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Stable Diffusion Webui
Commit 725a3849 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
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Merge branch 'dev' into pr/xyz-sort-001

parents c84118d7 8863b31d
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.gitignore
View file @ 725a3849
...@@ -32,4 +32,4 @@ notification.mp3 ...@@ -32,4 +32,4 @@ notification.mp3
/extensions /extensions
/test/stdout.txt /test/stdout.txt
/test/stderr.txt /test/stderr.txt
/cache.json /cache.json*
README.md
View file @ 725a3849
...@@ -120,6 +120,7 @@ sudo pacman -S wget git python3 ...@@ -120,6 +120,7 @@ sudo pacman -S wget git python3
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
``` ```
3. Run `webui.sh`. 3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon ### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
......
environment-wsl2.yaml
View file @ 725a3849
...@@ -4,8 +4,8 @@ channels: ...@@ -4,8 +4,8 @@ channels:
- defaults - defaults
dependencies: dependencies:
- python=3.10 - python=3.10
- pip=22.2.2 - pip=23.0
- cudatoolkit=11.3 - cudatoolkit=11.8
- pytorch=1.12.1 - pytorch=2.0
- torchvision=0.13.1 - torchvision=0.15
- numpy=1.23.1 - numpy=1.23
\ No newline at end of file
extensions-builtin/Lora/extra_networks_lora.py
View file @ 725a3849
...@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): ...@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
def activate(self, p, params_list): def activate(self, p, params_list):
additional = shared.opts.sd_lora additional = shared.opts.sd_lora
if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0: if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts] p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
......
extensions-builtin/Lora/scripts/lora_script.py
View file @ 725a3849
...@@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui) ...@@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui)
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), "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),
})) }))
extensions-builtin/ScuNET/scripts/scunet_model.py
View file @ 725a3849
...@@ -5,11 +5,15 @@ import traceback ...@@ -5,11 +5,15 @@ import traceback
import PIL.Image import PIL.Image
import numpy as np import numpy as np
import torch import torch
from tqdm import tqdm
from basicsr.utils.download_util import load_file_from_url from basicsr.utils.download_util import load_file_from_url
import modules.upscaler import modules.upscaler
from modules import devices, modelloader from modules import devices, modelloader
from scunet_model_arch import SCUNet as net from scunet_model_arch import SCUNet as net
from modules.shared import opts
from modules import images
class UpscalerScuNET(modules.upscaler.Upscaler): class UpscalerScuNET(modules.upscaler.Upscaler):
...@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler): ...@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
scalers.append(scaler_data2) scalers.append(scaler_data2)
self.scalers = scalers self.scalers = scalers
def do_upscale(self, img: PIL.Image, selected_file): @staticmethod
@torch.no_grad()
def tiled_inference(img, model):
# test the image tile by tile
h, w = img.shape[2:]
tile = opts.SCUNET_tile
tile_overlap = opts.SCUNET_tile_overlap
if tile == 0:
return model(img)
device = devices.get_device_for('scunet')
assert tile % 8 == 0, "tile size should be a multiple of window_size"
sf = 1
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
W = torch.zeros_like(E, dtype=devices.dtype, device=device)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
output = E.div_(W)
return output
def do_upscale(self, img: PIL.Image.Image, selected_file):
torch.cuda.empty_cache() torch.cuda.empty_cache()
model = self.load_model(selected_file) model = self.load_model(selected_file)
if model is None: if model is None:
print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
return img return img
device = devices.get_device_for('scunet') device = devices.get_device_for('scunet')
img = np.array(img) tile = opts.SCUNET_tile
img = img[:, :, ::-1] h, w = img.height, img.width
img = np.moveaxis(img, 2, 0) / 255 np_img = np.array(img)
img = torch.from_numpy(img).float() np_img = np_img[:, :, ::-1] # RGB to BGR
img = img.unsqueeze(0).to(device) np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
with torch.no_grad():
output = model(img) if tile > h or tile > w:
output = output.squeeze().float().cpu().clamp_(0, 1).numpy() _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
output = 255. * np.moveaxis(output, 0, 2) _img[:, :, :h, :w] = torch_img # pad image
output = output.astype(np.uint8) torch_img = _img
output = output[:, :, ::-1]
torch_output = self.tiled_inference(torch_img, model).squeeze(0)
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
del torch_img, torch_output
torch.cuda.empty_cache() torch.cuda.empty_cache()
return PIL.Image.fromarray(output, 'RGB')
output = np_output.transpose((1, 2, 0)) # CHW to HWC
output = output[:, :, ::-1] # BGR to RGB
return PIL.Image.fromarray((output * 255).astype(np.uint8))
def load_model(self, path: str): def load_model(self, path: str):
device = devices.get_device_for('scunet') device = devices.get_device_for('scunet')
...@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler): ...@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
model = model.to(device) model = model.to(device)
return model return model
extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
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// Stable Diffusion WebUI - Bracket checker // Stable Diffusion WebUI - Bracket checker
// Version 1.0 // By Hingashi no Florin/Bwin4L & @akx
// By Hingashi no Florin/Bwin4L
// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
function checkBrackets(evt, textArea, counterElt) { function checkBrackets(textArea, counterElt) {
errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n'; var counts = {};
errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n'; (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
errorStringCurly = '{...} - Different number of opening and closing curly brackets detected.\n'; counts[bracket] = (counts[bracket] || 0) + 1;
});
openBracketRegExp = /\(/g; var errors = [];
closeBracketRegExp = /\)/g;
function checkPair(open, close, kind) {
openSquareBracketRegExp = /\[/g; if (counts[open] !== counts[close]) {
closeSquareBracketRegExp = /\]/g; errors.push(
`${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
openCurlyBracketRegExp = /\{/g; );
