Merge branch 'dev' into master
This commit is contained in:
AUTOMATIC1111
GitHub
commit
1185bf3981
2
.gitignore
vendored
2
.gitignore
vendored
@ -32,4 +32,4 @@ notification.mp3
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/extensions
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/test/stdout.txt
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/test/stderr.txt
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/cache.json
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/cache.json*
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@ -120,6 +120,7 @@ sudo pacman -S wget git python3
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bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
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```
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3. Run `webui.sh`.
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4. Check `webui-user.sh` for options.
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### Installation on Apple Silicon
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Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
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@ -4,8 +4,8 @@ channels:
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- defaults
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dependencies:
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- python=3.10
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- pip=22.2.2
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- cudatoolkit=11.3
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- pytorch=1.12.1
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- torchvision=0.13.1
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- numpy=1.23.1
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- pip=23.0
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- cudatoolkit=11.8
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- pytorch=2.0
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- torchvision=0.15
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- numpy=1.23
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@ -25,22 +25,28 @@ class UpscalerLDSR(Upscaler):
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yaml_path = os.path.join(self.model_path, "project.yaml")
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old_model_path = os.path.join(self.model_path, "model.pth")
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new_model_path = os.path.join(self.model_path, "model.ckpt")
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safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
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local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
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local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
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local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
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local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
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if os.path.exists(yaml_path):
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statinfo = os.stat(yaml_path)
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if statinfo.st_size >= 10485760:
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print("Removing invalid LDSR YAML file.")
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os.remove(yaml_path)
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if os.path.exists(old_model_path):
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print("Renaming model from model.pth to model.ckpt")
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os.rename(old_model_path, new_model_path)
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if os.path.exists(safetensors_model_path):
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model = safetensors_model_path
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if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
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model = local_safetensors_path
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else:
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model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
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file_name="model.ckpt", progress=True)
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yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
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file_name="project.yaml", progress=True)
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model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
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yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
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try:
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return LDSR(model, yaml)
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@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
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def activate(self, p, params_list):
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additional = shared.opts.sd_lora
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if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
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if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui)
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shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
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"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),
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"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),
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}))
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@ -5,11 +5,15 @@ import traceback
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import PIL.Image
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import numpy as np
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import torch
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from tqdm import tqdm
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from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import devices, modelloader
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from scunet_model_arch import SCUNet as net
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from modules.shared import opts
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from modules import images
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class UpscalerScuNET(modules.upscaler.Upscaler):
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@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
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scalers.append(scaler_data2)
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self.scalers = scalers
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def do_upscale(self, img: PIL.Image, selected_file):
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@staticmethod
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@torch.no_grad()
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def tiled_inference(img, model):
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# test the image tile by tile
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h, w = img.shape[2:]
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tile = opts.SCUNET_tile
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tile_overlap = opts.SCUNET_tile_overlap
