Unverified Commit 660ae690 authored by Dynamic's avatar Dynamic Committed by GitHub

Merge branch 'AUTOMATIC1111:master' into kr-localization

parents 21364c5c 6bd6154a
---
name: Bug report
about: Create a report to help us improve
title: ''
labels: bug-report
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Expected behavior**
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**Screenshots**
If applicable, add screenshots to help explain your problem.
**Desktop (please complete the following information):**
- OS: [e.g. Windows, Linux]
- Browser [e.g. chrome, safari]
- Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`]
**Additional context**
Add any other context about the problem here.
name: Bug Report
description: You think somethings is broken in the UI
title: "[Bug]: "
labels: ["bug-report"]
body:
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
options:
- label: I have searched the existing issues and checked the recent builds/commits
required: true
- type: markdown
attributes:
value: |
*Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
- type: textarea
id: what-did
attributes:
label: What happened?
description: Tell us what happened in a very clear and simple way
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1. Go to ....
2. Press ....
3. ...
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required: true
- type: textarea
id: what-should
attributes:
label: What should have happened?
description: tell what you think the normal behavior should be
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- type: input
id: commit
attributes:
label: Commit where the problem happens
description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
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- type: dropdown
id: platforms
attributes:
label: What platforms do you use to access UI ?
multiple: true
options:
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- Linux
- MacOS
- iOS
- Android
- Other/Cloud
- type: dropdown
id: browsers
attributes:
label: What browsers do you use to access the UI ?
multiple: true
options:
- Mozilla Firefox
- Google Chrome
- Brave
- Apple Safari
- Microsoft Edge
- type: textarea
id: cmdargs
attributes:
label: Command Line Arguments
description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below
render: Shell
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attributes:
label: Additional information, context and logs
description: Please provide us with any relevant additional info, context or log output.
blank_issues_enabled: false
contact_links:
- name: WebUI Community Support
url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
about: Please ask and answer questions here.
---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: 'suggestion'
assignees: ''
---
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name: Feature request
description: Suggest an idea for this project
title: "[Feature Request]: "
labels: ["suggestion"]
body:
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attributes:
label: Is there an existing issue for this?
description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
options:
- label: I have searched the existing issues and checked the recent builds/commits
required: true
- type: markdown
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value: |
*Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
- type: textarea
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......@@ -27,3 +27,5 @@ __pycache__
notification.mp3
/SwinIR
/textual_inversion
.vscode
/extensions
......@@ -11,6 +11,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
......@@ -23,6 +24,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
......@@ -37,14 +39,14 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Prompt length validation
- get length of prompt in tokens as you type
- get a warning after generation if some text was truncated
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with --allow-code to enable)
- Mouseover hints for most UI elements
......@@ -59,10 +61,10 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge two checkpoints into one
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
......@@ -70,14 +72,35 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
- History tab: view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Use Hypernetworks
- Use VAEs
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
## Where are Aesthetic Gradients?!?!
Aesthetic Gradients are now an extension. You can install it using git:
```commandline
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
```
After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use Google Colab:
Alternatively, use online services (like Google Colab):
- [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl)
- [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh).
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
......