closeCurlyBracketRegExp = /\}/g;
totalOpenBracketMatches = 0;
totalCloseBracketMatches = 0;
totalOpenSquareBracketMatches = 0;
totalCloseSquareBracketMatches = 0;
totalOpenCurlyBracketMatches = 0;
totalCloseCurlyBracketMatches = 0;
openBracketMatches = textArea.value.match(openBracketRegExp);
if(openBracketMatches) {
totalOpenBracketMatches = openBracketMatches.length;
}
closeBracketMatches = textArea.value.match(closeBracketRegExp);
if(closeBracketMatches) {
totalCloseBracketMatches = closeBracketMatches.length;
}
openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
if(openSquareBracketMatches) {
totalOpenSquareBracketMatches = openSquareBracketMatches.length;
}
closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
if(closeSquareBracketMatches) {
totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
}
openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
if(openCurlyBracketMatches) {
totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
}
closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
if(closeCurlyBracketMatches) {
totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
}
if(totalOpenBracketMatches != totalCloseBracketMatches) {
if(!counterElt.title.includes(errorStringParen)) {
counterElt.title += errorStringParen;
}
} else {
counterElt.title = counterElt.title.replace(errorStringParen, '');
}
if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
if(!counterElt.title.includes(errorStringSquare)) {
counterElt.title += errorStringSquare;
}
} else {
counterElt.title = counterElt.title.replace(errorStringSquare, '');
}
if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
if(!counterElt.title.includes(errorStringCurly)) {
counterElt.title += errorStringCurly;
} }
} else {
counterElt.title = counterElt.title.replace(errorStringCurly, '');
} }
if(counterElt.title != '') { checkPair('(', ')', 'round brackets');
counterElt.classList.add('error'); checkPair('[', ']', 'square brackets');
} else { checkPair('{', '}', 'curly brackets');
counterElt.classList.remove('error'); counterElt.title = errors.join('\n');
} counterElt.classList.toggle('error', errors.length !== 0);
} }
function setupBracketChecking(id_prompt, id_counter){ function setupBracketChecking(id_prompt, id_counter) {
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
var counter = gradioApp().getElementById(id_counter) var counter = gradioApp().getElementById(id_counter)
textarea.addEventListener("input", function(evt){ if (textarea && counter) {
checkBrackets(evt, textarea, counter) textarea.addEventListener("input", () => checkBrackets(textarea, counter));
}); }
} }
onUiLoaded(function(){ onUiLoaded(function () {
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter') setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter') setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
setupBracketChecking('img2img_prompt', 'img2img_token_counter') setupBracketChecking('img2img_prompt', 'img2img_token_counter');
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter') setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
}) });
\ No newline at end of file
javascript/contextMenus.js
View file @ 725a3849
...@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2]; ...@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_generate', '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 //End example Context Menu Items
......
javascript/edit-attention.js
View file @ 725a3849
...@@ -17,7 +17,7 @@ function keyupEditAttention(event){ ...@@ -17,7 +17,7 @@ function keyupEditAttention(event){
// Find opening parenthesis around current cursor // Find opening parenthesis around current cursor
const before = text.substring(0, selectionStart); const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN); let beforeParen = before.lastIndexOf(OPEN);
if (beforeParen == -1) return false; if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE); let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
...@@ -27,7 +27,7 @@ function keyupEditAttention(event){ ...@@ -27,7 +27,7 @@ function keyupEditAttention(event){
// Find closing parenthesis around current cursor // Find closing parenthesis around current cursor
const after = text.substring(selectionStart); const after = text.substring(selectionStart);
let afterParen = after.indexOf(CLOSE); let afterParen = after.indexOf(CLOSE);
if (afterParen == -1) return false; if (afterParen == -1) return false;
let afterParenOpen = after.indexOf(OPEN); let afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) { while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1); afterParen = after.indexOf(CLOSE, afterParen + 1);
...@@ -43,10 +43,28 @@ function keyupEditAttention(event){ ...@@ -43,10 +43,28 @@ function keyupEditAttention(event){
target.setSelectionRange(selectionStart, selectionEnd); target.setSelectionRange(selectionStart, selectionEnd);
return true; 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 target.setSelectionRange(selectionStart, selectionEnd);
if(! selectCurrentParenthesisBlock('<', '>')){ return true;
selectCurrentParenthesisBlock('(', ')') }
// If the user hasn't selected anything, let's select their current parenthesis block or word
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
selectCurrentWord();
} }
event.preventDefault(); event.preventDefault();
...@@ -81,7 +99,13 @@ function keyupEditAttention(event){ ...@@ -81,7 +99,13 @@ function keyupEditAttention(event){
weight = parseFloat(weight.toPrecision(12)); weight = parseFloat(weight.toPrecision(12));
if(String(weight).length == 1) weight += ".0" 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.focus();
target.value = text; target.value = text;
...@@ -93,4 +117,4 @@ function keyupEditAttention(event){ ...@@ -93,4 +117,4 @@ function keyupEditAttention(event){
addEventListener('keydown', (event) => { addEventListener('keydown', (event) => {
keyupEditAttention(event); keyupEditAttention(event);
}); });
\ No newline at end of file
javascript/generationParams.js
View file @ 725a3849
...@@ -16,9 +16,9 @@ onUiUpdate(function(){ ...@@ -16,9 +16,9 @@ onUiUpdate(function(){
let modalObserver = new MutationObserver(function(mutations) { let modalObserver = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) { mutations.forEach(function(mutationRecord) {
let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img') if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
gradioApp().getElementById(selectedTab+"_generation_info_button").click() gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
}); });
}); });
......