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if tile == 0:
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return model(img)
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device = devices.get_device_for('scunet')
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assert tile % 8 == 0, "tile size should be a multiple of window_size"
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sf = 1
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stride = tile - tile_overlap
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h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
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w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
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E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
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W = torch.zeros_like(E, dtype=devices.dtype, device=device)
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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pbar.update(1)
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output = E.div_(W)
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return output
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def do_upscale(self, img: PIL.Image.Image, selected_file):
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torch.cuda.empty_cache()
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model = self.load_model(selected_file)
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if model is None:
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print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
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return img
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device = devices.get_device_for('scunet')
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device)
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tile = opts.SCUNET_tile
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h, w = img.height, img.width
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np_img = np.array(img)
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np_img = np_img[:, :, ::-1] # RGB to BGR
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np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
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torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = 255. * np.moveaxis(output, 0, 2)
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output = output.astype(np.uint8)
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output = output[:, :, ::-1]
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if tile > h or tile > w:
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_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
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_img[:, :, :h, :w] = torch_img # pad image
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torch_img = _img
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torch_output = self.tiled_inference(torch_img, model).squeeze(0)
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torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
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np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
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del torch_img, torch_output
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torch.cuda.empty_cache()
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return PIL.Image.fromarray(output, 'RGB')
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output = np_output.transpose((1, 2, 0)) # CHW to HWC
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output = output[:, :, ::-1] # BGR to RGB
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return PIL.Image.fromarray((output * 255).astype(np.uint8))
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def load_model(self, path: str):
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device = devices.get_device_for('scunet')
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@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
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model = model.to(device)
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return model
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@ -1,103 +1,42 @@
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// Stable Diffusion WebUI - Bracket checker
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// Version 1.0
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// By Hingashi no Florin/Bwin4L
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// By Hingashi no Florin/Bwin4L & @akx
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// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
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// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
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function checkBrackets(evt, textArea, counterElt) {
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errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n';
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errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
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errorStringCurly = '{...} - Different number of opening and closing curly brackets detected.\n';
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function checkBrackets(textArea, counterElt) {
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var counts = {};
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(textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
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counts[bracket] = (counts[bracket] || 0) + 1;
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});
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var errors = [];
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openBracketRegExp = /\(/g;
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closeBracketRegExp = /\)/g;
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openSquareBracketRegExp = /\[/g;
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closeSquareBracketRegExp = /\]/g;
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openCurlyBracketRegExp = /\{/g;
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closeCurlyBracketRegExp = /\}/g;
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totalOpenBracketMatches = 0;
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totalCloseBracketMatches = 0;
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totalOpenSquareBracketMatches = 0;
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totalCloseSquareBracketMatches = 0;
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totalOpenCurlyBracketMatches = 0;
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totalCloseCurlyBracketMatches = 0;
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openBracketMatches = textArea.value.match(openBracketRegExp);
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if(openBracketMatches) {
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totalOpenBracketMatches = openBracketMatches.length;
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}
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closeBracketMatches = textArea.value.match(closeBracketRegExp);
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if(closeBracketMatches) {
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totalCloseBracketMatches = closeBracketMatches.length;
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}
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openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