......@@ -523,7 +523,6 @@ Affandi,0.7170285,nudity
Diane Arbus,0.655138,digipa-high-impact
Joseph Ducreux,0.65247905,digipa-high-impact
Berthe Morisot,0.7165984,fineart
Hilma AF Klint,0.71643853,scribbles
Hilma af Klint,0.71643853,scribbles
Filippino Lippi,0.7163017,fineart
Leonid Afremov,0.7163005,fineart
......@@ -738,14 +737,12 @@ Abraham Mignon,0.60605425,fineart
Albert Bloch,0.69573116,nudity
Charles Dana Gibson,0.67155975,fineart
Alexandre-Évariste Fragonard,0.6507174,fineart
Alexandre-Évariste Fragonard,0.6507174,fineart
Ernst Fuchs,0.6953538,nudity
Alfredo Jaar,0.6952965,digipa-high-impact
Judy Chicago,0.6952246,weird
Frans van Mieris the Younger,0.6951849,fineart
Aertgen van Leyden,0.6951305,fineart
Emily Carr,0.69512105,fineart
Frances Macdonald,0.6950408,scribbles
Frances MacDonald,0.6950408,scribbles
Hannah Höch,0.69495845,scribbles
Gillis Rombouts,0.58770025,fineart
......@@ -895,7 +892,6 @@ Richard McGuire,0.6820089,scribbles
Anni Albers,0.65708244,digipa-high-impact
Aleksey Savrasov,0.65207493,fineart
Wayne Barlowe,0.6537874,fineart
Giorgio De Chirico,0.6815907,fineart
Giorgio de Chirico,0.6815907,fineart
Ernest Procter,0.6815795,fineart
Adriaen Brouwer,0.6815058,fineart
......@@ -1241,7 +1237,6 @@ Betty Churcher,0.65387225,fineart
Claes Corneliszoon Moeyaert,0.65386075,fineart
David Bomberg,0.6537477,fineart
Abraham Bosschaert,0.6535562,fineart
Giuseppe De Nittis,0.65354455,fineart
Giuseppe de Nittis,0.65354455,fineart
John La Farge,0.65342575,fineart
Frits Thaulow,0.65341854,fineart
......@@ -1522,7 +1517,6 @@ Gertrude Harvey,0.5903887,fineart
Grant Wood,0.6266253,fineart
Fyodor Vasilyev,0.5234919,digipa-med-impact
Cagnaccio di San Pietro,0.6261671,fineart
Cagnaccio Di San Pietro,0.6261671,fineart
Doris Boulton-Maude,0.62593174,fineart
Adolf Hirémy-Hirschl,0.5946784,fineart
Harold von Schmidt,0.6256755,fineart
......@@ -2411,7 +2405,6 @@ Hermann Feierabend,0.5346168,digipa-high-impact
Antonio Donghi,0.4610982,digipa-low-impact
Adonna Khare,0.4858036,digipa-med-impact
James Stokoe,0.5015107,digipa-med-impact
Art & Language,0.5341332,digipa-high-impact
Agustín Fernández,0.53403986,fineart
Germán Londoño,0.5338712,fineart
Emmanuelle Moureaux,0.5335641,digipa-high-impact
......
......@@ -3,12 +3,12 @@ let currentWidth = null;
let currentHeight = null;
let arFrameTimeout = setTimeout(function(){},0);
function dimensionChange(e,dimname){
function dimensionChange(e, is_width, is_height){
if(dimname == 'Width'){
if(is_width){
currentWidth = e.target.value*1.0
}
if(dimname == 'Height'){
if(is_height){
currentHeight = e.target.value*1.0
}
......@@ -18,22 +18,13 @@ function dimensionChange(e,dimname){
return;
}
var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200')
if(img2imgMode){
img2imgMode=img2imgMode.innerText
}else{
return;
}
var redrawImage = gradioApp().querySelector('div[data-testid=image] img');
var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img')
var targetElement = null;
if(img2imgMode=='img2img' && redrawImage){
targetElement = redrawImage;
}else if(img2imgMode=='Inpaint' && inpaintImage){
targetElement = inpaintImage;
var tabIndex = get_tab_index('mode_img2img')
if(tabIndex == 0){
targetElement = gradioApp().querySelector('div[data-testid=image] img');
} else if(tabIndex == 1){
targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
}
if(targetElement){
......@@ -98,22 +89,20 @@ onUiUpdate(function(){
var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
if(inImg2img){
let inputs = gradioApp().querySelectorAll('input');
inputs.forEach(function(e){
let parentLabel = e.parentElement.querySelector('label')
if(parentLabel && parentLabel.innerText){
if(!e.classList.contains('scrollwatch')){
if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){
e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} )
e.classList.add('scrollwatch')
}
if(parentLabel.innerText == 'Width'){
currentWidth = e.value*1.0
}
if(parentLabel.innerText == 'Height'){
currentHeight = e.value*1.0
}
}
}
inputs.forEach(function(e){
var is_width = e.parentElement.id == "img2img_width"
var is_height = e.parentElement.id == "img2img_height"
if((is_width || is_height) && !e.classList.contains('scrollwatch')){
e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
e.classList.add('scrollwatch')
}
if(is_width){
currentWidth = e.value*1.0
}
if(is_height){
currentHeight = e.value*1.0
}
})
}
});
......@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
window.document.addEventListener('dragover', e => {
const target = e.composedPath()[0];
const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
return;
}
e.stopPropagation();
......
