mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
Merge branch 'dev' into remove-watermark-option
This commit is contained in:
AUTOMATIC1111
GitHub
commit
263f0fb59c
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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@ -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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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) {
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if(!counterElt.title.includes(errorStringSquare)) {
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counterElt.title += errorStringSquare;
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}
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} else {
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counterElt.title = counterElt.title.replace(errorStringSquare, '');
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}
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checkPair('(', ')', 'round brackets');
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checkPair('[', ']', 'square brackets');
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checkPair('{', '}', 'curly brackets');
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counterElt.title = errors.join('\n');
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counterElt.classList.toggle('error', errors.length !== 0);
|
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}
|
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|
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if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
|
||||
if(!counterElt.title.includes(errorStringCurly)) {
|
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counterElt.title += errorStringCurly;
|
||||
}
|
||||
} else {
|
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counterElt.title = counterElt.title.replace(errorStringCurly, '');
|
||||
}
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function setupBracketChecking(id_prompt, id_counter) {
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var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
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var counter = gradioApp().getElementById(id_counter)
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||||
|
||||
if(counterElt.title != '') {
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counterElt.classList.add('error');
|
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} else {
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counterElt.classList.remove('error');
|
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if (textarea && counter) {
|
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textarea.addEventListener("input", () => checkBrackets(textarea, counter));
|
||||
}
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}
|
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|
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function setupBracketChecking(id_prompt, id_counter){
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var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
|
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var counter = gradioApp().getElementById(id_counter)
|
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textarea.addEventListener("input", function(evt){
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checkBrackets(evt, textarea, counter)
|
||||
});
|
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}
|
||||
|
||||
onUiLoaded(function(){
|
||||
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
|
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setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
|
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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);
|
||||
});
|
||||
});
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
|
||||
function extensions_apply(_, _){
|
||||
function extensions_apply(_, _, disable_all){
|
||||
var disable = []
|
||||
var update = []
|
||||
|
||||
@ -13,10 +13,10 @@ function extensions_apply(_, _){
|
||||
|
||||
restart_reload()
|
||||
|
||||
return [JSON.stringify(disable), JSON.stringify(update)]
|
||||
return [JSON.stringify(disable), JSON.stringify(update), disable_all]
|
||||
}
|
||||
|
||||
function extensions_check(){
|
||||
function extensions_check(_, _){
|
||||
var disable = []
|
||||
|
||||
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
|
||||
|
||||
@ -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.",
|
||||
@ -110,7 +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
|
||||
}
|
||||
|
||||
18
launch.py
18
launch.py
@ -121,12 +121,12 @@ def run_python(code, desc=None, errdesc=None):
|
||||
return run(f'"{python}" -c "{code}"', desc, errdesc)
|
||||
|
||||
|
||||
def run_pip(args, desc=None):
|
||||
def run_pip(args, desc=None, live=False):
|
||||
if skip_install:
|
||||
return
|
||||
|
||||
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
|
||||
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
|
||||
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
|
||||
|
||||
|
||||
def check_run_python(code):
|
||||
@ -206,6 +206,10 @@ def list_extensions(settings_file):
|
||||
print(e, file=sys.stderr)
|
||||
|
||||
disabled_extensions = set(settings.get('disabled_extensions', []))
|
||||
disable_all_extensions = settings.get('disable_all_extensions', 'none')
|
||||
|
||||
if disable_all_extensions != 'none':
|
||||
return []
|
||||
|
||||
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
|
||||
|
||||
@ -221,10 +225,10 @@ def run_extensions_installers(settings_file):
|
||||
def prepare_environment():
|
||||
global skip_install
|
||||
|
||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
|
||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118")
|
||||
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
|
||||
|
||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
|
||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
|
||||
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
|
||||
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
|
||||
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
|
||||
@ -235,7 +239,7 @@ def prepare_environment():
|
||||
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
|
||||
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
|
||||
|
||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
|
||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
|
||||
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
|
||||
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
|
||||
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
|
||||
@ -267,7 +271,7 @@ def prepare_environment():
|
||||
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
|
||||
if platform.system() == "Windows":
|
||||
if platform.python_version().startswith("3.10"):
|
||||
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
|
||||
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
|
||||
else:
|
||||
print("Installation of xformers is not supported in this version of Python.")