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if(openSquareBracketMatches) {
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totalOpenSquareBracketMatches = openSquareBracketMatches.length;
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}
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closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
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if(closeSquareBracketMatches) {
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totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
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}
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openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
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if(openCurlyBracketMatches) {
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totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
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}
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closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
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if(closeCurlyBracketMatches) {
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totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
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}
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|
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if(totalOpenBracketMatches != totalCloseBracketMatches) {
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if(!counterElt.title.includes(errorStringParen)) {
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counterElt.title += errorStringParen;
|
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function checkPair(open, close, kind) {
|
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if (counts[open] !== counts[close]) {
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errors.push(
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`${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
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||||
);
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||||
}
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} else {
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counterElt.title = counterElt.title.replace(errorStringParen, '');
|
||||
}
|
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|
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if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
|
||||
if(!counterElt.title.includes(errorStringSquare)) {
|
||||
counterElt.title += errorStringSquare;
|
||||
}
|
||||
} else {
|
||||
counterElt.title = counterElt.title.replace(errorStringSquare, '');
|
||||
}
|
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checkPair('(', ')', 'round brackets');
|
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checkPair('[', ']', 'square brackets');
|
||||
checkPair('{', '}', 'curly brackets');
|
||||
counterElt.title = errors.join('\n');
|
||||
counterElt.classList.toggle('error', errors.length !== 0);
|
||||
}
|
||||
|
||||
if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
|
||||
if(!counterElt.title.includes(errorStringCurly)) {
|
||||
counterElt.title += errorStringCurly;
|
||||
}
|
||||
} else {
|
||||
counterElt.title = counterElt.title.replace(errorStringCurly, '');
|
||||
}
|
||||
function setupBracketChecking(id_prompt, id_counter) {
|
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var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
|
||||
var counter = gradioApp().getElementById(id_counter)
|
||||
|
||||
if(counterElt.title != '') {
|
||||
counterElt.classList.add('error');
|
||||
} else {
|
||||
counterElt.classList.remove('error');
|
||||
if (textarea && counter) {
|
||||
textarea.addEventListener("input", () => checkBrackets(textarea, counter));
|
||||
}
|
||||
}
|
||||
|
||||
function setupBracketChecking(id_prompt, id_counter){
|
||||
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
|
||||
var counter = gradioApp().getElementById(id_counter)
|
||||
|
||||
textarea.addEventListener("input", function(evt){
|
||||
checkBrackets(evt, textarea, counter)
|
||||
});
|
||||
}
|
||||
|
||||
onUiLoaded(function(){
|
||||
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
|
||||
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
|
||||
setupBracketChecking('img2img_prompt', 'img2img_token_counter')
|
||||
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
|
||||
})
|
||||
onUiLoaded(function () {
|
||||
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
|
||||
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
|
||||
setupBracketChecking('img2img_prompt', 'img2img_token_counter');
|
||||
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
|
||||
});
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -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);
|
||||
});
|
||||
});
|
||||
|
||||
@ -16,9 +16,9 @@ onUiUpdate(function(){
|
||||
|
||||
let modalObserver = new MutationObserver(function(mutations) {
|
||||
mutations.forEach(function(mutationRecord) {
|
||||
let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
|
||||
if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
|
||||
gradioApp().getElementById(selectedTab+"_generation_info_button").click()
|
||||
let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
|
||||
if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
|
||||
gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
@ -65,8 +65,8 @@ titles = {
|
||||
|
||||
"Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
|
||||
|
||||
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
|
||||
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
|
||||
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
|
||||
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
|
||||
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
|
||||
|
||||
"Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
|
||||
@ -85,7 +85,6 @@ titles = {
|
||||
"vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
|
||||
|
||||
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
|
||||
"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
|
||||
|
||||
"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
|
||||
"Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
|
||||
@ -111,7 +110,8 @@ titles = {
|
||||
"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
|
||||
"Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
|
||||
"Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
|
||||
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
|
||||
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
|
||||
"Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -251,8 +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);
|
||||
|
||||
|
||||
@ -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
|
||||
}
|
||||
|
||||
17
launch.py
17
launch.py
@ -19,7 +19,6 @@ python = sys.executable
|
||||
git = os.environ.get('GIT', "git")
|
||||
index_url = os.environ.get('INDEX_URL', "")
|
||||
stored_commit_hash = None
|
||||
skip_install = False
|
||||
dir_repos = "repositories"
|
||||
|
||||
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
|
||||
@ -121,12 +120,12 @@ def run_python(code, desc=None, errdesc=None):
|
||||
return run(f'"{python}" -c "{code}"', desc, errdesc)
|
||||
|
||||
|
||||
def run_pip(args, desc=None):
|
||||
if skip_install:
|
||||
def run_pip(command, desc=None, live=False):
|
||||
if args.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 {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
|
||||
|
||||
|