......@@ -9,9 +9,38 @@ addEventListener('keydown', (event) => {
let minus = "ArrowDown"
if (event.key != plus && event.key != minus) return;
selectionStart = target.selectionStart;
selectionEnd = target.selectionEnd;
if(selectionStart == selectionEnd) return;
let selectionStart = target.selectionStart;
let selectionEnd = target.selectionEnd;
// If the user hasn't selected anything, let's select their current parenthesis block
if (selectionStart === selectionEnd) {
// Find opening parenthesis around current cursor
const before = target.value.substring(0, selectionStart);
let beforeParen = before.lastIndexOf("(");
if (beforeParen == -1) return;
let beforeParenClose = before.lastIndexOf(")");
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf("(", beforeParen - 1);
beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1);
}
// Find closing parenthesis around current cursor
const after = target.value.substring(selectionStart);
let afterParen = after.indexOf(")");
if (afterParen == -1) return;
let afterParenOpen = after.indexOf("(");
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(")", afterParen + 1);
afterParenOpen = after.indexOf("(", afterParenOpen + 1);
}
if (beforeParen === -1 || afterParen === -1) return;
// Set the selection to the text between the parenthesis
const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen);
const lastColon = parenContent.lastIndexOf(":");
selectionStart = beforeParen + 1;
selectionEnd = selectionStart + lastColon;
target.setSelectionRange(selectionStart, selectionEnd);
}
event.preventDefault();
......
......@@ -91,6 +91,8 @@ titles = {
"Weighted sum": "Result = A * (1 - M) + B * M",
"Add difference": "Result = A + (B - C) * M",
"Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
}
......
......@@ -17,14 +17,6 @@ var images_history_click_image = function(){
images_history_set_image_info(this);
}
var images_history_click_tab = function(){
var tabs_box = gradioApp().getElementById("images_history_tab");
if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
tabs_box.classList.add(this.getAttribute("tabname"))
}
}
function images_history_disabled_del(){
gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
btn.setAttribute('disabled','disabled');
......@@ -43,7 +35,6 @@ function images_history_get_parent_by_tagname(item, tagname){
var parent = item.parentElement;
tagname = tagname.toUpperCase()
while(parent.tagName != tagname){
console.log(parent.tagName, tagname)
parent = parent.parentElement;
}
return parent;
......@@ -88,15 +79,15 @@ function images_history_set_image_info(button){
}
function images_history_get_current_img(tabname, image_path, files){
function images_history_get_current_img(tabname, img_index, files){
return [
gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),
image_path,
tabname,
gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),
files
];
}
function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
function images_history_delete(del_num, tabname, image_index){
image_index = parseInt(image_index);
var tab = gradioApp().getElementById(tabname + '_images_history');
var set_btn = tab.querySelector(".images_history_set_index");
......@@ -107,6 +98,7 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
}
});
var img_num = buttons.length / 2;
del_num = Math.min(img_num - image_index, del_num)
if (img_num <= del_num){
setTimeout(function(tabname){
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
......@@ -114,30 +106,28 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
} else {
var next_img
for (var i = 0; i < del_num; i++){
if (image_index + i < image_index + img_num){
buttons[image_index + i].style.display = 'none';
buttons[image_index + img_num + 1].style.display = 'none';
next_img = image_index + i + 1
}
buttons[image_index + i].style.display = 'none';
buttons[image_index + i + img_num].style.display = 'none';
next_img = image_index + i + 1
}
var bnt;
if (next_img >= img_num){
btn = buttons[image_index - del_num];
btn = buttons[image_index - 1];
} else {
btn = buttons[next_img];
}
setTimeout(function(btn){btn.click()}, 30, btn);
}
images_history_disabled_del();
return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
}
function images_history_turnpage(img_path, page_index, image_index, tabname){
function images_history_turnpage(tabname){
gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
buttons.forEach(function(elem) {
elem.style.display = 'block';
})
return [img_path, page_index, image_index, tabname];
})
}
function images_history_enable_del_buttons(){
......@@ -147,60 +137,64 @@ function images_history_enable_del_buttons(){
}
function images_history_init(){
var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
if (load_txt2img_button){
var tabnames = gradioApp().getElementById("images_history_tabnames_list")
if (tabnames){
images_history_tab_list = tabnames.querySelector("textarea").value.split(",")
for (var i in images_history_tab_list ){
tab = images_history_tab_list[i];
var tab = images_history_tab_list[i];
gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");
gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");
gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px");
}
//preload
if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){