javascript/hints.js
View file @ 725a3849
...@@ -65,8 +65,8 @@ titles = { ...@@ -65,8 +65,8 @@ titles = {
"Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.", "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.", "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_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]; 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", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
"Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
...@@ -111,7 +111,8 @@ titles = { ...@@ -111,7 +111,8 @@ titles = {
"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", "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.", "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.", "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."
} }
......
javascript/imageviewer.js
View file @ 725a3849
...@@ -251,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() { ...@@ -251,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() {
modal.appendChild(modalNext) modal.appendChild(modalNext)
gradioApp().appendChild(modal) try {
gradioApp().appendChild(modal);
} catch (e) {
gradioApp().body.appendChild(modal);
}
document.body.appendChild(modal); document.body.appendChild(modal);
......
javascript/progressbar.js
View file @ 725a3849
...@@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre ...@@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
return return
} }
if(elapsedFromStart > 5 && !res.queued && !res.active){ if(elapsedFromStart > 40 && !res.queued && !res.active){
removeProgressBar() removeProgressBar()
return return
} }
......
launch.py
View file @ 725a3849
...@@ -121,12 +121,12 @@ def run_python(code, desc=None, errdesc=None): ...@@ -121,12 +121,12 @@ def run_python(code, desc=None, errdesc=None):
return run(f'"{python}" -c "{code}"', desc, errdesc) 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: if skip_install:
return return
index_url_line = f' --index-url {index_url}' if index_url != '' else '' 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): def check_run_python(code):
...@@ -225,10 +225,10 @@ def run_extensions_installers(settings_file): ...@@ -225,10 +225,10 @@ def run_extensions_installers(settings_file):
def prepare_environment(): def prepare_environment():
global skip_install 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") 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") 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") 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") openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
...@@ -271,7 +271,7 @@ def prepare_environment(): ...@@ -271,7 +271,7 @@ def prepare_environment():
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers: if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows": if platform.system() == "Windows":
if platform.python_version().startswith("3.10"): 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: else:
print("Installation of xformers is not supported in this version of Python.") 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") print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
...@@ -296,7 +296,7 @@ def prepare_environment(): ...@@ -296,7 +296,7 @@ def prepare_environment():
if not os.path.isfile(requirements_file): if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, 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) run_extensions_installers(settings_file=args.ui_settings_file)
......
modules/api/api.py
View file @ 725a3849
...@@ -6,7 +6,6 @@ import uvicorn ...@@ -6,7 +6,6 @@ import uvicorn
import gradio as gr import gradio as gr
from threading import Lock from threading import Lock
from io import BytesIO from io import BytesIO
from gradio.processing_utils import decode_base64_to_file
from fastapi import APIRouter, Depends, FastAPI, Request, Response from fastapi import APIRouter, Depends, FastAPI, Request, Response
from fastapi.security import HTTPBasic, HTTPBasicCredentials from fastapi.security import HTTPBasic, HTTPBasicCredentials
from fastapi.exceptions import HTTPException from fastapi.exceptions import HTTPException
...@@ -272,7 +271,9 @@ class Api: ...@@ -272,7 +271,9 @@ class Api:
raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") 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 # 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]: 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 return script_args
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
...@@ -395,16 +396,11 @@ class Api: ...@@ -395,16 +396,11 @@ class Api:
def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
reqDict = setUpscalers(req) reqDict = setUpscalers(req)
def prepareFiles(file): image_list = reqDict.pop('imageList', [])
file = decode_base64_to_file(file.data, file_path=file.name) image_folder = [decode_base64_to_image(x.data) for x in image_list]
file.orig_name = file.name
return file
reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
reqDict.pop('imageList')
with self.queue_lock: 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]) return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
......
modules/devices.py
View file @ 725a3849
...@@ -92,14 +92,18 @@ def cond_cast_float(input): ...@@ -92,14 +92,18 @@ def cond_cast_float(input):
def randn(seed, shape): def randn(seed, shape):
from modules.shared import opts
torch.manual_seed(seed) 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=cpu).to(device)
return torch.randn(shape, device=device) return torch.randn(shape, device=device)
def randn_without_seed(shape): 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=cpu).to(device)
return torch.randn(shape, device=device) return torch.randn(shape, device=device)
......
modules/extra_networks_hypernet.py
View file @ 725a3849
...@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): ...@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
def activate(self, p, params_list): def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork 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] 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])) params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
......
modules/generation_parameters_copypaste.py
View file @ 725a3849
...@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model ...@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
restore_old_hires_fix_params(res) 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 return res
...@@ -304,6 +308,8 @@ infotext_to_setting_name_mapping = [ ...@@ -304,6 +308,8 @@ infotext_to_setting_name_mapping = [
('UniPC skip type', 'uni_pc_skip_type'), ('UniPC skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'), ('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'), ('UniPC lower order final', 'uni_pc_lower_order_final'),
('RNG', 'randn_source'),
('NGMS', 's_min_uncond'),
] ]
......
