
@@ -33,7 +31,7 @@ class TestPngInfoWorking(unittest.TestCase):
     def setUp(self):
         self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image"
         self.png_info = {
-            "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))
+            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
         }
 
     def test_png_info_performed(self):
@@ -44,7 +42,7 @@ class TestInterrogateWorking(unittest.TestCase):
     def setUp(self):
         self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image"
         self.interrogate = {
-            "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))),
+            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")),
             "model": "clip"
         }
 

test/basic_features/img2img_test.py

@@ -1,16 +1,14 @@
-import os
 import unittest
 import requests
 from gradio.processing_utils import encode_pil_to_base64
 from PIL import Image
-from modules.paths import script_path
 
 
 class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
         self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
         self.simple_img2img = {
-            "init_images": [encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))],
+            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
             "resize_mode": 0,
             "denoising_strength": 0.75,
             "mask": None,
@@ -49,11 +47,11 @@ class TestImg2ImgWorking(unittest.TestCase):
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
 
     def test_inpainting_masked_performed(self):
-        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))
+        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
 
     def test_inpainting_with_inverted_masked_performed(self):
-        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))
+        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
         self.simple_img2img["inpainting_mask_invert"] = True
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)