var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
tabs_box.setAttribute("id", "images_history_tab");
var tab_btns = tabs_box.querySelectorAll("button");
for (var i in images_history_tab_list){
var tabname = images_history_tab_list[i]
tab_btns[i].setAttribute("tabname", tabname);
tab_btns[i].addEventListener('click', function(){
var tabs_box = gradioApp().getElementById("images_history_tab");
if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
tabs_box.classList.add(this.getAttribute("tabname"))
}
});
}
tab_btns[0].click()
}
var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
tabs_box.setAttribute("id", "images_history_tab");
var tab_btns = tabs_box.querySelectorAll("button");
for (var i in images_history_tab_list){
var tabname = images_history_tab_list[i]
tab_btns[i].setAttribute("tabname", tabname);
// this refreshes history upon tab switch
// until the history is known to work well, which is not the case now, we do not do this at startup
//tab_btns[i].addEventListener('click', images_history_click_tab);
}
tabs_box.classList.add(images_history_tab_list[0]);
// same as above, at page load
//load_txt2img_button.click();
} else {
setTimeout(images_history_init, 500);
}
}
var images_history_tab_list = ["txt2img", "img2img", "extras"];
var images_history_tab_list = "";
setTimeout(images_history_init, 500);
document.addEventListener("DOMContentLoaded", function() {
var mutationObserver = new MutationObserver(function(m){
for (var i in images_history_tab_list ){
let tabname = images_history_tab_list[i]
var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
buttons.forEach(function(bnt){
bnt.addEventListener('click', images_history_click_image, true);
});
// same as load_txt2img_button.click() above
/*
var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
if (cls_btn){
cls_btn.addEventListener('click', function(){
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
}, false);
}*/
}
if (images_history_tab_list != ""){
for (var i in images_history_tab_list ){
let tabname = images_history_tab_list[i]
var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
buttons.forEach(function(bnt){
bnt.addEventListener('click', images_history_click_image, true);
});
var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
if (cls_btn){
cls_btn.addEventListener('click', function(){
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
}, false);
}
}
}
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
});
// various functions for interation with ui.py not large enough to warrant putting them in separate files
function set_theme(theme){
gradioURL = window.location.href
if (!gradioURL.includes('?__theme=')) {
window.location.replace(gradioURL + '?__theme=' + theme);
}
}
function selected_gallery_index(){
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
......
......@@ -86,7 +86,24 @@ def git_clone(url, dir, name, commithash=None):
if commithash is not None:
run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
def version_check(commit):
try:
import requests
commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
if commit != "<none>" and commits['commit']['sha'] != commit:
print("--------------------------------------------------------")
print("| You are not up to date with the most recent release. |")
print("| Consider running `git pull` to update. |")
print("--------------------------------------------------------")
elif commits['commit']['sha'] == commit:
print("You are up to date with the most recent release.")
else:
print("Not a git clone, can't perform version check.")
except Exception as e:
print("versipm check failed",e)
def prepare_enviroment():
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
......@@ -110,13 +127,14 @@ def prepare_enviroment():
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
args = shlex.split(commandline_args)
sys.argv += shlex.split(commandline_args)
args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
xformers = '--xformers' in args
deepdanbooru = '--deepdanbooru' in args
ngrok = '--ngrok' in args
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
sys.argv, update_check = extract_arg(sys.argv, '--update-check')
xformers = '--xformers' in sys.argv
deepdanbooru = '--deepdanbooru' in sys.argv
ngrok = '--ngrok' in sys.argv
try:
commit = run(f"{git} rev-parse HEAD").strip()
......@@ -125,7 +143,7 @@ def prepare_enviroment():
print(f"Python {sys.version}")
print(f"Commit hash: {commit}")
if not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
......@@ -138,9 +156,15 @@ def prepare_enviroment():
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
if (not is_installed("xformers") or reinstall_xformers) and xformers:
if platform.system() == "Windows":
run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
if platform.python_version().startswith("3.10"):
run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
if not is_installed("xformers"):
exit(0)
elif platform.system() == "Linux":
run_pip("install xformers", "xformers")
......@@ -163,9 +187,10 @@ def prepare_enviroment():
run_pip(f"install -r {requirements_file}", "requirements for Web UI")
sys.argv += args
if "--exit" in args:
if update_check:
version_check(commit)
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
......
This diff is collapsed.