modules/images.py
View file @ 725a3849
...@@ -352,6 +352,8 @@ class FilenameGenerator: ...@@ -352,6 +352,8 @@ class FilenameGenerator:
'prompt_no_styles': lambda self: self.prompt_no_style(), 'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False), 'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(), 'prompt_words': lambda self: self.prompt_words(),
'hasprompt': lambda self, *args: self.hasprompt(*args), #accept 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' default_time_format = '%Y%m%d%H%M%S'
...@@ -360,6 +362,22 @@ class FilenameGenerator: ...@@ -360,6 +362,22 @@ class FilenameGenerator:
self.seed = seed self.seed = seed
self.prompt = prompt self.prompt = prompt
self.image = image 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): def prompt_no_style(self):
if self.p is None or self.prompt is None: if self.p is None or self.prompt is None:
......
modules/img2img.py
View file @ 725a3849
...@@ -151,7 +151,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s ...@@ -151,7 +151,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
override_settings=override_settings, override_settings=override_settings,
) )
p.scripts = modules.scripts.scripts_txt2img p.scripts = modules.scripts.scripts_img2img
p.script_args = args p.script_args = args
if shared.cmd_opts.enable_console_prompts: if shared.cmd_opts.enable_console_prompts:
......
modules/interrogate.py
View file @ 725a3849
...@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir): ...@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
category_types = ["artists", "flavors", "mediums", "movements"] category_types = ["artists", "flavors", "mediums", "movements"]
try: try:
os.makedirs(tmpdir) os.makedirs(tmpdir, exist_ok=True)
for category_type in category_types: 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")) 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) os.rename(tmpdir, content_dir)
...@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir): ...@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
errors.display(e, "downloading default CLIP interrogate categories") errors.display(e, "downloading default CLIP interrogate categories")
finally: finally:
if os.path.exists(tmpdir): if os.path.exists(tmpdir):
os.remove(tmpdir) os.removedirs(tmpdir)
class InterrogateModels: class InterrogateModels:
......
modules/postprocessing.py
View file @ 725a3849
...@@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, ...@@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
if extras_mode == 1: if extras_mode == 1:
for img in image_folder: 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_data.append(image)
image_names.append(os.path.splitext(img.orig_name)[0]) image_names.append(fn)
elif extras_mode == 2: elif extras_mode == 2:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled' assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
assert input_dir, 'input directory not selected' assert input_dir, 'input directory not selected'
......
modules/processing.py
View file @ 725a3849
...@@ -3,6 +3,7 @@ import math ...@@ -3,6 +3,7 @@ import math
import os import os
import sys import sys
import warnings import warnings
import hashlib
import torch import torch
import numpy as np import numpy as np
...@@ -105,7 +106,7 @@ class StableDiffusionProcessing: ...@@ -105,7 +106,7 @@ class StableDiffusionProcessing:
""" """
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing 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: if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr) print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
...@@ -140,6 +141,7 @@ class StableDiffusionProcessing: ...@@ -140,6 +141,7 @@ class StableDiffusionProcessing:
self.denoising_strength: float = denoising_strength self.denoising_strength: float = denoising_strength
self.sampler_noise_scheduler_override = None self.sampler_noise_scheduler_override = None
self.ddim_discretize = ddim_discretize or opts.ddim_discretize 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_churn = s_churn or opts.s_churn
self.s_tmin = s_tmin or opts.s_tmin self.s_tmin = s_tmin or opts.s_tmin
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
...@@ -162,6 +164,7 @@ class StableDiffusionProcessing: ...@@ -162,6 +164,7 @@ class StableDiffusionProcessing:
self.all_seeds = None self.all_seeds = None
self.all_subseeds = None self.all_subseeds = None
self.iteration = 0 self.iteration = 0
@property @property
def sd_model(self): def sd_model(self):
...@@ -476,6 +479,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter ...@@ -476,6 +479,9 @@ 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, "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, "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, "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) generation_params.update(p.extra_generation_params)
...@@ -639,8 +645,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: ...@@ -639,8 +645,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
processed = Processed(p, [], p.seed, "") processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0)) file.write(processed.infotext(p, 0))
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc) step_multiplier = 1
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c) 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: if len(model_hijack.comments) > 0:
for comment in model_hijack.comments: for comment in model_hijack.comments:
...@@ -706,9 +718,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: ...@@ -706,9 +718,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image.info["parameters"] = text image.info["parameters"] = text
output_images.append(image) 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 = 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: 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") images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
...@@ -1007,6 +1019,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): ...@@ -1007,6 +1019,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.color_corrections = [] self.color_corrections = []
imgs = [] imgs = []
for img in self.init_images: 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) image = images.flatten(img, opts.img2img_background_color)
if crop_region is None and self.resize_mode != 3: if crop_region is None and self.resize_mode != 3:
......
modules/safe.py
View file @ 725a3849
# this code is adapted from the script contributed by anon from /h/ # this code is adapted from the script contributed by anon from /h/
import io
import pickle import pickle
import collections import collections
import sys import sys
...@@ -12,11 +11,9 @@ import _codecs ...@@ -12,11 +11,9 @@ import _codecs
import zipfile import zipfile
import re import re
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
def encode(*args): def encode(*args):
out = _codecs.encode(*args) out = _codecs.encode(*args)
return out return out
...@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler): ...@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):
def persistent_load(self, saved_id): def persistent_load(self, saved_id):
assert saved_id[0] == 'storage' assert saved_id[0] == 'storage'
return TypedStorage() return TypedStorage(_internal=True)
def find_class(self, module, name): def find_class(self, module, name):
if self.extra_handler is not None: if self.extra_handler is not None:
......