||||
def check_run_python(code):
|
||||
@ -223,12 +222,10 @@ 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")
|
||||
@ -271,7 +268,7 @@ 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")
|
||||
@ -296,7 +293,7 @@ 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)
|
||||
|
||||
|
||||
@ -6,7 +6,6 @@ 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
|
||||
@ -272,7 +271,9 @@ class Api:
|
||||
raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
|
||||
# always on script with no arg should always run so you don't really need to add them to the requests
|
||||
if "args" in request.alwayson_scripts[alwayson_script_name]:
|
||||
script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
|
||||
# min between arg length in scriptrunner and arg length in the request
|
||||
for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
|
||||
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
|
||||
return script_args
|
||||
|
||||
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
|
||||
@ -395,16 +396,11 @@ 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])
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
|
||||
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
||||
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import json
|
||||
|
||||
|
||||
import torch
|
||||
@ -71,7 +72,7 @@ def to_half(tensor, enable):
|
||||
return tensor
|
||||
|
||||
|
||||
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
|
||||
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
|
||||
shared.state.begin()
|
||||
shared.state.job = 'model-merge'
|
||||
|
||||
@ -241,13 +242,54 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
||||
shared.state.textinfo = "Saving"
|
||||
print(f"Saving to {output_modelname}...")
|
||||
|
||||
metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
|
||||
|
||||
if save_metadata:
|
||||
merge_recipe = {
|
||||
"type": "webui", # indicate this model was merged with webui's built-in merger
|
||||
"primary_model_hash": primary_model_info.sha256,
|
||||
"secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
|
||||
"tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
|
||||
"interp_method": interp_method,
|
||||
"multiplier": multiplier,
|
||||
"save_as_half": save_as_half,
|
||||
"custom_name": custom_name,
|
||||
"config_source": config_source,
|
||||
"bake_in_vae": bake_in_vae,
|
||||
"discard_weights": discard_weights,
|
||||
"is_inpainting": result_is_inpainting_model,
|
||||
"is_instruct_pix2pix": result_is_instruct_pix2pix_model
|
||||
}
|
||||
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
|
||||
|
||||
def add_model_metadata(checkpoint_info):
|
||||
checkpoint_info.calculate_shorthash()
|
||||
metadata["sd_merge_models"][checkpoint_info.sha256] = {
|
||||
"name": checkpoint_info.name,
|
||||
"legacy_hash": checkpoint_info.hash,
|
||||
"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
|
||||
}
|
||||
|
||||
metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
|
||||
|
||||
add_model_metadata(primary_model_info)
|
||||
if secondary_model_info:
|
||||
add_model_metadata(secondary_model_info)
|
||||
if tertiary_model_info:
|
||||
add_model_metadata(tertiary_model_info)
|
||||
|
||||
metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
|
||||
|
||||
_, extension = os.path.splitext(output_modelname)
|
||||
if extension.lower() == ".safetensors":
|
||||
safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
|
||||
safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
|
||||
else:
|
||||
torch.save(theta_0, output_modelname)
|
||||
|
||||
sd_models.list_models()
|
||||
created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
|
||||
if created_model:
|
||||
created_model.calculate_shorthash()
|
||||
|
||||
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
|
||||
|
||||
|
||||
@ -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 +308,8 @@ 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'),
|
||||
('NGMS', 's_min_uncond'),
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -318,6 +318,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
|
||||
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
|
||||
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
|
||||
max_filename_part_length = 128
|
||||
NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
|
||||
|
||||
|
||||
def sanitize_filename_part(text, replace_spaces=True):
|
||||
@ -352,6 +353,10 @@ 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(),
|
||||
'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
|
||||
'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
|
||||
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
|
||||
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
|
||||
}
|
||||
default_time_format = '%Y%m%d%H%M%S'
|
||||
|
||||
@ -360,6 +365,22 @@ class FilenameGenerator:
|
||||
self.seed = seed
|
||||
self.prompt = prompt
|
||||
self.image = image
|
||||
|
||||
def hasprompt(self, *args):
|
||||
lower = self.prompt.lower()
|
||||
if self.p is None or self.prompt is None:
|
||||
return None
|
||||
outres = ""
|
||||
for arg in args:
|
||||
if arg != "":
|
||||
division = arg.split("|")
|
||||
expected = division[0].lower()
|
||||
default = division[1] if len(division) > 1 else ""
|
||||
if lower.find(expected) >= 0:
|
||||
outres = f'{outres}{expected}'
|
||||
else:
|
||||
outres = outres if default == "" else f'{outres}{default}'
|
||||
return sanitize_filename_part(outres)
|
||||
|
||||
def prompt_no_style(self):
|
||||
if self.p is None or self.prompt is None:
|
||||
@ -403,9 +424,9 @@ class FilenameGenerator:
|
||||
|
||||
for m in re_pattern.finditer(x):
|
||||
text, pattern = m.groups()
|
||||
res += text
|
||||
|
||||
if pattern is None:
|
||||
res += text
|
||||
continue
|
||||
|
||||
pattern_args = []
|
||||
@ -426,11 +447,13 @@ class FilenameGenerator:
|
||||
print(f"Error adding [{pattern}] to filename", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
if replacement is not None:
|
||||
res += str(replacement)
|
||||
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
|
||||
continue
|
||||
elif replacement is not None:
|
||||
res += text + str(replacement)
|
||||
continue
|
||||
|
||||
res += f'[{pattern}]'
|
||||
res += f'{text}[{pattern}]'
|
||||
|
||||
return res
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ import sys
|
||||
import traceback
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
|
||||
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
|
||||
|
||||
from modules import devices, sd_samplers
|
||||
from modules.generation_parameters_copypaste import create_override_settings_dict
|
||||
@ -46,7 +46,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
img = Image.open(image)
|
||||
try:
|
||||
img = Image.open(image)
|
||||
except UnidentifiedImageError:
|
||||
continue
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
img = ImageOps.exif_transpose(img)
|
||||
p.init_images = [img] * p.batch_size
|
||||
@ -151,7 +154,7 @@ 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:
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -13,6 +13,18 @@ def connect(token, port, region):
|
||||
config = conf.PyngrokConfig(
|
||||
auth_token=token, region=region
|
||||
)
|
||||
|
||||
# Guard for existing tunnels
|
||||
existing = ngrok.get_tunnels(pyngrok_config=config)
|
||||
if existing:
|
||||
for established in existing:
|
||||
# Extra configuration in the case that the user is also using ngrok for other tunnels
|
||||
if established.config['addr'][-4:] == str(port):
|
||||
public_url = existing[0].public_url
|
||||
print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
|
||||
'You can use this link after the launch is complete.')