This diff is collapsed.
from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.sd_samplers import all_samplers
from modules.extras import run_pnginfo
import modules.shared as shared
import uvicorn
from fastapi import Body, APIRouter, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field, Json
import json
import io
import base64
from PIL import Image
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
class TextToImageResponse(BaseModel):
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: Json
info: Json
class ImageToImageResponse(BaseModel):
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: Json
info: Json
class Api:
def __init__(self, app, queue_lock):
self.router = APIRouter()
self.app = app
self.queue_lock = queue_lock
self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
def __base64_to_image(self, base64_string):
# if has a comma, deal with prefix
if "," in base64_string:
base64_string = base64_string.split(",")[1]
imgdata = base64.b64decode(base64_string)
# convert base64 to PIL image
return Image.open(io.BytesIO(imgdata))
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
sampler_index = sampler_to_index(txt2imgreq.sampler_index)
if sampler_index is None:
raise HTTPException(status_code=404, detail="Sampler not found")
populate = txt2imgreq.copy(update={ # Override __init__ params
"sd_model": shared.sd_model,
"sampler_index": sampler_index[0],
"do_not_save_samples": True,
"do_not_save_grid": True
}
)
p = StableDiffusionProcessingTxt2Img(**vars(populate))
# Override object param
with self.queue_lock:
processed = process_images(p)
b64images = []
for i in processed.images:
buffer = io.BytesIO()
i.save(buffer, format="png")
b64images.append(base64.b64encode(buffer.getvalue()))
return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
sampler_index = sampler_to_index(img2imgreq.sampler_index)
if sampler_index is None:
raise HTTPException(status_code=404, detail="Sampler not found")
init_images = img2imgreq.init_images
if init_images is None:
raise HTTPException(status_code=404, detail="Init image not found")
mask = img2imgreq.mask
if mask:
mask = self.__base64_to_image(mask)
populate = img2imgreq.copy(update={ # Override __init__ params
"sd_model": shared.sd_model,
"sampler_index": sampler_index[0],
"do_not_save_samples": True,
"do_not_save_grid": True,
"mask": mask
}
)
p = StableDiffusionProcessingImg2Img(**vars(populate))
imgs = []
for img in init_images:
img = self.__base64_to_image(img)
imgs = [img] * p.batch_size
p.init_images = imgs
# Override object param
with self.queue_lock:
processed = process_images(p)
b64images = []
for i in processed.images:
buffer = io.BytesIO()
i.save(buffer, format="png")
b64images.append(base64.b64encode(buffer.getvalue()))
return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
def extrasapi(self):
raise NotImplementedError
def pnginfoapi(self):
raise NotImplementedError
def launch(self, server_name, port):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port)
from array import array
from inflection import underscore
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, create_model
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
import inspect
API_NOT_ALLOWED = [
"self",
"kwargs",
"sd_model",
"outpath_samples",
"outpath_grids",
"sampler_index",
"do_not_save_samples",
"do_not_save_grid",
"extra_generation_params",
"overlay_images",
"do_not_reload_embeddings",
"seed_enable_extras",
"prompt_for_display",
"sampler_noise_scheduler_override",
"ddim_discretize"
]
class ModelDef(BaseModel):
"""Assistance Class for Pydantic Dynamic Model Generation"""
field: str
field_alias: str
field_type: Any
field_value: Any
class PydanticModelGenerator:
"""
Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
source_data is a snapshot of the default values produced by the class
params are the names of the actual keys required by __init__
"""
def __init__(
self,
model_name: str = None,
class_instance = None,
additional_fields = None,
):
def field_type_generator(k, v):
# field_type = str if not overrides.get(k) else overrides[k]["type"]
# print(k, v.annotation, v.default)
field_type = v.annotation
return Optional[field_type]
def merge_class_params(class_):
all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
parameters = {}
for classes in all_classes:
parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
return parameters
self._model_name = model_name
self._class_data = merge_class_params(class_instance)
self._model_def = [
ModelDef(
field=underscore(k),
field_alias=k,
field_type=field_type_generator(k, v),
field_value=v.default
)
for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
]
for fields in additional_fields:
self._model_def.append(ModelDef(
field=underscore(fields["key"]),
field_alias=fields["key"],
field_type=fields["type"],
field_value=fields["default"]))
def generate_model(self):
"""
Creates a pydantic BaseModel
from the json and overrides provided at initialization
"""
fields = {
d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
}
DynamicModel = create_model(self._model_name, **fields)
DynamicModel.__config__.allow_population_by_field_name = True
DynamicModel.__config__.allow_mutation = True
return DynamicModel
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingTxt2Img",
StableDiffusionProcessingTxt2Img,
[{"key": "sampler_index", "type": str, "default": "Euler"}]
).generate_model()
StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingImg2Img",
StableDiffusionProcessingImg2Img,
[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
).generate_model()
\ No newline at end of file
import os.path
import sys
import traceback