|
||||
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
|
||||
@ -292,7 +296,7 @@ def prepare_environment():
|
||||
|
||||
if not os.path.isfile(requirements_file):
|
||||
requirements_file = os.path.join(script_path, requirements_file)
|
||||
run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
|
||||
run_pip(f"install -r \"{requirements_file}\"", "requirements")
|
||||
|
||||
run_extensions_installers(settings_file=args.ui_settings_file)
|
||||
|
||||
|
||||
BIN
models/karlo/ViT-L-14_stats.th
Normal file
BIN
models/karlo/ViT-L-14_stats.th
Normal file
Binary file not shown.
@ -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)
|
||||
|
||||
|
||||
@ -15,7 +15,12 @@ if not os.path.exists(extensions_dir):
|
||||
|
||||
|
||||
def active():
|
||||
return [x for x in extensions if x.enabled]
|
||||
if shared.opts.disable_all_extensions == "all":
|
||||
return []
|
||||
elif shared.opts.disable_all_extensions == "extra":
|
||||
return [x for x in extensions if x.enabled and x.is_builtin]
|
||||
else:
|
||||
return [x for x in extensions if x.enabled]
|
||||
|
||||
|
||||
class Extension:
|
||||
@ -97,6 +102,11 @@ def list_extensions():
|
||||
if not os.path.isdir(extensions_dir):
|
||||
return
|
||||
|
||||
if shared.opts.disable_all_extensions == "all":
|
||||
print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
|
||||
elif shared.opts.disable_all_extensions == "extra":
|
||||
print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
|
||||
|
||||
extension_paths = []
|
||||
for dirname in [extensions_dir, extensions_builtin_dir]:
|
||||
if not os.path.isdir(dirname):
|
||||
@ -112,4 +122,3 @@ def list_extensions():
|
||||
for dirname, path, is_builtin in extension_paths:
|
||||
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
|
||||
extensions.append(extension)
|
||||
|
||||
|
||||
@ -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]))
|
||||
|
||||
|
||||
@ -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'),
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -312,7 +312,7 @@ class Hypernetwork:
|
||||
|
||||
def list_hypernetworks(path):
|
||||
res = {}
|
||||
for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
|
||||
for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
|
||||
name = os.path.splitext(os.path.basename(filename))[0]
|
||||
# Prevent a hypothetical "None.pt" from being listed.
|
||||
if name != "None":
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -151,13 +151,14 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
||||
override_settings=override_settings,
|
||||
)
|
||||
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.scripts = modules.scripts.scripts_img2img
|
||||
p.script_args = args
|
||||
|
||||
if shared.cmd_opts.enable_console_prompts:
|
||||
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
||||
|
||||
p.extra_generation_params["Mask blur"] = mask_blur
|
||||
if mask:
|
||||
p.extra_generation_params["Mask blur"] = mask_blur
|
||||
|
||||
if is_batch:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
|
||||
if hasattr(sd_model.cond_stage_model, 'model'):