|
||||
return
|
||||
|
||||
try:
|
||||
if account is None:
|
||||
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
|
||||
|
||||
@ -18,9 +18,14 @@ 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'
|
||||
|
||||
@ -3,6 +3,7 @@ import math
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
import hashlib
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
@ -105,7 +106,7 @@ class StableDiffusionProcessing:
|
||||
"""
|
||||
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
|
||||
"""
|
||||
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
|
||||
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
|
||||
if sampler_index is not None:
|
||||
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
|
||||
|
||||
@ -140,6 +141,7 @@ class StableDiffusionProcessing:
|
||||
self.denoising_strength: float = denoising_strength
|
||||
self.sampler_noise_scheduler_override = None
|
||||
self.ddim_discretize = ddim_discretize or opts.ddim_discretize
|
||||
self.s_min_uncond = s_min_uncond or opts.s_min_uncond
|
||||
self.s_churn = s_churn or opts.s_churn
|
||||
self.s_tmin = s_tmin or opts.s_tmin
|
||||
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
|
||||
@ -162,6 +164,8 @@ class StableDiffusionProcessing:
|
||||
self.all_seeds = None
|
||||
self.all_subseeds = None
|
||||
self.iteration = 0
|
||||
self.is_hr_pass = False
|
||||
|
||||
|
||||
@property
|
||||
def sd_model(self):
|
||||
@ -476,6 +480,9 @@ 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,
|
||||
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
@ -639,8 +646,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
file.write(processed.infotext(p, 0))
|
||||
|
||||
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
|
||||
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
|
||||
step_multiplier = 1
|
||||
if not shared.opts.dont_fix_second_order_samplers_schedule:
|
||||
try:
|
||||
step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
|
||||
except:
|
||||
pass
|
||||
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
|
||||
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
for comment in model_hijack.comments:
|
||||
@ -670,6 +683,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
|
||||
|
||||
for i, x_sample in enumerate(x_samples_ddim):
|
||||
p.batch_index = i
|
||||
|
||||
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
|
||||
@ -706,9 +721,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
image.info["parameters"] = text
|
||||
output_images.append(image)
|
||||
|
||||
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
|
||||
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
|
||||
image_mask = p.mask_for_overlay.convert('RGB')
|
||||
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
|
||||
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
|
||||
|
||||
if opts.save_mask:
|
||||
images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
|
||||
@ -718,7 +733,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
if opts.return_mask:
|
||||
output_images.append(image_mask)
|
||||
|
||||
|
||||
if opts.return_mask_composite:
|
||||
output_images.append(image_mask_composite)
|
||||
|
||||
@ -871,6 +886,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
if not self.enable_hr:
|
||||
return samples
|
||||
|
||||
self.is_hr_pass = True
|
||||
|
||||
target_width = self.hr_upscale_to_x
|
||||
target_height = self.hr_upscale_to_y
|
||||
|
||||
@ -940,6 +957,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
|
||||
|
||||
self.is_hr_pass = False
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
@ -1007,6 +1026,12 @@ 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:
|
||||
|
||||
@ -9,7 +9,7 @@ from realesrgan import RealESRGANer
|
||||
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.shared import cmd_opts, opts
|
||||
|
||||
from modules import modelloader
|
||||
|
||||
class UpscalerRealESRGAN(Upscaler):
|
||||
def __init__(self, path):
|
||||
@ -23,7 +23,15 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
self.enable = True
|
||||
self.scalers = []
|
||||
scalers = self.load_models(path)
|
||||
|
||||
local_model_paths = self.find_models(ext_filter=[".pth"])
|
||||
for scaler in scalers:
|
||||
if scaler.local_data_path.startswith("http"):
|
||||
filename = modelloader.friendly_name(scaler.local_data_path)
|
||||
local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
|
||||
if local:
|
||||
scaler.local_data_path = local
|
||||
|
||||
if scaler.name in opts.realesrgan_enabled_models:
|
||||
self.scalers.append(scaler)
|
||||
|
||||
@ -64,7 +72,9 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
print(f"Unable to find model info: {path}")
|
||||
return None
|
||||
|
||||
info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
|
||||
if info.local_data_path.startswith("http"):
|
||||
info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
|
||||
|
||||
return info
|
||||
except Exception as e:
|
||||
print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -52,6 +52,15 @@ class CheckpointInfo:
|
||||
|
||||
self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
|
||||
|
||||
self.metadata = {}
|
||||
|
||||
_, ext = os.path.splitext(self.filename)
|
||||
if ext.lower() == ".safetensors":
|
||||
try:
|
||||
self.metadata = read_metadata_from_safetensors(filename)
|
||||
except Exception as e:
|
||||
errors.display(e, f"reading checkpoint metadata: {filename}")
|
||||
|
||||
def register(self):
|
||||
checkpoints_list[self.title] = self
|
||||
for id in self.ids:
|
||||
@ -544,4 +553,4 @@ def unload_model_weights(sd_model=None, info=None):
|
||||
|
||||
print(f"Unloaded weights {timer.summary()}.")
