diff --git a/.gitignore b/.gitignore index 0b1d17ca..3b48ba9a 100644 --- a/.gitignore +++ b/.gitignore @@ -32,4 +32,4 @@ notification.mp3 /extensions /test/stdout.txt /test/stderr.txt -/cache.json +/cache.json* diff --git a/README.md b/README.md index 24f8e799..b67e2296 100644 --- a/README.md +++ b/README.md @@ -13,9 +13,9 @@ 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: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) + - a man in a `((tuxedo))` - will pay more attention to tuxedo + - 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) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion @@ -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 @@ -91,7 +91,6 @@ 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. @@ -101,7 +100,7 @@ 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. @@ -159,4 +158,4 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - 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) +- (You) \ No newline at end of file diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml index f8872750..0c4ae680 100644 --- a/environment-wsl2.yaml +++ b/environment-wsl2.yaml @@ -4,8 +4,8 @@ channels: - defaults dependencies: - python=3.10 - - pip=22.2.2 - - cudatoolkit=11.3 - - pytorch=1.12.1 - - torchvision=0.13.1 - - numpy=1.23.1 \ No newline at end of file + - pip=23.0 + - cudatoolkit=11.8 + - pytorch=2.0 + - torchvision=0.15 + - numpy=1.23 diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index 6be6ef73..45f899fc 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): 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: + if additional != "None" 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"" for x in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 7c371deb..d3eb0d3b 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -2,20 +2,34 @@ import glob import os import re import torch +from typing import Union from modules import shared, devices, sd_models, errors 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_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)") -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+)_(.+)") -re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)") +re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") +re_compiled = {} + +suffix_conversion = { + "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): - def match(match_list, regex): +def convert_diffusers_name_to_compvis(key, is_sd2): + def match(match_list, regex_text): + 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 @@ -26,16 +40,33 @@ def convert_diffusers_name_to_compvis(key): m = [] - if match(m, re_unet_down_blocks): - return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}" + if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" - if match(m, re_unet_mid_blocks): - return f"diffusion_model_middle_block_1_{m[1]}" + if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) + return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" - if match(m, re_unet_up_blocks): - return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}" + if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + 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 @@ -101,15 +132,22 @@ 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(): - fullkey = convert_diffusers_name_to_compvis(key_diffusers) - key, lora_key = fullkey.split(".", 1) + key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1) + key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2) sd_module = shared.sd_model.lora_layer_mapping.get(key, None) + if sd_module is None: - keys_failed_to_match.append(key_diffusers) + m = re_x_proj.match(key) + 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) @@ -123,15 +161,21 @@ 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.device, dtype=devices.dtype) + module.to(device=devices.cpu, dtype=devices.dtype) if lora_key == "lora_up.weight": lora_module.up = module @@ -177,29 +221,120 @@ def load_loras(names, multipliers=None): loaded_loras.append(lora) -def lora_forward(module, input, res): - input = devices.cond_cast_unet(input) - if len(loaded_loras) == 0: - return res +def lora_calc_updown(lora, module, target): + with torch.no_grad(): + up = module.up.weight.to(target.device, dtype=target.dtype) + down = module.down.weight.to(target.device, dtype=target.dtype) - lora_layer_name = getattr(module, 'lora_layer_name', None) - for lora in loaded_loras: - module = lora.modules.get(lora_layer_name, None) - if module is not None: - if shared.opts.lora_apply_to_outputs and res.shape == input.shape: - res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) + if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1): + updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3) + else: + updown = up @ down + + updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) + + 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: - res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) + self.weight.copy_(weights_backup) - return res + 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): - return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input)) + lora_apply_weights(self) + + 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): - return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input)) + lora_apply_weights(self) + + 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(): @@ -212,7 +347,7 @@ 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): + for filename in sorted(candidates, key=str.lower): if os.path.isdir(filename): continue diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..3fc38ab9 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared 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(): @@ -20,11 +24,27 @@ 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) @@ -32,7 +52,5 @@ 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), - "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"), - + "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), })) diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index e0fbf3a3..c7fd5739 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -5,11 +5,15 @@ import traceback import PIL.Image import numpy as np import torch +from tqdm import tqdm + from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from scunet_model_arch import SCUNet as net +from modules.shared import opts +from modules import images class UpscalerScuNET(modules.upscaler.Upscaler): @@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers.append(scaler_data2) self.scalers = scalers - def do_upscale(self, img: PIL.Image, selected_file): + @staticmethod + @torch.no_grad() + def tiled_inference(img, model): + # test the image tile by tile + h, w = img.shape[2:] + tile = opts.SCUNET_tile + tile_overlap = opts.SCUNET_tile_overlap + if tile == 0: + return model(img) + + device = devices.get_device_for('scunet') + assert tile % 8 == 0, "tile size should be a multiple of window_size" + sf = 1 + + stride = tile - tile_overlap + h_idx_list = list(range(0, h - tile, stride)) + [h - tile] + w_idx_list = list(range(0, w - tile, stride)) + [w - tile] + E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device) + W = torch.zeros_like(E, dtype=devices.dtype, device=device) + + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar: + for h_idx in h_idx_list: + + for w_idx in w_idx_list: + + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) + output = E.div_(W) + + return output + + def do_upscale(self, img: PIL.Image.Image, selected_file): + torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: + print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) return img device = devices.get_device_for('scunet') - img = np.array(img) - img = img[:, :, ::-1] - img = np.moveaxis(img, 2, 0) / 255 - img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(device) + tile = opts.SCUNET_tile + h, w = img.height, img.width + np_img = np.array(img) + np_img = np_img[:, :, ::-1] # RGB to BGR + np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW + torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore - with torch.no_grad(): - output = model(img) - output = output.squeeze().float().cpu().clamp_(0, 1).numpy() - output = 255. * np.moveaxis(output, 0, 2) - output = output.astype(np.uint8) - output = output[:, :, ::-1] + if tile > h or tile > w: + _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device) + _img[:, :, :h, :w] = torch_img # pad image + torch_img = _img + + torch_output = self.tiled_inference(torch_img, model).squeeze(0) + torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any + np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() + del torch_img, torch_output torch.cuda.empty_cache() - return PIL.Image.fromarray(output, 'RGB') + + output = np_output.transpose((1, 2, 0)) # CHW to HWC + output = output[:, :, ::-1] # BGR to RGB + return PIL.Image.fromarray((output * 255).astype(np.uint8)) def load_model(self, path: str): device = devices.get_device_for('scunet') @@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler): model = model.to(device) return model - diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 0f164b82..a8278cca 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){ currentHeight = e.target.value*1.0 } - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) + var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; 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('div[data-testid=image] img'); + targetElement = gradioApp().querySelector('#img2img_image 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 @@ -30,7 +30,7 @@ function dimensionChange(e, is_width, is_height){ } else if(tabIndex == 3){ // Inpaint sketch targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); } - + if(targetElement){ @@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){ if(!arPreviewRect){ arPreviewRect = document.createElement('div') arPreviewRect.id = "imageARPreview"; - gradioApp().getRootNode().appendChild(arPreviewRect) + gradioApp().appendChild(arPreviewRect) } @@ -91,23 +91,26 @@ onUiUpdate(function(){ if(arPreviewRect){ arPreviewRect.style.display = 'none'; } - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) - if(inImg2img){ - let inputs = gradioApp().querySelectorAll('input'); - inputs.forEach(function(e){ - var is_width = e.parentElement.id == "img2img_width" - var is_height = e.parentElement.id == "img2img_height" + var tabImg2img = gradioApp().querySelector("#tab_img2img"); + if (tabImg2img) { + var inImg2img = tabImg2img.style.display == "block"; + if(inImg2img){ + let inputs = gradioApp().querySelectorAll('input'); + inputs.forEach(function(e){ + 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')){ - e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} ) - e.classList.add('scrollwatch') - } - if(is_width){ - currentWidth = e.value*1.0 - } - if(is_height){ - currentHeight = e.value*1.0 - } - }) - } + if((is_width || is_height) && !e.classList.contains('scrollwatch')){ + e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} ) + e.classList.add('scrollwatch') + } + if(is_width){ + currentWidth = e.value*1.0 + } + if(is_height){ + currentHeight = e.value*1.0 + } + }) + } + } }); diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index 06f505b0..9468c107 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2]; appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever) - appendContextMenuOption('#roll','Roll three', - function(){ - let rollbutton = get_uiCurrentTabContent().querySelector('#roll'); - setTimeout(function(){rollbutton.click()},100) - setTimeout(function(){rollbutton.click()},200) - setTimeout(function(){rollbutton.click()},300) - } - ) })(); //End example Context Menu Items diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 20a5aadf..588c7b77 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -17,7 +17,7 @@ function keyupEditAttention(event){ // Find opening parenthesis around current cursor const before = text.substring(0, selectionStart); let beforeParen = before.lastIndexOf(OPEN); - if (beforeParen == -1) return false; + if (beforeParen == -1) return false; let beforeParenClose = before.lastIndexOf(CLOSE); while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); @@ -27,7 +27,7 @@ function keyupEditAttention(event){ // Find closing parenthesis around current cursor const after = text.substring(selectionStart); let afterParen = after.indexOf(CLOSE); - if (afterParen == -1) return false; + if (afterParen == -1) return false; let afterParenOpen = after.indexOf(OPEN); while (afterParenOpen !== -1 && afterParen > afterParenOpen) { afterParen = after.indexOf(CLOSE, afterParen + 1); @@ -43,10 +43,28 @@ function keyupEditAttention(event){ target.setSelectionRange(selectionStart, selectionEnd); return true; } + + function selectCurrentWord(){ + if (selectionStart !== selectionEnd) return false; + const delimiters = opts.keyedit_delimiters + " \r\n\t"; + + // seek backward until to find beggining + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } - // If the user hasn't selected anything, let's select their current parenthesis block - if(! selectCurrentParenthesisBlock('<', '>')){ - selectCurrentParenthesisBlock('(', ')') + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { + selectCurrentWord(); } event.preventDefault(); @@ -81,7 +99,13 @@ function keyupEditAttention(event){ weight = parseFloat(weight.toPrecision(12)); if(String(weight).length == 1) weight += ".0" - text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1); + if (closeCharacter == ')' && weight == 1) { + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5); + selectionStart--; + selectionEnd--; + } else { + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1); + } target.focus(); target.value = text; @@ -93,4 +117,4 @@ function keyupEditAttention(event){ addEventListener('keydown', (event) => { keyupEditAttention(event); -}); \ No newline at end of file +}); diff --git a/javascript/extensions.js b/javascript/extensions.js index c593cd2e..72924a28 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,5 +1,5 @@ -function extensions_apply(_, _){ +function extensions_apply(_, _, disable_all){ var disable = [] var update = [] @@ -13,10 +13,10 @@ function extensions_apply(_, _){ restart_reload() - return [JSON.stringify(disable), JSON.stringify(update)] + return [JSON.stringify(disable), JSON.stringify(update), disable_all] } -function extensions_check(){ +function extensions_check(_, _){ var disable = [] gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){ diff --git a/javascript/generationParams.js b/javascript/generationParams.js index 95f05093..06a771bc 100644 --- a/javascript/generationParams.js +++ b/javascript/generationParams.js @@ -16,7 +16,7 @@ onUiUpdate(function(){ let modalObserver = new MutationObserver(function(mutations) { mutations.forEach(function(mutationRecord) { - let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText + let selectedTab = gradioApp().querySelector('#tabs div button')?.innerText if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img') gradioApp().getElementById(selectedTab+"_generation_info_button").click() }); diff --git a/javascript/hints.js b/javascript/hints.js index b3f3d08d..f48a0eb6 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -21,8 +21,7 @@ titles = { "\u{1f5d1}\ufe0f": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", "\u{1f4d2}": "Paste available values into the field", - "\u{1f3b4}": "Show extra networks", - + "\u{1f3b4}": "Show/hide 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", diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 7547e771..3deffa9b 