|
||||
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
|
||||
|
||||
# remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
|
||||
# remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
|
||||
# send the model to GPU. Then put modules back. the modules will be in CPU.
|
||||
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
|
||||
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
|
||||
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
|
||||
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
|
||||
sd_model.to(devices.device)
|
||||
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
|
||||
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
|
||||
|
||||
# register hooks for those the first three models
|
||||
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
|
||||
@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram):
|
||||
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
|
||||
if sd_model.depth_model:
|
||||
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
|
||||
if sd_model.embedder:
|
||||
sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
|
||||
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
|
||||
|
||||
if hasattr(sd_model.cond_stage_model, 'model'):
|
||||
|
||||
@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
|
||||
|
||||
if extras_mode == 1:
|
||||
for img in image_folder:
|
||||
image = Image.open(img)
|
||||
if isinstance(img, Image.Image):
|
||||
image = img
|
||||
fn = ''
|
||||
else:
|
||||
image = Image.open(os.path.abspath(img.name))
|
||||
fn = os.path.splitext(img.orig_name)[0]
|
||||
|
||||
image_data.append(image)
|
||||
image_names.append(os.path.splitext(img.orig_name)[0])
|
||||
image_names.append(fn)
|
||||
elif extras_mode == 2:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
||||
assert input_dir, 'input directory not selected'
|
||||
|
||||
@ -3,6 +3,7 @@ import math
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
import hashlib
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
@ -78,28 +79,34 @@ def apply_overlay(image, paste_loc, index, overlays):
|
||||
|
||||
|
||||
def txt2img_image_conditioning(sd_model, x, width, height):
|
||||
if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
|
||||
# Dummy zero conditioning if we're not using inpainting model.
|
||||
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
|
||||
|
||||
# The "masked-image" in this case will just be all zeros since the entire image is masked.
|
||||
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
|
||||
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
|
||||
|
||||
# Add the fake full 1s mask to the first dimension.
|
||||
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
|
||||
image_conditioning = image_conditioning.to(x.dtype)
|
||||
|
||||
return image_conditioning
|
||||
|
||||
elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
|
||||
|
||||
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
|
||||
|
||||
else:
|
||||
# Dummy zero conditioning if we're not using inpainting or unclip models.
|
||||
# Still takes up a bit of memory, but no encoder call.
|
||||
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
|
||||
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
|
||||
|
||||
# The "masked-image" in this case will just be all zeros since the entire image is masked.
|
||||
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
|
||||
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
|
||||
|
||||
# Add the fake full 1s mask to the first dimension.
|
||||
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
|
||||
image_conditioning = image_conditioning.to(x.dtype)
|
||||
|
||||
return image_conditioning
|
||||
|
||||
|
||||
class StableDiffusionProcessing:
|
||||
"""
|
||||
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)
|
||||
|
||||
@ -134,6 +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
|
||||
@ -156,6 +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):
|
||||
@ -190,6 +200,14 @@ class StableDiffusionProcessing:
|
||||
|
||||
return conditioning_image
|
||||
|
||||
def unclip_image_conditioning(self, source_image):
|
||||
c_adm = self.sd_model.embedder(source_image)
|
||||
if self.sd_model.noise_augmentor is not None:
|
||||
noise_level = 0 # TODO: Allow other noise levels?
|
||||
c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
|
||||
c_adm = torch.cat((c_adm, noise_level_emb), 1)
|
||||
return c_adm
|
||||
|
||||
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
|
||||
self.is_using_inpainting_conditioning = True
|
||||
|
||||
@ -241,6 +259,9 @@ class StableDiffusionProcessing:
|
||||
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
|
||||
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
|
||||
|
||||
if self.sampler.conditioning_key == "crossattn-adm":
|
||||
return self.unclip_image_conditioning(source_image)