|
||||
|
||||
return sd_model
|
||||
return sd_model
|
||||
|
||||
@ -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
|
||||
|
||||
@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module):
|
||||
|
||||
return denoised
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
|
||||
if state.interrupted or state.skipped:
|
||||
raise sd_samplers_common.InterruptedException
|
||||
|
||||
@ -115,12 +115,21 @@ class CFGDenoiser(torch.nn.Module):
|
||||
sigma_in = denoiser_params.sigma
|
||||
tensor = denoiser_params.text_cond
|
||||
uncond = denoiser_params.text_uncond
|
||||
skip_uncond = False
|
||||
|
||||
if tensor.shape[1] == uncond.shape[1]:
|
||||
if not is_edit_model:
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
else:
|
||||
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
|
||||
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
|
||||
skip_uncond = True
|
||||
x_in = x_in[:-batch_size]
|
||||
sigma_in = sigma_in[:-batch_size]
|
||||
|
||||
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
|
||||
if is_edit_model:
|
||||
cond_in = torch.cat([tensor, uncond, uncond])
|
||||
elif skip_uncond:
|
||||
cond_in = tensor
|
||||
else:
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
|
||||
@ -144,7 +153,13 @@ class CFGDenoiser(torch.nn.Module):
|
||||
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
|
||||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
|
||||
if not skip_uncond:
|
||||
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_image_indexes = [x[0][0] for x in conds_list]
|
||||
if skip_uncond:
|
||||
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
|
||||
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
|
||||
|
||||
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
|
||||
cfg_denoised_callback(denoised_params)
|
||||
@ -152,20 +167,21 @@ class CFGDenoiser(torch.nn.Module):
|
||||
devices.test_for_nans(x_out, "unet")
|
||||
|
||||
if opts.live_preview_content == "Prompt":
|
||||
sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
|
||||
sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
|
||||
elif opts.live_preview_content == "Negative prompt":
|
||||
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
|
||||
|
||||
if not is_edit_model:
|
||||
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
|
||||
else:
|
||||
if is_edit_model:
|
||||
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
|
||||
elif skip_uncond:
|
||||
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
|
||||
else:
|
||||
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
|
||||
|
||||
if self.mask is not None:
|
||||
denoised = self.init_latent * self.mask + self.nmask * denoised
|
||||
|
||||
self.step += 1
|
||||
|
||||
return denoised
|
||||
|
||||
|
||||
@ -190,7 +206,7 @@ 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)
|
||||
@ -210,6 +226,7 @@ class KDiffusionSampler:
|
||||
self.eta = None
|
||||
self.config = None
|
||||
self.last_latent = None
|
||||
self.s_min_uncond = None
|
||||
|
||||
self.conditioning_key = sd_model.model.conditioning_key
|
||||
|
||||
@ -244,6 +261,7 @@ class KDiffusionSampler:
|
||||
self.model_wrap_cfg.step = 0
|
||||
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
|
||||
self.eta = p.eta if p.eta is not None else opts.eta_ancestral
|
||||
self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
|
||||
|
||||
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
|
||||
|
||||
@ -326,6 +344,7 @@ class KDiffusionSampler:
|
||||
'image_cond': image_conditioning,
|
||||
'uncond': unconditional_conditioning,
|
||||
'cond_scale': p.cfg_scale,
|
||||
's_min_uncond': self.s_min_uncond
|
||||
}
|
||||
|
||||
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
@ -359,7 +378,8 @@ class KDiffusionSampler:
|
||||
'cond': conditioning,
|
||||
'image_cond': image_conditioning,
|
||||
'uncond': unconditional_conditioning,
|
||||
'cond_scale': p.cfg_scale
|
||||
'cond_scale': p.cfg_scale,
|
||||
's_min_uncond': self.s_min_uncond
|
||||
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
||||
return samples
|
||||
|
||||