modules/sd_samplers_common.py
View file @ 725a3849
...@@ -60,3 +60,13 @@ def store_latent(decoded): ...@@ -60,3 +60,13 @@ def store_latent(decoded):
class InterruptedException(BaseException): class InterruptedException(BaseException):
pass 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
modules/sd_samplers_kdiffusion.py
View file @ 725a3849
...@@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module): ...@@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module):
return denoised 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: if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException raise sd_samplers_common.InterruptedException
...@@ -115,12 +115,21 @@ class CFGDenoiser(torch.nn.Module): ...@@ -115,12 +115,21 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = denoiser_params.sigma sigma_in = denoiser_params.sigma
tensor = denoiser_params.text_cond tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond uncond = denoiser_params.text_uncond
skip_uncond = False
if tensor.shape[1] == uncond.shape[1]: # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
if not is_edit_model: if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
cond_in = torch.cat([tensor, uncond]) skip_uncond = True
else: 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]) 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: if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in)) x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
...@@ -144,7 +153,13 @@ class CFGDenoiser(torch.nn.Module): ...@@ -144,7 +153,13 @@ class CFGDenoiser(torch.nn.Module):
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) if not skip_uncond:
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
denoised_image_indexes = [x[0][0] for x in conds_list]
if skip_uncond:
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params) cfg_denoised_callback(denoised_params)
...@@ -152,20 +167,21 @@ class CFGDenoiser(torch.nn.Module): ...@@ -152,20 +167,21 @@ class CFGDenoiser(torch.nn.Module):
devices.test_for_nans(x_out, "unet") devices.test_for_nans(x_out, "unet")
if opts.live_preview_content == "Prompt": 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": elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
if not is_edit_model: if is_edit_model:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
else:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) 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: if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised denoised = self.init_latent * self.mask + self.nmask * denoised
self.step += 1 self.step += 1
return denoised return denoised
...@@ -190,7 +206,7 @@ class TorchHijack: ...@@ -190,7 +206,7 @@ class TorchHijack:
if noise.shape == x.shape: if noise.shape == x.shape:
return noise 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) return torch.randn_like(x, device=devices.cpu).to(x.device)
else: else:
return torch.randn_like(x) return torch.randn_like(x)
...@@ -210,6 +226,7 @@ class KDiffusionSampler: ...@@ -210,6 +226,7 @@ class KDiffusionSampler:
self.eta = None self.eta = None
self.config = None self.config = None
self.last_latent = None self.last_latent = None
self.s_min_uncond = None
self.conditioning_key = sd_model.model.conditioning_key self.conditioning_key = sd_model.model.conditioning_key
...@@ -244,6 +261,7 @@ class KDiffusionSampler: ...@@ -244,6 +261,7 @@ class KDiffusionSampler:
self.model_wrap_cfg.step = 0 self.model_wrap_cfg.step = 0
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None) 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.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 []) k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
...@@ -326,6 +344,7 @@ class KDiffusionSampler: ...@@ -326,6 +344,7 @@ class KDiffusionSampler:
'image_cond': image_conditioning, 'image_cond': image_conditioning,
'uncond': unconditional_conditioning, 'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale, '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)) samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
...@@ -359,7 +378,8 @@ class KDiffusionSampler: ...@@ -359,7 +378,8 @@ class KDiffusionSampler:
'cond': conditioning, 'cond': conditioning,
'image_cond': image_conditioning, 'image_cond': image_conditioning,
'uncond': unconditional_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)) }, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples return samples
......
modules/shared.py
View file @ 725a3849
...@@ -4,6 +4,7 @@ import json ...@@ -4,6 +4,7 @@ import json
import os import os
import sys import sys
import time import time
import requests
from PIL import Image from PIL import Image
import gradio as gr import gradio as gr
...@@ -39,6 +40,7 @@ restricted_opts = { ...@@ -39,6 +40,7 @@ restricted_opts = {
"outdir_grids", "outdir_grids",
"outdir_txt2img_grids", "outdir_txt2img_grids",
"outdir_save", "outdir_save",
"outdir_init_images"
} }
ui_reorder_categories = [ ui_reorder_categories = [
...@@ -54,6 +56,21 @@ ui_reorder_categories = [ ...@@ -54,6 +56,21 @@ ui_reorder_categories = [
"scripts", "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 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 = \ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
...@@ -253,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" ...@@ -253,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"), "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_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"), "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"), "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"), "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
...@@ -268,6 +286,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { ...@@ -268,6 +286,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs), "outdir_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_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_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"), { options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
...@@ -283,6 +302,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { ...@@ -283,6 +302,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}), "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), "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"), { options_templates.update(options_section(('face-restoration', "Face restoration"), {
...@@ -331,6 +352,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { ...@@ -331,6 +352,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "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}), "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"), "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"), { options_templates.update(options_section(('compatibility', "Compatibility"), {
...@@ -338,6 +360,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { ...@@ -338,6 +360,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."), "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."), "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)."), "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"), { options_templates.update(options_section(('interrogate', "Interrogate Options"), {
...@@ -361,7 +384,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), { ...@@ -361,7 +384,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"), "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
"extra_networks_card_height": OptionInfo(0, "Card height 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"), "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"), { options_templates.update(options_section(('ui', "User interface"), {
...@@ -382,11 +405,13 @@ options_templates.update(options_section(('ui', "User interface"), { ...@@ -382,11 +405,13 @@ options_templates.update(options_section(('ui', "User interface"), {
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"), "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_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_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"), "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]}), "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_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab 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)), "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"), { options_templates.update(options_section(('ui', "Live previews"), {
...@@ -405,6 +430,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" ...@@ -405,6 +430,7 @@ 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}), "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']}), "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_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_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}), '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}), 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
...@@ -600,6 +626,24 @@ clip_model = None ...@@ -600,6 +626,24 @@ clip_model = None
progress_print_out = sys.stdout 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: class TotalTQDM:
def __init__(self): def __init__(self):
......