|
||||
|
||||
# Dummy zero conditioning if we're not using inpainting or depth model.
|
||||
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
|
||||
|
||||
@ -459,6 +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)
|
||||
@ -622,8 +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:
|
||||
@ -653,6 +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)
|
||||
|
||||
@ -689,9 +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")
|
||||
@ -701,7 +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)
|
||||
|
||||
@ -854,6 +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
|
||||
|
||||
@ -923,6 +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
|
||||
|
||||
|
||||
@ -990,6 +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:
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -122,7 +122,7 @@ def list_models():
|
||||
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
|
||||
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
|
||||
|
||||
for filename in model_list:
|
||||
for filename in sorted(model_list, key=str.lower):
|
||||
checkpoint_info = CheckpointInfo(filename)
|
||||
checkpoint_info.register()
|
||||
|
||||
@ -383,6 +383,14 @@ def repair_config(sd_config):
|
||||
elif shared.cmd_opts.upcast_sampling:
|
||||
sd_config.model.params.unet_config.params.use_fp16 = True
|
||||
|
||||
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
||||
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
|
||||
|
||||
# For UnCLIP-L, override the hardcoded karlo directory
|
||||
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
|
||||
karlo_path = os.path.join(paths.models_path, 'karlo')
|
||||
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
|
||||
|
||||
|
||||
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
|
||||
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
|
||||
|
||||
@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
|
||||
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
|
||||
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
|
||||
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
|
||||
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
|
||||
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
|
||||
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
|
||||
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
|
||||
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
|
||||
@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict):
|
||||
def guess_model_config_from_state_dict(sd, filename):
|
||||
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
|
||||
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
|
||||
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
|
||||
|
||||
if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
|
||||
return config_depth_model
|
||||
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
|
||||
return config_unclip
|
||||
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
|
||||
return config_unopenclip
|
||||
|
||||
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler:
|
||||
|
||||
# Have to unwrap the inpainting conditioning here to perform pre-processing
|
||||
image_conditioning = None
|
||||
uc_image_conditioning = None
|
||||
if isinstance(cond, dict):
|
||||
image_conditioning = cond["c_concat"][0]
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
image_conditioning = cond["c_adm"]
|
||||
uc_image_conditioning = unconditional_conditioning["c_adm"]
|
||||
else:
|
||||
image_conditioning = cond["c_concat"][0]
|
||||
cond = cond["c_crossattn"][0]
|
||||
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
|
||||
|
||||
@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler:
|
||||
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
|
||||
# Note that they need to be lists because it just concatenates them later.
|
||||
if image_conditioning is not None:
|
||||
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
|
||||
else:
|
||||
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
return x, ts, cond, unconditional_conditioning
|
||||
|
||||
@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler:
|
||||
|
||||
# Wrap the conditioning models with additional image conditioning for inpainting model
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
|
||||
else:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
|
||||
|
||||
@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler:
|
||||
# Wrap the conditioning models with additional image conditioning for inpainting model
|
||||
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
|
||||
else:
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
|
||||
|
||||
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module):
|
||||
batch_size = len(conds_list)
|
||||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
|
||||
if shared.sd_model.model.conditioning_key == "crossattn-adm":
|
||||
image_uncond = torch.zeros_like(image_cond)
|
||||
make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
|
||||
else:
|
||||
image_uncond = image_cond
|
||||
make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
|
||||
|
||||
if not is_edit_model:
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
|
||||
else:
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
|
||||
|
||||
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
|
||||
cfg_denoiser_callback(denoiser_params)
|
||||
@ -108,21 +115,30 @@ 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={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
||||
@ -135,9 +151,15 @@ class CFGDenoiser(torch.nn.Module):
|
||||
else:
|
||||
c_crossattn = torch.cat([tensor[a:b]], uncond)
|
||||
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
|
||||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
|
||||
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)
|
||||
@ -145,20 +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
|
||||
|
||||
|
||||
@ -183,7 +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)
|
||||
@ -203,6 +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
|
||||
|
||||
@ -237,6 +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 [])
|
||||
|
||||
@ -319,6 +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))
|
||||
@ -352,7 +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 = \
|
||||
@ -252,6 +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"),
|
||||
"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"),
|
||||
@ -267,6 +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"), {
|
||||
@ -282,6 +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"), {
|
||||
@ -330,6 +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"), {
|
||||
@ -337,6 +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"), {
|
||||
@ -360,7 +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"), {
|
||||
@ -381,11 +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"), {
|
||||
@ -404,6 +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}),
|
||||
@ -421,7 +447,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
||||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
||||
"disabled_extensions": OptionInfo([], "Disable these extensions"),
|
||||
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
}))
|
||||
|
||||
@ -598,6 +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)
|
||||
|
||||
@ -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
|
||||
|
||||
@ -70,17 +70,6 @@ def gr_show(visible=True):
|
||||
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
|
||||
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
||||
|
||||
css_hide_progressbar = """
|
||||
.wrap .m-12 svg { display:none!important; }
|
||||
.wrap .m-12::before { content:"Loading..." }
|
||||
.wrap .z-20 svg { display:none!important; }
|
||||
.wrap .z-20::before { content:"Loading..." }
|
||||
.wrap.cover-bg .z-20::before { content:"" }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
.meta-text-center { display:none!important; }
|
||||
"""