@ -4,6 +4,7 @@ import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import requests
|
||||
|
||||
from PIL import Image
|
||||
import gradio as gr
|
||||
@ -39,6 +40,7 @@ restricted_opts = {
|
||||
"outdir_grids",
|
||||
"outdir_txt2img_grids",
|
||||
"outdir_save",
|
||||
"outdir_init_images"
|
||||
}
|
||||
|
||||
ui_reorder_categories = [
|
||||
@ -54,6 +56,21 @@ ui_reorder_categories = [
|
||||
"scripts",
|
||||
]
|
||||
|
||||
# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
|
||||
gradio_hf_hub_themes = [
|
||||
"gradio/glass",
|
||||
"gradio/monochrome",
|
||||
"gradio/seafoam",
|
||||
"gradio/soft",
|
||||
"freddyaboulton/dracula_revamped",
|
||||
"gradio/dracula_test",
|
||||
"abidlabs/dracula_test",
|
||||
"abidlabs/pakistan",
|
||||
"dawood/microsoft_windows",
|
||||
"ysharma/steampunk"
|
||||
]
|
||||
|
||||
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
|
||||
|
||||
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
|
||||
@ -252,7 +269,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
|
||||
"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"),
|
||||
@ -268,6 +285,7 @@ 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"), {
|
||||
@ -283,6 +301,8 @@ 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"), {
|
||||
@ -331,6 +351,7 @@ 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"), {
|
||||
@ -338,6 +359,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
|
||||
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
|
||||
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
|
||||
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
|
||||
@ -361,7 +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"), {
|
||||
@ -382,11 +404,13 @@ 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 <extra networks:0.9>", 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"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
@ -405,6 +429,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
|
||||
@ -600,6 +625,24 @@ clip_model = None
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
|
||||
gradio_theme = gr.themes.Base()
|
||||
|
||||
|
||||
def reload_gradio_theme(theme_name=None):
|
||||
global gradio_theme
|
||||
if not theme_name:
|
||||
theme_name = opts.gradio_theme
|
||||
|
||||
if theme_name == "Default":
|
||||
gradio_theme = gr.themes.Default()
|
||||
else:
|
||||
try:
|
||||
gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
|
||||
except requests.exceptions.ConnectionError:
|
||||
print("Can't access HuggingFace Hub, falling back to default Gradio theme")
|
||||
gradio_theme = gr.themes.Default()
|
||||
|
||||
|
||||
|
||||
class TotalTQDM:
|
||||
def __init__(self):
|
||||
|
||||
@ -72,16 +72,14 @@ class StyleDatabase:
|
||||
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
|
||||
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Write to temporary file first, so we don't nuke the file if something goes wrong
|
||||
fd, temp_path = tempfile.mkstemp(".csv")
|
||||
# Always keep a backup file around
|
||||
if os.path.exists(path):
|
||||
shutil.copy(path, path + ".bak")
|
||||
|
||||
fd = os.open(path, os.O_RDWR|os.O_CREAT)
|
||||
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
|
||||
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
|
||||
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
|
||||
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
|
||||
writer.writeheader()
|
||||
writer.writerows(style._asdict() for k, style in self.styles.items())
|
||||
|
||||
# Always keep a backup file around
|
||||
if os.path.exists(path):
|
||||
shutil.move(path, path + ".bak")
|
||||
shutil.move(temp_path, path)
|
||||
|
||||
@ -161,7 +161,9 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
|
||||
params.subindex = 0
|
||||
filename = os.path.join(src, imagefile)
|
||||
try:
|
||||
img = Image.open(filename).convert("RGB")
|
||||
img = Image.open(filename)
|
||||
img = ImageOps.exif_transpose(img)
|
||||
img = img.convert("RGB")
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
|
||||
@ -233,6 +233,12 @@ class EmbeddingDatabase:
|
||||
self.load_from_dir(embdir)
|
||||
embdir.update()