modules/textual_inversion/textual_inversion.py
View file @ 725a3849
...@@ -233,6 +233,12 @@ class EmbeddingDatabase: ...@@ -233,6 +233,12 @@ class EmbeddingDatabase:
self.load_from_dir(embdir) self.load_from_dir(embdir)
embdir.update() 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())) displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
if self.previously_displayed_embeddings != displayed_embeddings: if self.previously_displayed_embeddings != displayed_embeddings:
self.previously_displayed_embeddings = displayed_embeddings self.previously_displayed_embeddings = displayed_embeddings
......
modules/ui.py
View file @ 725a3849
...@@ -171,8 +171,8 @@ def create_seed_inputs(target_interface): ...@@ -171,8 +171,8 @@ def create_seed_inputs(target_interface):
with FormRow(elem_id=target_interface + '_seed_row', variant="compact"): 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 = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
seed.style(container=False) seed.style(container=False)
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_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') 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) seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
...@@ -468,7 +468,7 @@ def create_ui(): ...@@ -468,7 +468,7 @@ def create_ui():
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") 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"): 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: if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"): with gr.Column(elem_id="txt2img_column_batch"):
...@@ -1204,7 +1204,7 @@ def create_ui(): ...@@ -1204,7 +1204,7 @@ def create_ui():
with gr.Column(elem_id='ti_gallery_container'): with gr.Column(elem_id='ti_gallery_container'):
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) 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_progress = gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="")
...@@ -1565,7 +1565,7 @@ def create_ui(): ...@@ -1565,7 +1565,7 @@ def create_ui():
for _interface, label, _ifid in interfaces: for _interface, label, _ifid in interfaces:
shared.tab_names.append(label) shared.tab_names.append(label)
with gr.Blocks(analytics_enabled=False, title="Stable Diffusion") as demo: with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"): 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])): for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True) component = create_setting_component(k, is_quicksettings=True)
...@@ -1705,7 +1705,7 @@ def create_ui(): ...@@ -1705,7 +1705,7 @@ def create_ui():
if init_field is not None: if init_field is not None:
init_field(saved_value) 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') apply_field(x, 'visible')
if type(x) == gr.Slider: if type(x) == gr.Slider:
......
modules/ui_common.py
View file @ 725a3849
...@@ -125,7 +125,7 @@ Requested path was: {f} ...@@ -125,7 +125,7 @@ Requested path was: {f}
with gr.Column(variant='panel', elem_id=f"{tabname}_results"): with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"): 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 generation_info = None
with gr.Column(): with gr.Column():
......
modules/ui_components.py
View file @ 725a3849
...@@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown): ...@@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown):
def get_block_name(self): def get_block_name(self):
return "dropdown" 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"
modules/ui_extensions.py
View file @ 725a3849
...@@ -129,7 +129,7 @@ def normalize_git_url(url): ...@@ -129,7 +129,7 @@ def normalize_git_url(url):
return url return url
def install_extension_from_url(dirname, url): def install_extension_from_url(dirname, url, branch_name=None):
check_access() check_access()
assert url, 'No URL specified' assert url, 'No URL specified'
...@@ -150,10 +150,17 @@ def install_extension_from_url(dirname, url): ...@@ -150,10 +150,17 @@ def install_extension_from_url(dirname, url):
try: try:
shutil.rmtree(tmpdir, True) shutil.rmtree(tmpdir, True)
with git.Repo.clone_from(url, tmpdir) as repo: if not branch_name:
repo.remote().fetch() # if no branch is specified, use the default branch
for submodule in repo.submodules: with git.Repo.clone_from(url, tmpdir) as repo:
submodule.update() 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: try:
os.rename(tmpdir, target_dir) os.rename(tmpdir, target_dir)
except OSError as err: except OSError as err:
...@@ -376,13 +383,14 @@ def create_ui(): ...@@ -376,13 +383,14 @@ def create_ui():
with gr.TabItem("Install from URL"): with gr.TabItem("Install from URL"):
install_url = gr.Text(label="URL for extension's git repository") 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_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
install_button = gr.Button(value="Install", variant="primary") install_button = gr.Button(value="Install", variant="primary")
install_result = gr.HTML(elem_id="extension_install_result") install_result = gr.HTML(elem_id="extension_install_result")
install_button.click( install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]), 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], outputs=[extensions_table, install_result],
) )
......
modules/ui_postprocessing.py
View file @ 725a3849
...@@ -13,7 +13,7 @@ def create_ui(): ...@@ -13,7 +13,7 @@ def create_ui():
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image") 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: 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: 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") 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")
......