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -32,13 +32,7 @@ function negmod(n, m) { function updateOnBackgroundChange() { const modalImage = gradioApp().getElementById("modalImage") if (modalImage && modalImage.offsetParent) { - let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - let currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + let currentButton = selected_gallery_button(); if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; @@ -50,22 +44,10 @@ function updateOnBackgroundChange() { } function modalImageSwitch(offset) { - var allgalleryButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item") - var galleryButtons = [] - allgalleryButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - galleryButtons.push(elem); - } - }) + var galleryButtons = all_gallery_buttons(); if (galleryButtons.length > 1) { - var allcurrentButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item.selected") - var currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + var currentButton = selected_gallery_button(); var result = -1 galleryButtons.forEach(function(v, i) { @@ -269,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() { modal.appendChild(modalNext) - gradioApp().appendChild(modal) - + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } document.body.appendChild(modal); diff --git a/javascript/progressbar.js b/javascript/progressbar.js index 4ac9b8db..8df3f569 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre return } - if(elapsedFromStart > 5 && !res.queued && !res.active){ + if(elapsedFromStart > 40 && !res.queued && !res.active){ removeProgressBar() return } diff --git a/javascript/ui.js b/javascript/ui.js index fcaf5608..4a440193 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -7,9 +7,31 @@ 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$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected'); + var visibleCurrentButton = null; + allCurrentButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleCurrentButton = elem; + } + }) + return visibleCurrentButton; +} + function selected_gallery_index(){ - var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item') - var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2') + var buttons = all_gallery_buttons(); + var button = selected_gallery_button(); var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) @@ -18,14 +40,18 @@ function selected_gallery_index(){ } function extract_image_from_gallery(gallery){ - if(gallery.length == 1){ - return [gallery[0]] + if (gallery.length == 0){ + return [null]; + } + if (gallery.length == 1){ + return [gallery[0]]; } index = selected_gallery_index() if (index < 0 || index >= gallery.length){ - return [null] + // Use the first image in the gallery as the default + index = 0; } return [gallery[index]]; diff --git a/launch.py b/launch.py index c41ae82d..af1c8309 100644 --- a/launch.py +++ b/launch.py @@ -121,12 +121,12 @@ def run_python(code, desc=None, errdesc=None): return run(f'"{python}" -c "{code}"', desc, errdesc) -def run_pip(args, desc=None): +def run_pip(args, desc=None, live=False): if skip_install: return index_url_line = f' --index-url {index_url}' if index_url != '' else '' - return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}") + return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live) def check_run_python(code): @@ -206,6 +206,10 @@ 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 [] return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions] @@ -221,10 +225,10 @@ def run_extensions_installers(settings_file): def prepare_environment(): global skip_install - 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") + torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118") requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") - xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425') + xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17') 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") @@ -235,7 +239,7 @@ 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', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e") + stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf") 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") @@ -267,7 +271,7 @@ def prepare_environment(): if (not is_installed("xformers") or args.reinstall_xformers) and args.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_package}", "xformers", live=True) 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") @@ -292,7 +296,7 @@ def prepare_environment(): if not os.path.isfile(requirements_file): requirements_file = os.path.join(script_path, requirements_file) - run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI") + run_pip(f"install -r \"{requirements_file}\"", "requirements") run_extensions_installers(settings_file=args.ui_settings_file) diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th new file mode 100644 index 00000000..a6a06e94 Binary files /dev/null and b/models/karlo/ViT-L-14_stats.th differ diff --git a/modules/api/api.py b/modules/api/api.py index 13af9ed6..5ed670e9 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -3,9 +3,9 @@ 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.security import HTTPBasic, HTTPBasicCredentials from fastapi.exceptions import HTTPException @@ -197,6 +197,9 @@ class Api: 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: return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) @@ -230,7 +233,7 @@ class Api: script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] - def init_script_args(self, request, selectable_scripts, selectable_idx, script_runner): + 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: @@ -238,13 +241,24 @@ class Api: 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 - else: - # when [0] = 0 no selectable script to run - script_args[0] = 0 # Now check for always on scripts if request.alwayson_scripts and (len(request.alwayson_scripts) > 0): @@ -265,6 +279,8 @@ class Api: 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 @@ -280,7 +296,7 @@ class Api: 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, selectable_scripts, selectable_script_idx, script_runner) + 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) @@ -317,6 +333,8 @@ class Api: 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 @@ -334,7 +352,7 @@ class Api: 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, selectable_scripts, selectable_script_idx, script_runner) + 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) @@ -376,16 +394,11 @@ class Api: def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): reqDict = setUpscalers(req) - def prepareFiles(file): - file = decode_base64_to_file(file.data, file_path=file.name) - file.orig_name = file.name - return file - - reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) - reqDict.pop('imageList') + image_list = reqDict.pop('imageList', []) + image_folder = [decode_base64_to_image(x.data) for x in image_list] with self.queue_lock: - result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict) + result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) diff --git a/modules/cmd_args.py b/modules/cmd_args.py index 0af87251..81c0b82a 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -4,6 +4,7 @@ from modules.paths_internal import models_path, script_path, data_path, extensio 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") diff --git a/modules/devices.py b/modules/devices.py index 52c3e7cd..c705a3cb 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -92,14 +92,18 @@ def cond_cast_float(input): def randn(seed, shape): + from modules.shared import opts + torch.manual_seed(seed) - if device.type == 'mps': + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - if device.type == 'mps': + from modules.shared import opts + + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/extensions.py b/modules/extensions.py index a14ffbf0..3a7a0372 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -5,16 +5,22 @@ import traceback import time import git -from modules import paths, shared +from modules import shared from modules.paths_internal import extensions_dir, extensions_builtin_dir extensions = [] -if not os.path.exists(paths.extensions_dir): - os.makedirs(paths.extensions_dir) +if not os.path.exists(extensions_dir): + os.makedirs(extensions_dir) + def active(): - return [x for x in extensions if x.enabled] + if shared.opts.disable_all_extensions == "all": + 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: @@ -26,21 +32,29 @@ class Extension: 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(path, ".git")): - repo = git.Repo(path) + if os.path.exists(os.path.join(self.path, ".git")): + repo = git.Repo(self.path) except Exception: - print(f"Error reading github repository info from {path}:", file=sys.stderr) + print(f"Error reading github repository info from {self.path}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) if repo is None or repo.bare: self.remote = None else: try: - self.remote = next(repo.remote().urls, None) self.status = 'unknown' + self.remote = next(repo.remote().urls, None) head = repo.head.commit ts = time.asctime(time.gmtime(repo.head.commit.committed_date)) self.version = f'{head.hexsha[:8]} ({ts})' @@ -85,11 +99,16 @@ class Extension: def list_extensions(): extensions.clear() - if not os.path.isdir(paths.extensions_dir): + if not os.path.isdir(extensions_dir): return + if shared.opts.disable_all_extensions == "all": + 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 [paths.extensions_dir, paths.extensions_builtin_dir]: + for dirname in [extensions_dir, extensions_builtin_dir]: if not os.path.isdir(dirname): return @@ -98,9 +117,8 @@ def list_extensions(): if not os.path.isdir(path): continue - extension_paths.append((extension_dirname, path, dirname == paths.extensions_builtin_dir)) + extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir)) for dirname, path, is_builtin in extension_paths: extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin) extensions.append(extension) - diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index d3a4d7ad..33d100dd 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): 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: + if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0: p.all_prompts = [x + f"" for x in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 6df76858..e7269363 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model restore_old_hires_fix_params(res) + # Missing RNG means the default was set, which is GPU RNG + if "RNG" not in res: + res["RNG"] = "GPU" + return res @@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [ ('UniPC skip type', 'uni_pc_skip_type'), ('UniPC order', 'uni_pc_order'), ('UniPC lower order final', 'uni_pc_lower_order_final'), + ('RNG', 'randn_source'), ] diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f6ef42d5..1fc49537 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -312,7 +312,7 @@ class Hypernetwork: def list_hypernetworks(path): res = {} - for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)): + for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": diff --git a/modules/images.py b/modules/images.py index 7030aaaa..1a118a69 100644 --- a/modules/images.py +++ b/modules/images.py @@ -261,9 +261,12 @@ 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] - assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}" + if len(upscalers) == 0: + upscaler = shared.sd_upscalers[0] + print(f"could not find upscaler named {upscaler_name or ''}, 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: @@ -349,6 +352,7 @@ class FilenameGenerator: 'prompt_no_styles': lambda self: self.prompt_no_style(), 'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False), 'prompt_words': lambda self: self.prompt_words(), + 'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"], } default_time_format = '%Y%m%d%H%M%S' diff --git a/modules/img2img.py b/modules/img2img.py index c973b770..d22d9a49 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -151,13 +151,14 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s override_settings=override_settings, ) - p.scripts = modules.scripts.scripts_txt2img + p.scripts = modules.scripts.scripts_img2img p.script_args = args if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) - p.extra_generation_params["Mask blur"] = mask_blur + if mask: + 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" diff --git a/modules/interrogate.py b/modules/interrogate.py index cbb80683..e1665708 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir): category_types = ["artists", "flavors", "mediums", "movements"] try: - os.makedirs(tmpdir) + os.makedirs(tmpdir, exist_ok=True) for category_type in category_types: torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt")) os.rename(tmpdir, content_dir) @@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir): errors.display(e, "downloading default CLIP interrogate categories") finally: if os.path.exists(tmpdir): - os.remove(tmpdir) + os.removedirs(tmpdir) class InterrogateModels: diff --git a/modules/lowvram.py b/modules/lowvram.py index 042a0254..e254cc13 100644 --- a/modules/lowvram.py +++ b/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 four big modules, cond, first_stage, depth (if applicable), and unet from the model and then + # remove several big modules: cond, first_stage, depth/embedder (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), sd_model.model - sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None + 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 + 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.to(devices.device) - sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored + sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, 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) @@ -69,6 +69,8 @@ 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'): diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 09d8e605..4dc1a2ab 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, if extras_mode == 1: for img in image_folder: - image = Image.open(img) + if isinstance(img, Image.Image): + image = img + fn = '' + else: + image = Image.open(os.path.abspath(img.name)) + fn = os.path.splitext(img.orig_name)[0] + image_data.append(image) - image_names.append(os.path.splitext(img.orig_name)[0]) + image_names.append(fn) elif extras_mode == 2: assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled' assert input_dir, 'input directory not selected' diff --git a/modules/processing.py b/modules/processing.py index 2e5a363f..7bac154d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -3,6 +3,7 @@ import math import os import sys import warnings +import hashlib import torch import numpy as np @@ -78,22 +79,28 @@ def apply_overlay(image, paste_loc, index, overlays): def txt2img_image_conditioning(sd_model, x, width, height): - if sd_model.model.conditioning_key not in {'hybrid', 'concat'}: - # Dummy zero conditioning if we're not using inpainting model. + if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models + + # 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: """ @@ -190,6 +197,14 @@ class StableDiffusionProcessing: 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 @@ -241,6 +256,9 @@ class StableDiffusionProcessing: 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) @@ -459,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "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, + "Init image hash": getattr(p, 'init_img_hash', None), + "RNG": (opts.randn_source if opts.randn_source != "GPU" else