|
||||
|
||||
# re-sort word_embeddings because load_from_dir may not load in alphabetic order.
|
||||
# using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
|
||||
sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
|
||||
self.word_embeddings.clear()
|
||||
self.word_embeddings.update(sorted_word_embeddings)
|
||||
|
||||
displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
|
||||
if self.previously_displayed_embeddings != displayed_embeddings:
|
||||
self.previously_displayed_embeddings = displayed_embeddings
|
||||
|
||||
@ -171,8 +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)
|
||||
|
||||
@ -468,7 +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"):
|
||||
@ -1019,8 +1019,9 @@ def create_ui():
|
||||
interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
|
||||
|
||||
with FormRow():
|
||||
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
|
||||
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
|
||||
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
|
||||
save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
|
||||
|
||||
with FormRow():
|
||||
with gr.Column():
|
||||
@ -1204,7 +1205,7 @@ 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="")
|
||||
|
||||
@ -1565,7 +1566,7 @@ def create_ui():
|
||||
for _interface, label, _ifid in interfaces:
|
||||
shared.tab_names.append(label)
|
||||
|
||||
with gr.Blocks(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)
|
||||
@ -1658,6 +1659,7 @@ def create_ui():
|
||||
config_source,
|
||||
bake_in_vae,
|
||||
discard_weights,
|
||||
save_metadata,
|
||||
],
|
||||
outputs=[
|
||||
primary_model_name,
|
||||
@ -1705,7 +1707,7 @@ 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:
|
||||
|
||||
@ -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():
|
||||
|
||||
@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown):
|
||||
|
||||
def get_block_name(self):
|
||||
return "dropdown"
|
||||
|
||||
|
||||
class DropdownEditable(FormComponent, gr.Dropdown):
|
||||
"""Same as gr.Dropdown but allows editing value"""
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(allow_custom_value=True, **kwargs)
|
||||
|
||||
def get_block_name(self):
|
||||
return "dropdown"
|
||||
|
||||
|
||||
@ -129,7 +129,7 @@ def normalize_git_url(url):
|
||||
return url
|
||||
|
||||
|
||||
def install_extension_from_url(dirname, url):
|
||||
def install_extension_from_url(dirname, url, branch_name=None):
|
||||
check_access()
|
||||
|
||||
assert url, 'No URL specified'
|
||||
@ -150,10 +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 not 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:
|
||||
@ -376,13 +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_url, install_branch],
|
||||
outputs=[extensions_table, install_result],
|
||||
)
|
||||
|
||||
|
||||
@ -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")
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
astunparse
|
||||
blendmodes
|
||||
accelerate
|
||||
basicsr
|
||||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio==3.23
|
||||
invisible-watermark
|
||||
gradio==3.27
|
||||
numpy
|
||||
omegaconf
|
||||
opencv-contrib-python
|
||||
|
||||
@ -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
|
||||
|
||||
@ -7,7 +7,7 @@ function gradioApp() {
|
||||
}
|
||||
|
||||
function get_uiCurrentTab() {
|
||||
return gradioApp().querySelector('#tabs button:not(.border-transparent)')
|
||||
return gradioApp().querySelector('#tabs button.selected')
|
||||
}
|
||||
|
||||
function get_uiCurrentTabContent() {
|
||||
|
||||
@ -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, "<ast>", "exec"), module.__dict__)
|
||||
if type(last_ast.body[0]) == ast.Expr:
|
||||
return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
|
||||
else:
|
||||
exec(compile(last_ast, "<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)
|
||||
|
||||
|
||||
@ -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])
|
||||
|
||||
|
||||
@ -211,7 +211,8 @@ axis_options = [
|
||||
AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
|
||||
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
|
||||
AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn")),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin")),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax")),
|
||||
@ -374,16 +375,19 @@ 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 +405,74 @@ 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 +530,19 @@ 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
|
||||
|
||||
@ -312,6 +312,10 @@ 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;
|
||||
|
||||
@ -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
|
||||
|
||||
@ -43,4 +43,7 @@
|
||||
# Uncomment to enable accelerated launch
|
||||
#export ACCELERATE="True"
|
||||
|
||||
# Uncomment to disable TCMalloc
|
||||
#export NO_TCMALLOC="True"
|
||||
|
||||
###########################################
|
||||
|
||||
53
webui.py
53
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")
|
||||
|
||||
@ -150,6 +192,7 @@ def initialize():
|
||||
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
|
||||
shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
|
||||
startup_timer.record("opts onchange")
|
||||
|
||||
shared.reload_hypernetworks()
|
||||
|
||||
22
webui.sh
22
webui.sh
@ -113,12 +113,13 @@ case "$gpu_info" in
|
||||
printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
;;
|
||||
*)
|
||||
*)
|
||||
;;
|
||||
esac
|
||||
if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
|
||||
then
|
||||
export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
|
||||
# AMD users will still use torch 1.13 because 2.0 does not seem to work.
|
||||
export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2"
|
||||
fi
|
||||
|
||||
for preq in "${GIT}" "${python_cmd}"
|
||||
@ -172,15 +173,30 @@ else
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Try using TCMalloc on Linux
|
||||
prepare_tcmalloc() {
|
||||
if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
|
||||
TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.so.\d" | head -n 1)"
|
||||
if [[ ! -z "${TCMALLOC}" ]]; then
|
||||
echo "Using TCMalloc: ${TCMALLOC}"
|
||||
export LD_PRELOAD="${TCMALLOC}"
|
||||
else
|
||||
printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n"
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
|
||||
then
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Accelerating launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
prepare_tcmalloc
|
||||
exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
|
||||
else
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Launching launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
prepare_tcmalloc
|
||||
exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
fi
|
||||
|
||||
Loading…
Reference in New Issue
Block a user