requirements_versions.txt
View file @ 725a3849
blendmodes==2022 blendmodes==2022
transformers==4.25.1 transformers==4.25.1
accelerate==0.12.0 accelerate==0.18.0
basicsr==1.4.2 basicsr==1.4.2
gfpgan==1.3.8 gfpgan==1.3.8
gradio==3.23 gradio==3.27
numpy==1.23.3 numpy==1.23.5
Pillow==9.4.0 Pillow==9.4.0
realesrgan==0.3.0 realesrgan==0.3.0
torch torch
...@@ -25,6 +25,6 @@ lark==1.1.2 ...@@ -25,6 +25,6 @@ lark==1.1.2
inflection==0.5.1 inflection==0.5.1
GitPython==3.1.30 GitPython==3.1.30
torchsde==0.2.5 torchsde==0.2.5
safetensors==0.3.0 safetensors==0.3.1
httpcore<=0.15 httpcore<=0.15
fastapi==0.94.0 fastapi==0.94.0
scripts/custom_code.py
View file @ 725a3849
import modules.scripts as scripts import modules.scripts as scripts
import gradio as gr import gradio as gr
import ast
import copy
from modules.processing import Processed from modules.processing import Processed
from modules.shared import opts, cmd_opts, state 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): class Script(scripts.Script):
def title(self): def title(self):
...@@ -13,12 +44,23 @@ class Script(scripts.Script): ...@@ -13,12 +44,23 @@ class Script(scripts.Script):
return cmd_opts.allow_code return cmd_opts.allow_code
def ui(self, is_img2img): 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
p.width = 768
p.height = 768
p.batch_size = 2
p.steps = 10
return process_images(p)
"""
return [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): def run(self, p, code, indent_level):
assert cmd_opts.allow_code, '--allow-code option must be enabled' assert cmd_opts.allow_code, '--allow-code option must be enabled'
display_result_data = [[], -1, ""] display_result_data = [[], -1, ""]
...@@ -29,13 +71,20 @@ class Script(scripts.Script): ...@@ -29,13 +71,20 @@ class Script(scripts.Script):
display_result_data[2] = i display_result_data[2] = i
from types import ModuleType from types import ModuleType
compiled = compile(code, '', 'exec')
module = ModuleType("testmodule") module = ModuleType("testmodule")
module.__dict__.update(globals()) module.__dict__.update(globals())
module.p = p module.p = p
module.display = display 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) return Processed(p, *display_result_data)
\ No newline at end of file
scripts/postprocessing_upscale.py
View file @ 725a3849
...@@ -4,8 +4,8 @@ import numpy as np ...@@ -4,8 +4,8 @@ import numpy as np
from modules import scripts_postprocessing, shared from modules import scripts_postprocessing, shared
import gradio as gr 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 = {} upscale_cache = {}
...@@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): ...@@ -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 gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
with FormRow(): with FormRow():
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w") with gr.Column(elem_id="upscaling_column_size", scale=4):
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h") upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop") 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(): with FormRow():
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name) extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
...@@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): ...@@ -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 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility") extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
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_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab]) tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
......
scripts/xyz_grid.py
View file @ 725a3849
...@@ -212,6 +212,7 @@ axis_options = [ ...@@ -212,6 +212,7 @@ axis_options = [
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), 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]), 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: sorted(sd_models.checkpoints_list, key=str.casefold)), 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 Churn", float, apply_field("s_churn")),
AxisOption("Sigma min", float, apply_field("s_tmin")), AxisOption("Sigma min", float, apply_field("s_tmin")),
AxisOption("Sigma max", float, apply_field("s_tmax")), AxisOption("Sigma max", float, apply_field("s_tmax")),
...@@ -374,16 +375,19 @@ class Script(scripts.Script): ...@@ -374,16 +375,19 @@ class Script(scripts.Script):
with gr.Row(): 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_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 = 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) fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
with gr.Row(): 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_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 = 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) fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
with gr.Row(): 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_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 = 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) fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
with gr.Row(variant="compact", elem_id="axis_options"): with gr.Row(variant="compact", elem_id="axis_options"):
...@@ -401,54 +405,74 @@ class Script(scripts.Script): ...@@ -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_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") 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): 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, self.current_axis_options[axis1_type].label, axis1_values 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) 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) 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) swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
def fill(x_type): def fill(x_type):
axis = self.current_axis_options[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_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values]) 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]) fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
def select_axis(x_type): def select_axis(axis_type,axis_values_dropdown):
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None) choices = self.current_axis_options[axis_type].choices
has_choices = choices is not None
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button]) current_values = axis_values_dropdown
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button]) if has_choices:
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button]) 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,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 = ( self.infotext_fields = (
(x_type, "X Type"), (x_type, "X Type"),
(x_values, "X Values"), (x_values, "X Values"),
(x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
(y_type, "Y Type"), (y_type, "Y Type"),
(y_values, "Y Values"), (y_values, "Y Values"),
(y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
(z_type, "Z Type"), (z_type, "Z Type"),
(z_values, "Z Values"), (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: if not no_fixed_seeds:
modules.processing.fix_seed(p) modules.processing.fix_seed(p)
if not opts.return_grid: if not opts.return_grid:
p.batch_size = 1 p.batch_size = 1
def process_axis(opt, vals): def process_axis(opt, vals, vals_dropdown):
if opt.label == 'Nothing': if opt.label == 'Nothing':
return [0] 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: if opt.type == int:
valslist_ext = [] valslist_ext = []
...@@ -506,13 +530,19 @@ class Script(scripts.Script): ...@@ -506,13 +530,19 @@ class Script(scripts.Script):
return valslist return valslist
x_opt = self.current_axis_options[x_type] 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] 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] 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 # 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 Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
......