None) } generation_params.update(p.extra_generation_params) @@ -990,6 +1010,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.color_corrections = [] imgs = [] for img in self.init_images: + + # Save init image + if opts.save_init_img: + self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest() + images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False) + image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: diff --git a/modules/safe.py b/modules/safe.py index 82d44be3..dadf319c 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -1,6 +1,5 @@ # this code is adapted from the script contributed by anon from /h/ -import io import pickle import collections import sys @@ -12,11 +11,9 @@ import _codecs import zipfile import re - # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage - def encode(*args): out = _codecs.encode(*args) return out @@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id): assert saved_id[0] == 'storage' - return TypedStorage() + return TypedStorage(_internal=True) def find_class(self, module, name): if self.extra_handler is not None: diff --git a/modules/scripts.py b/modules/scripts.py index d661be4f..4d0bbd66 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -553,3 +553,15 @@ 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 diff --git a/modules/sd_models.py b/modules/sd_models.py index 86218c08..6ea874df 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,7 +122,7 @@ 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 model_list: + for filename in sorted(model_list, key=str.lower): checkpoint_info = CheckpointInfo(filename) checkpoint_info.register() @@ -383,6 +383,14 @@ def repair_config(sd_config): 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' sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight' diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 91c21700..9398f528 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -14,6 +14,8 @@ 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") @@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict): 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: diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index a1aac7cf..bc074238 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -60,3 +60,13 @@ def store_latent(decoded): class InterruptedException(BaseException): pass + + +if opts.randn_source == "CPU": + import torchsde._brownian.brownian_interval + + def torchsde_randn(size, dtype, device, seed): + generator = torch.Generator(devices.cpu).manual_seed(int(seed)) + return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device) + + torchsde._brownian.brownian_interval._randn = torchsde_randn diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index 083da18c..bfcc5574 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler: # Have to unwrap the inpainting conditioning here to perform pre-processing image_conditioning = None + uc_image_conditioning = None if isinstance(cond, dict): - image_conditioning = cond["c_concat"][0] + 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] @@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler: # 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]} + 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 @@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler: # 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]} + 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)) @@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler: # 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]} + 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]) diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 93f0e55a..a547d1b5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module): 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_cond]) + 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_cond] + [torch.zeros_like(self.init_latent)]) + 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) @@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module): cond_in = torch.cat([tensor, uncond, 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]}) + 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={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]}) + 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 @@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module): 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={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]}) + 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={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) + 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) @@ -183,7 +190,7 @@ class TorchHijack: if noise.shape == x.shape: return noise - if x.device.type == 'mps': + if opts.randn_source == "CPU" or x.device.type == 'mps': return torch.randn_like(x, device=devices.cpu).to(x.device) else: return torch.randn_like(x) diff --git a/modules/shared.py b/modules/shared.py index 2f7892cd..8b5f752e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -40,6 +40,7 @@ restricted_opts = { "outdir_grids", "outdir_txt2img_grids", "outdir_save", + "outdir_init_images" } ui_reorder_categories = [ @@ -269,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "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"), + "save_init_img": OptionInfo(False, "Save init images when using img2img"), "temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"), "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"), @@ -284,6 +286,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs), "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs), "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs), + "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs), })) options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), { @@ -299,6 +302,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { "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()}), "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), + "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}), + "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}), })) options_templates.update(options_section(('face-restoration', "Face restoration"), { @@ -347,6 +352,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "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"), + "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}), })) options_templates.update(options_section(('compatibility', "Compatibility"), { @@ -377,7 +383,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), { "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), + "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), })) options_templates.update(options_section(('ui', "User interface"), { @@ -398,6 +404,7 @@ options_templates.update(options_section(('ui', "User interface"), { "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"), "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 ", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}), + "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), "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"), @@ -439,7 +446,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), { })) options_templates.update(options_section((None, "Hidden options"), { - "disabled_extensions": OptionInfo([], "Disable those extensions"), + "disabled_extensions": OptionInfo([], "Disable these 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"), })) @@ -675,7 +683,7 @@ mem_mon.start() def listfiles(dirname): - filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")] + filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")] return [file for file in filenames if os.path.isfile(file)] diff --git a/modules/ui.py b/modules/ui.py index 6e049881..dab94b93 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -70,17 +70,6 @@ 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' # 