|
||||
|
||||
# Using constants for these since the variation selector isn't visible.
|
||||
# Important that they exactly match script.js for tooltip to work.
|
||||
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
||||
@ -182,8 +171,8 @@ def create_seed_inputs(target_interface):
|
||||
with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
|
||||
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
|
||||
seed.style(container=False)
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
|
||||
|
||||
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
|
||||
|
||||
@ -479,7 +468,7 @@ def create_ui():
|
||||
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
||||
|
||||
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
@ -1215,7 +1204,7 @@ def create_ui():
|
||||
|
||||
with gr.Column(elem_id='ti_gallery_container'):
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
|
||||
@ -1566,22 +1555,6 @@ def create_ui():
|
||||
(train_interface, "Train", "ti"),
|
||||
]
|
||||
|
||||
css = ""
|
||||
|
||||
for cssfile in modules.scripts.list_files_with_name("style.css"):
|
||||
if not os.path.isfile(cssfile):
|
||||
continue
|
||||
|
||||
with open(cssfile, "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if os.path.exists(os.path.join(data_path, "user.css")):
|
||||
with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
css += css_hide_progressbar
|
||||
|
||||
interfaces += script_callbacks.ui_tabs_callback()
|
||||
interfaces += [(settings_interface, "Settings", "settings")]
|
||||
|
||||
@ -1592,7 +1565,7 @@ def create_ui():
|
||||
for _interface, label, _ifid in interfaces:
|
||||
shared.tab_names.append(label)
|
||||
|
||||
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
with gr.Row(elem_id="quicksettings", variant="compact"):
|
||||
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
|
||||
component = create_setting_component(k, is_quicksettings=True)
|
||||
@ -1655,6 +1628,7 @@ def create_ui():
|
||||
fn=get_settings_values,
|
||||
inputs=[],
|
||||
outputs=[component_dict[k] for k in component_keys],
|
||||
queue=False,
|
||||
)
|
||||
|
||||
def modelmerger(*args):
|
||||
@ -1731,7 +1705,7 @@ def create_ui():
|
||||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
|
||||
if type(x) == gr.Slider:
|
||||
@ -1777,25 +1751,60 @@ def create_ui():
|
||||
return demo
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
def webpath(fn):
|
||||
if fn.startswith(script_path):
|
||||
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
|
||||
else:
|
||||
web_path = os.path.abspath(fn)
|
||||
|
||||
return f'file={web_path}?{os.path.getmtime(fn)}'
|
||||
|
||||
|
||||
def javascript_html():
|
||||
script_js = os.path.join(script_path, "script.js")
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath(script_js)}?{os.path.getmtime(script_js)}"></script>\n'
|
||||
head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
|
||||
|
||||
inline = f"{localization.localization_js(shared.opts.localization)};"
|
||||
if cmd_opts.theme is not None:
|
||||
inline += f"set_theme('{cmd_opts.theme}');"
|
||||
|
||||
for script in modules.scripts.list_scripts("javascript", ".js"):
|
||||
head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
|
||||
|
||||
for script in modules.scripts.list_scripts("javascript", ".mjs"):
|
||||
head += f'<script type="module" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
|
||||
head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
|
||||
|
||||
head += f'<script type="text/javascript">{inline}</script>\n'
|
||||
|
||||
return head
|
||||
|
||||
|
||||
def css_html():
|
||||
head = ""
|
||||
|
||||
def stylesheet(fn):
|
||||
return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
|
||||
|
||||
for cssfile in modules.scripts.list_files_with_name("style.css"):
|
||||
if not os.path.isfile(cssfile):
|
||||
continue
|
||||
|
||||
head += stylesheet(cssfile)
|
||||
|
||||
if os.path.exists(os.path.join(data_path, "user.css")):
|
||||
head += stylesheet(os.path.join(data_path, "user.css"))
|
||||
|
||||
return head
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
js = javascript_html()
|
||||
css = css_html()
|
||||
|
||||
def template_response(*args, **kwargs):
|
||||
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
|