style.css
View file @ 725a3849
...@@ -312,6 +312,10 @@ div.dimensions-tools{ ...@@ -312,6 +312,10 @@ div.dimensions-tools{
align-content: center; 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{ #mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
height: 480px !important; height: 480px !important;
max-height: 480px !important; max-height: 480px !important;
......
webui-macos-env.sh
View file @ 725a3849
...@@ -11,7 +11,7 @@ fi ...@@ -11,7 +11,7 @@ fi
export install_dir="$HOME" export install_dir="$HOME"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate" 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_REPO="https://github.com/brkirch/k-diffusion.git"
export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
export PYTORCH_ENABLE_MPS_FALLBACK=1 export PYTORCH_ENABLE_MPS_FALLBACK=1
......
webui-user.sh
View file @ 725a3849
...@@ -43,4 +43,7 @@ ...@@ -43,4 +43,7 @@
# Uncomment to enable accelerated launch # Uncomment to enable accelerated launch
#export ACCELERATE="True" #export ACCELERATE="True"
# Uncomment to disable TCMalloc
#export NO_TCMALLOC="True"
########################################### ###########################################
webui.py
View file @ 725a3849
...@@ -20,6 +20,9 @@ startup_timer = timer.Timer() ...@@ -20,6 +20,9 @@ startup_timer = timer.Timer()
import torch 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 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=DeprecationWarning, module="pytorch_lightning")
warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
startup_timer.record("import torch") startup_timer.record("import torch")
import gradio import gradio
...@@ -67,11 +70,51 @@ else: ...@@ -67,11 +70,51 @@ else:
server_name = "0.0.0.0" if cmd_opts.listen else None 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(): def check_versions():
if shared.cmd_opts.skip_version_check: if shared.cmd_opts.skip_version_check:
return return
expected_torch_version = "1.13.1" expected_torch_version = "2.0.0"
if version.parse(torch.__version__) < version.parse(expected_torch_version): if version.parse(torch.__version__) < version.parse(expected_torch_version):
errors.print_error_explanation(f""" errors.print_error_explanation(f"""
...@@ -84,7 +127,7 @@ there are reports of issues with training tab on the latest version. ...@@ -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. Use --skip-version-check commandline argument to disable this check.
""".strip()) """.strip())
expected_xformers_version = "0.0.16rc425" expected_xformers_version = "0.0.17"
if shared.xformers_available: if shared.xformers_available:
import xformers import xformers
...@@ -99,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check. ...@@ -99,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check.
def initialize(): def initialize():
fix_asyncio_event_loop_policy()
check_versions() check_versions()
extensions.list_extensions() extensions.list_extensions()
...@@ -126,9 +171,6 @@ def initialize(): ...@@ -126,9 +171,6 @@ def initialize():
modules.scripts.load_scripts() modules.scripts.load_scripts()
startup_timer.record("load scripts") startup_timer.record("load scripts")
modelloader.load_upscalers()
startup_timer.record("load upscalers")
modules.sd_vae.refresh_vae_list() modules.sd_vae.refresh_vae_list()
startup_timer.record("refresh VAE") startup_timer.record("refresh VAE")
...@@ -150,6 +192,7 @@ def initialize(): ...@@ -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", 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("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("temp_dir", ui_tempdir.on_tmpdir_changed)
shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
startup_timer.record("opts onchange") startup_timer.record("opts onchange")
shared.reload_hypernetworks() shared.reload_hypernetworks()
......
webui.sh
View file @ 725a3849
...@@ -23,7 +23,7 @@ fi ...@@ -23,7 +23,7 @@ fi
# Install directory without trailing slash # Install directory without trailing slash
if [[ -z "${install_dir}" ]] if [[ -z "${install_dir}" ]]
then then
install_dir="/home/$(whoami)" install_dir="${HOME}"
fi fi
# Name of the subdirectory (defaults to stable-diffusion-webui) # Name of the subdirectory (defaults to stable-diffusion-webui)
...@@ -113,12 +113,13 @@ case "$gpu_info" in ...@@ -113,12 +113,13 @@ case "$gpu_info" in
printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half" printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
printf "\n%s\n" "${delimiter}" printf "\n%s\n" "${delimiter}"
;; ;;
*) *)
;; ;;
esac esac
if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]] if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
then then
export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2" # AMD users will still use torch 1.13 because 2.0 does not seem to work.
export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2"
fi fi
for preq in "${GIT}" "${python_cmd}" for preq in "${GIT}" "${python_cmd}"
...@@ -172,15 +173,30 @@ else ...@@ -172,15 +173,30 @@ else
exit 1 exit 1
fi fi
# Try using TCMalloc on Linux
prepare_tcmalloc() {
if [[ "${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)" ] if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
then then
printf "\n%s\n" "${delimiter}" printf "\n%s\n" "${delimiter}"
printf "Accelerating launch.py..." printf "Accelerating launch.py..."
printf "\n%s\n" "${delimiter}" printf "\n%s\n" "${delimiter}"
prepare_tcmalloc
exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@" exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
else else
printf "\n%s\n" "${delimiter}" printf "\n%s\n" "${delimiter}"
printf "Launching launch.py..." printf "Launching launch.py..."
printf "\n%s\n" "${delimiter}" printf "\n%s\n" "${delimiter}"
prepare_tcmalloc
exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@" exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
fi fi
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