🎲️ @@ -182,8 +171,8 @@ def create_seed_inputs(target_interface): with FormRow(elem_id=target_interface + '_seed_row', variant="compact"): 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') - reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed') + random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed') + reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed') seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False) @@ -479,7 +468,7 @@ def create_ui(): 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") + res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims") if opts.dimensions_and_batch_together: with gr.Column(elem_id="txt2img_column_batch"): @@ -1215,7 +1204,7 @@ def create_ui(): with gr.Column(elem_id='ti_gallery_container'): ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) - ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) + ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4) ti_progress = gr.HTML(elem_id="ti_progress", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="") @@ -1566,22 +1555,6 @@ 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(data_path, "user.css")): - with open(os.path.join(data_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")] @@ -1592,7 +1565,7 @@ def create_ui(): for _interface, label, _ifid in interfaces: shared.tab_names.append(label) - with gr.Blocks(css=css, theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo: + with gr.Blocks(theme=shared.gradio_theme, 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) @@ -1655,6 +1628,7 @@ def create_ui(): fn=get_settings_values, inputs=[], outputs=[component_dict[k] for k in component_keys], + queue=False, ) def modelmerger(*args): @@ -1731,7 +1705,7 @@ def create_ui(): if init_field is not None: init_field(saved_value) - if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible: + if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible: apply_field(x, 'visible') if type(x) == gr.Slider: @@ -1777,25 +1751,60 @@ def create_ui(): return demo -def reload_javascript(): +def webpath(fn): + if fn.startswith(script_path): + 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'\n' + head = f'\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'\n' + head += f'\n' for script in modules.scripts.list_scripts("javascript", ".mjs"): - head += f'\n' + head += f'\n' head += f'\n' + return head + + +def css_html(): + head = "" + + def stylesheet(fn): + return f'' + + 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'', f'{head}'.encode("utf8")) + res.body = res.body.replace(b'', f'{js}'.encode("utf8")) + res.body = res.body.replace(b'', f'{css}'.encode("utf8")) res.init_headers() return res diff --git a/modules/ui_common.py b/modules/ui_common.py index 0f3427c8..27ab3ebb 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -125,7 +125,7 @@ Requested path was: {f} with gr.Column(variant='panel', elem_id=f"{tabname}_results"): with gr.Group(elem_id=f"{tabname}_gallery_container"): - result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) + result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4) generation_info = None with gr.Column(): @@ -145,8 +145,7 @@ Requested path was: {f} ) if tabname != "extras": - with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}') + 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") diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index da7e79f0..b402bc8b 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -21,7 +21,7 @@ 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): +def apply_and_restart(disable_list, update_list, disable_all): check_access() disabled = json.loads(disable_list) @@ -43,6 +43,7 @@ def apply_and_restart(disable_list, update_list): 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() @@ -63,6 +64,9 @@ def check_updates(id_task, disable_list): 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) @@ -87,6 +91,8 @@ def extension_table(): """ for ext in extensions.extensions: + ext.read_info_from_repo() + remote = f"""{html.escape("built-in" if ext.is_builtin else ext.remote or '')}""" if ext.can_update: @@ -94,9 +100,13 @@ def extension_table(): 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""" - + {html.escape(ext.name)} {remote} {ext.version} {ext_status} @@ -119,7 +129,7 @@ def normalize_git_url(url): return url -def install_extension_from_url(dirname, url): +def install_extension_from_url(dirname, branch_name, url): check_access() assert url, 'No URL specified' @@ -140,10 +150,17 @@ def install_extension_from_url(dirname, url): try: shutil.rmtree(tmpdir, True) - with git.Repo.clone_from(url, tmpdir) as repo: - repo.remote().fetch() - for submodule in repo.submodules: - submodule.update() + if branch_name == '': + # if no branch is specified, use the default branch + with git.Repo.clone_from(url, tmpdir) as repo: + repo.remote().fetch() + for submodule in repo.submodules: + submodule.update() + else: + with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo: + repo.remote().fetch() + for submodule in repo.submodules: + submodule.update() try: os.rename(tmpdir, target_dir) except OSError as err: @@ -289,16 +306,24 @@ def create_ui(): with gr.Row(elem_id="extensions_installed_top"): 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) - info = gr.HTML() + html = "" + if shared.opts.disable_all_extensions != "none": + html = """ + + "Disable all extensions" was set, change it to "none" to load all extensions again + + """ + 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], + inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all], outputs=[], ) @@ -358,13 +383,14 @@ def create_ui(): with gr.TabItem("Install from URL"): install_url = gr.Text(label="URL for extension's git repository") + install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch") install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto") install_button = gr.Button(value="Install", variant="primary") install_result = gr.HTML(elem_id="extension_install_result") install_button.click( fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]), - inputs=[install_dirname, install_url], + inputs=[install_dirname, install_branch, install_url], outputs=[extensions_table, install_result], ) diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index daea03d6..25eb464b 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -2,8 +2,10 @@ 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 @@ -252,10 +254,10 @@ def create_ui(container, button, tabname): def toggle_visibility(is_visible): is_visible = not is_visible - return is_visible, gr.update(visible=is_visible) + 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.