||||
res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
|
||||
res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
|
||||
res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
|
||||
@ -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"
|
||||
|
||||
|
||||
@ -21,7 +21,7 @@ def check_access():
|
||||
assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
|
||||
|
||||
|
||||
def apply_and_restart(disable_list, update_list):
|
||||
def apply_and_restart(disable_list, update_list, disable_all):
|
||||
check_access()
|
||||
|
||||
disabled = json.loads(disable_list)
|
||||
@ -43,6 +43,7 @@ def apply_and_restart(disable_list, update_list):
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
shared.opts.disabled_extensions = disabled
|
||||
shared.opts.disable_all_extensions = disable_all
|
||||
shared.opts.save(shared.config_filename)
|
||||
|
||||
shared.state.interrupt()
|
||||
@ -99,9 +100,13 @@ def extension_table():
|
||||
else:
|
||||
ext_status = ext.status
|
||||
|
||||
style = ""
|
||||
if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
|
||||
style = ' style="color: var(--primary-400)"'
|
||||
|
||||
code += f"""
|
||||
<tr>
|
||||
<td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
|
||||
<td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
|
||||
<td>{remote}</td>
|
||||
<td>{ext.version}</td>
|
||||
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
|
||||
@ -124,7 +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'
|
||||
@ -145,10 +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:
|
||||
@ -294,16 +306,24 @@ def create_ui():
|
||||
with gr.Row(elem_id="extensions_installed_top"):
|
||||
apply = gr.Button(value="Apply and restart UI", variant="primary")
|
||||
check = gr.Button(value="Check for updates")
|
||||
extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
|
||||
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
|
||||
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
|
||||
|
||||
info = gr.HTML()
|
||||
html = ""
|
||||
if shared.opts.disable_all_extensions != "none":
|
||||
html = """
|
||||
<span style="color: var(--primary-400);">
|
||||
"Disable all extensions" was set, change it to "none" to load all extensions again
|
||||
</span>
|
||||
"""
|
||||
info = gr.HTML(html)
|
||||
extensions_table = gr.HTML(lambda: extension_table())
|
||||
|
||||
apply.click(
|
||||
fn=apply_and_restart,
|
||||
_js="extensions_apply",
|
||||
inputs=[extensions_disabled_list, extensions_update_list],
|
||||
inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
@ -363,13 +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")
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
astunparse
|
||||
blendmodes
|
||||
accelerate
|
||||
basicsr
|
||||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio==3.23
|
||||
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
|
||||
|
||||
@ -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
|
||||
|
||||
12
style.css
12
style.css
@ -312,12 +312,23 @@ div.dimensions-tools{
|
||||
align-content: center;
|
||||
}
|
||||
|
||||
div#extras_scale_to_tab div.form{
|
||||
flex-direction: row;
|
||||
}
|
||||
|
||||
#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
|
||||
height: 480px !important;
|
||||
max-height: 480px !important;
|
||||
min-height: 480px !important;
|
||||
}
|
||||
|
||||
#img2img_sketch, #img2maskimg, #inpaint_sketch {
|
||||
overflow: overlay !important;
|
||||
resize: auto;
|
||||
background: var(--panel-background-fill);
|
||||
z-index: 5;
|
||||
}
|
||||
|
||||
.image-buttons button{
|
||||
min-width: auto;
|
||||
}
|
||||
@ -329,6 +340,7 @@ div.dimensions-tools{
|
||||
/* settings */
|
||||
#quicksettings {
|
||||
width: fit-content;
|
||||
align-items: end;
|
||||
}
|
||||
|
||||
#quicksettings > div, #quicksettings > fieldset{
|
||||
|
||||
@ -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()
|
||||
|
||||
24
webui.sh
24
webui.sh
@ -23,7 +23,7 @@ fi
|
||||
# Install directory without trailing slash
|
||||
if [[ -z "${install_dir}" ]]
|
||||
then
|
||||
install_dir="/home/$(whoami)"
|
||||
install_dir="${HOME}"
|
||||
fi
|
||||
|
||||
# Name of the subdirectory (defaults to stable-diffusion-webui)
|
||||
@ -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