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button]) def refresh(): res = [] @@ -290,6 +292,7 @@ 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: @@ -299,7 +302,12 @@ def setup_ui(ui, gallery): assert is_allowed, f'writing to {filename} is not allowed' - image.save(filename) + if geninfo: + 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] diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index b418d955..d278e1b6 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -13,7 +13,7 @@ def create_ui(): extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image") with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch: - image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch") + image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch") with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir: extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir") diff --git a/requirements.txt b/requirements.txt index c72b2927..9091f612 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,11 @@ +astunparse blendmodes accelerate basicsr fonts font-roboto gfpgan -gradio==3.23 +gradio==3.27 invisible-watermark numpy omegaconf diff --git a/requirements_versions.txt b/requirements_versions.txt index df65431a..94d32d3d 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -1,10 +1,10 @@ blendmodes==2022 transformers==4.25.1 -accelerate==0.12.0 +accelerate==0.18.0 basicsr==1.4.2 gfpgan==1.3.8 -gradio==3.23 -numpy==1.23.3 +gradio==3.27 +numpy==1.23.5 Pillow==9.4.0 realesrgan==0.3.0 torch @@ -25,6 +25,6 @@ lark==1.1.2 inflection==0.5.1 GitPython==3.1.30 torchsde==0.2.5 -safetensors==0.3.0 +safetensors==0.3.1 httpcore<=0.15 fastapi==0.94.0 diff --git a/scripts/custom_code.py b/scripts/custom_code.py index d29113e6..4071d86d 100644 --- a/scripts/custom_code.py +++ b/scripts/custom_code.py @@ -1,9 +1,40 @@ import modules.scripts as scripts import gradio as gr +import ast +import copy from modules.processing import Processed from modules.shared import opts, cmd_opts, state + +def convertExpr2Expression(expr): + expr.lineno = 0 + expr.col_offset = 0 + result = ast.Expression(expr.value, lineno=0, col_offset = 0) + + return result + + +def exec_with_return(code, module): + """ + like exec() but can return values + https://stackoverflow.com/a/52361938/5862977 + """ + code_ast = ast.parse(code) + + init_ast = copy.deepcopy(code_ast) + init_ast.body = code_ast.body[:-1] + + last_ast = copy.deepcopy(code_ast) + last_ast.body = code_ast.body[-1:] + + exec(compile(init_ast, "", "exec"), module.__dict__) + if type(last_ast.body[0]) == ast.Expr: + return eval(compile(convertExpr2Expression(last_ast.body[0]), "", "eval"), module.__dict__) + else: + exec(compile(last_ast, "", "exec"), module.__dict__) + + class Script(scripts.Script): def title(self): @@ -13,12 +44,23 @@ class Script(scripts.Script): return cmd_opts.allow_code def ui(self, is_img2img): - code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code")) + example = """from modules.processing import process_images - return [code] +p.width = 768 +p.height = 768 +p.batch_size = 2 +p.steps = 10 + +return process_images(p) +""" - def run(self, p, code): + code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code")) + indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level")) + + return [code, indent_level] + + def run(self, p, code, indent_level): assert cmd_opts.allow_code, '--allow-code option must be enabled' display_result_data = [[], -1, ""] @@ -29,13 +71,20 @@ class Script(scripts.Script): display_result_data[2] = i from types import ModuleType - compiled = compile(code, '', 'exec') module = ModuleType("testmodule") module.__dict__.update(globals()) module.p = p module.display = display - exec(compiled, module.__dict__) + + indent = " " * indent_level + indented = code.replace('\n', '\n' + indent) + body = f"""def __webuitemp__(): +{indent}{indented} +__webuitemp__()""" + + result = exec_with_return(body, module) + + if isinstance(result, Processed): + return result return Processed(p, *display_result_data) - - \ No newline at end of file diff --git a/scripts/loopback.py b/scripts/loopback.py index 9c388aa8..d3065fe6 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -54,15 +54,12 @@ class Script(scripts.Script): return strength progress = loop / (loops - 1) - match denoising_curve: - case "Aggressive": - strength = math.sin((progress) * math.pi * 0.5) - - case "Lazy": - strength = 1 - math.cos((progress) * math.pi * 0.5) - - case _: - strength = progress + 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 diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index 11eab31a..ef1186ac 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -4,8 +4,8 @@ import numpy as np from modules import scripts_postprocessing, shared import gradio as gr -from modules.ui_components import FormRow - +from modules.ui_components import FormRow, ToolButton +from modules.ui import switch_values_symbol upscale_cache = {} @@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to: with FormRow(): - 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_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop") + with gr.Column(elem_id="upscaling_column_size", scale=4): + upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w") + upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h") + with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"): + upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn") + upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop") 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) @@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): 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") + upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False) tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab]) tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab]) diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 3895a795..52ae1c6e 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -374,16 +374,19 @@ 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")) + x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True) fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_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")) + y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True) fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_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")) + z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True) 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"): @@ -401,54 +404,74 @@ class Script(scripts.Script): 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): - return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values + def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown): + return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown - xy_swap_args = [x_type, x_values, y_type, y_values] + xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown] 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] + yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown] 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] + xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown] 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] - return ", ".join(axis.choices()) if axis.choices else gr.update() + return axis.choices() if axis.choices else gr.update() - 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]) + fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown]) + fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown]) + fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown]) - def select_axis(x_type): - return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None) + def select_axis(axis_type,axis_values_dropdown): + choices = self.current_axis_options[axis_type].choices + has_choices = choices is not None + current_values = axis_values_dropdown + if has_choices: + choices = choices() + if isinstance(current_values,str): + current_values = current_values.split(",") + current_values = list(filter(lambda x: x in choices, current_values)) + return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values) - 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]) + x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown]) + y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown]) + z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown]) + + def get_dropdown_update_from_params(axis,params): + val_key = axis + " Values" + vals = params.get(val_key,"") + valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] + return gr.update(value = valslist) self.infotext_fields = ( (x_type, "X Type"), (x_values, "X Values"), + (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)), (y_type, "Y Type"), (y_values, "Y Values"), + (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)), (z_type, "Z Type"), (z_values, "Z Values"), + (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)), ) - 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] + return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, 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, 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, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size): if not no_fixed_seeds: modules.processing.fix_seed(p) if not opts.return_grid: p.batch_size = 1 - def process_axis(opt, vals): + def process_axis(opt, vals, vals_dropdown): if opt.label == 'Nothing': return [0] - valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] + if opt.choices is not None: + valslist = vals_dropdown + else: + valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x] if opt.type == int: valslist_ext = [] @@ -506,13 +529,19 @@ class Script(scripts.Script): return valslist x_opt = self.current_axis_options[x_type] - xs = process_axis(x_opt, x_values) + if x_opt.choices is not None: + x_values = ",".join(x_values_dropdown) + xs = process_axis(x_opt, x_values, x_values_dropdown) y_opt = self.current_axis_options[y_type] - ys = process_axis(y_opt, y_values) + if y_opt.choices is not None: + y_values = ",".join(y_values_dropdown) + ys = process_axis(y_opt, y_values, y_values_dropdown) z_opt = self.current_axis_options[z_type] - zs = process_axis(z_opt, z_values) + if z_opt.choices is not None: + z_values = ",".join(z_values_dropdown) + zs = process_axis(z_opt, z_values, z_values_dropdown) # 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 diff --git a/style.css b/style.css index 0dcc3e25..aafc2362 100644 --- a/style.css +++ b/style.css @@ -7,7 +7,7 @@ --block-background-fill: transparent; } -.block.padded{ +.block.padded:not(.gradio-accordion) { padding: 0 !important; } @@ -54,10 +54,6 @@ div.compact{ gap: 1em; } -.gradio-dropdown ul.options{ - z-index: 3000; -} - .gradio-dropdown label span:not(.has-info), .gradio-textbox label span:not(.has-info), .gradio-number label span:not(.has-info) @@ -65,11 +61,30 @@ div.compact{ margin-bottom: 0; } +.gradio-dropdown ul.options{ + z-index: 3000; + min-width: fit-content; + max-width: inherit; + white-space: nowrap; +} + +.gradio-dropdown ul.options li.item { + padding: 0.05em 0; +} + +.gradio-dropdown ul.options li.item.selected { + background-color: var(--neutral-100); +} + +.dark .gradio-dropdown ul.options li.item.selected { + background-color: var(--neutral-900); +} + .gradio-dropdown div.wrap.wrap.wrap.wrap{ box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05); } -.gradio-dropdown .wrap-inner.wrap-inner.wrap-inner{ +.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{ flex-wrap: unset; } @@ -123,6 +138,18 @@ div.gradio-html.min{ border-radius: 0.5em; } +.gradio-button.secondary-down{ + background: var(--button-secondary-background-fill); + color: var(--button-secondary-text-color); +} +.gradio-button.secondary-down, .gradio-button.secondary-down:hover{ + box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset; +} +.gradio-button.secondary-down:hover{ + background: var(--button-secondary-background-fill-hover); + color: var(--button-secondary-text-color-hover); +} + .checkboxes-row{ margin-bottom: 0.5em; margin-left: 0em; @@ -285,12 +312,23 @@ div.dimensions-tools{ align-content: center; } +div#extras_scale_to_tab div.form{ + flex-direction: row; +} + #mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{ height: 480px !important; max-height: 480px !important; min-height: 480px !important; } +#img2img_sketch, #img2maskimg, #inpaint_sketch { + overflow: overlay !important; + resize: auto; + background: var(--panel-background-fill); + z-index: 5; +} + .image-buttons button{ min-width: auto; } @@ -302,6 +340,7 @@ div.dimensions-tools{ /* settings */ #quicksettings { width: fit-content; + align-items: end; } #quicksettings > div, #quicksettings > fieldset{ @@ -507,6 +546,17 @@ div.dimensions-tools{ background-color: rgba(0, 0, 0, 0.8); } +#imageARPreview { + position: absolute; + top: 0px; + left: 0px; + border: 2px solid red; + background: rgba(255, 0, 0, 0.3); + z-index: 900; + pointer-events: none; + display: none; +} + /* context menu (ie for the generate button) */ #context-menu{ diff --git a/webui-macos-env.sh b/webui-macos-env.sh index 37cac4fb..65d80413 100644 --- a/webui-macos-env.sh +++ b/webui-macos-env.sh @@ -11,7 +11,7 @@ fi export install_dir="$HOME" export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate" -export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1" +export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118" export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git" export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" export PYTORCH_ENABLE_MPS_FALLBACK=1 diff --git a/webui.py b/webui.py index 6986e576..95623c6f 100644 --- a/webui.py +++ b/webui.py @@ -20,6 +20,9 @@ startup_timer = timer.Timer() import torch import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning") +warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision") + + startup_timer.record("import torch") import gradio @@ -67,11 +70,51 @@ else: server_name = "0.0.0.0" if cmd_opts.listen else None +def fix_asyncio_event_loop_policy(): + """ + The default `asyncio` event loop policy only automatically creates + event loops in the main threads. Other threads must create event + loops explicitly or `asyncio.get_event_loop` (and therefore + `.IOLoop.current`) will fail. Installing this policy allows event + loops to be created automatically on any thread, matching the + behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2). + """ + + import asyncio + + if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"): + # "Any thread" and "selector" should be orthogonal, but there's not a clean + # interface for composing policies so pick the right base. + _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore + else: + _BasePolicy = asyncio.DefaultEventLoopPolicy + + class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore + """Event loop policy that allows loop creation on any thread. + Usage:: + + asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy()) + """ + + def get_event_loop(self) -> asyncio.AbstractEventLoop: + try: + return super().get_event_loop() + except (RuntimeError, AssertionError): + # This was an AssertionError in python 3.4.2 (which ships with debian jessie) + # and changed to a RuntimeError in 3.4.3. + # "There is no current event loop in thread %r" + loop = self.new_event_loop() + self.set_event_loop(loop) + return loop + + asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy()) + + def check_versions(): if shared.cmd_opts.skip_version_check: return - expected_torch_version = "1.13.1" + expected_torch_version = "2.0.0" if version.parse(torch.__version__) < version.parse(expected_torch_version): errors.print_error_explanation(f""" @@ -84,7 +127,7 @@ there are reports of issues with training tab on the latest version. Use --skip-version-check commandline argument to disable this check. """.strip()) - expected_xformers_version = "0.0.16rc425" + expected_xformers_version = "0.0.17" if shared.xformers_available: import xformers @@ -99,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check. def initialize(): + fix_asyncio_event_loop_policy() + check_versions() extensions.list_extensions() @@ -126,9 +171,6 @@ def initialize(): modules.scripts.load_scripts() startup_timer.record("load scripts") - modelloader.load_upscalers() - startup_timer.record("load upscalers") - modules.sd_vae.refresh_vae_list() startup_timer.record("refresh VAE") @@ -266,9 +308,6 @@ def webui(): inbrowser=cmd_opts.autolaunch, prevent_thread_lock=True ) - for dep in shared.demo.dependencies: - dep['show_progress'] = False # disable gradio css animation on component update - # after initial launch, disable --autolaunch for subsequent restarts cmd_opts.autolaunch = False