import PIL.Image
import numpy as np
import torch
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import devices, modelloader
from modules.bsrgan_model_arch import RRDBNet
class UpscalerBSRGAN(modules.upscaler.Upscaler):
def __init__(self, dirname):
self.name = "BSRGAN"
self.model_name = "BSRGAN 4x"
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
self.user_path = dirname
super().__init__()
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
scalers = []
if len(model_paths) == 0:
scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
if "http" in file:
name = self.model_name
else:
name = modelloader.friendly_name(file)
try:
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
scalers.append(scaler_data)
except Exception:
print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
self.scalers = scalers
def do_upscale(self, img: PIL.Image, selected_file):
torch.cuda.empty_cache()
model = self.load_model(selected_file)
if model is None:
return img
model.to(devices.device_bsrgan)
torch.cuda.empty_cache()
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(devices.device_bsrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
torch.cuda.empty_cache()
return PIL.Image.fromarray(output, 'RGB')
def load_model(self, path: str):
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
progress=True)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
return None
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
return model
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5 * 0.2 + x
class RRDB(nn.Module):
'''Residual in Residual Dense Block'''
def __init__(self, nf, gc=32):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
return out * 0.2 + x
class RRDBNet(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
super(RRDBNet, self).__init__()
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.sf = sf
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.RRDB_trunk = make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.sf==4:
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.conv_first(x)
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
if self.sf==4:
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return out
\ No newline at end of file
......@@ -50,11 +50,12 @@ def create_deepbooru_process(threshold, deepbooru_opts):
the tags.
"""
from modules import shared # prevents circular reference
shared.deepbooru_process_manager = multiprocessing.Manager()
context = multiprocessing.get_context("spawn")
shared.deepbooru_process_manager = context.Manager()
shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
shared.deepbooru_process.start()
......@@ -157,8 +158,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
# sort by reverse by likelihood and normal for alpha, and format tag text as requested
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
for weight, tag in unsorted_tags_in_theshold:
# note: tag_outformat will still have a colon if include_ranks is True
tag_outformat = tag.replace(':', ' ')
tag_outformat = tag
if use_spaces:
tag_outformat = tag_outformat.replace('_', ' ')
if use_escape:
......
import sys, os, shlex
import contextlib
import torch
from modules import errors
# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
......@@ -9,10 +8,22 @@ has_mps = getattr(torch, 'has_mps', False)
cpu = torch.device("cpu")
def extract_device_id(args, name):
for x in range(len(args)):
if name in args[x]: return args[x+1]
return None
def get_optimal_device():
if torch.cuda.is_available():
return torch.device("cuda")
from modules import shared
device_id = shared.cmd_opts.device_id
if device_id is not None:
cuda_device = f"cuda:{device_id}"
return torch.device(cuda_device)
else:
return torch.device("cuda")
if has_mps:
return torch.device("mps")
......@@ -34,7 +45,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
dtype = torch.float16
dtype_vae = torch.float16
......
......@@ -11,62 +11,109 @@ from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
def fix_model_layers(crt_model, pretrained_net):
# this code is adapted from https://github.com/xinntao/ESRGAN
if 'conv_first.weight' in pretrained_net:
return pretrained_net
if 'model.0.weight' not in pretrained_net:
is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
if is_realesrgan:
raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
else:
raise Exception("The file is not a ESRGAN model.")
crt_net = crt_model.state_dict()
load_net_clean = {}
for k, v in pretrained_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
pretrained_net = load_net_clean
tbd = []
for k, v in crt_net.items():
tbd.append(k)
# directly copy
for k, v in crt_net.items():
if k in pretrained_net and pretrained_net[k].size() == v.size():
crt_net[k] = pretrained_net[k]
tbd.remove(k)
crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
for k in tbd.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[k] = pretrained_net[ori_k]
tbd.remove(k)
crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
return crt_net
def mod2normal(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
if 'conv_first.weight' in state_dict:
crt_net = {}
items = []
for k, v in state_dict.items():
items.append(k)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
for k in items.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[ori_k] = state_dict[k]
items.remove(k)
crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
crt_net['model.3.weight'] = state_dict['upconv1.weight']
crt_net['model.3.bias'] = state_dict['upconv1.bias']
crt_net['model.6.weight'] = state_dict['upconv2.weight']
crt_net['model.6.bias'] = state_dict['upconv2.bias']
crt_net['model.8.weight'] = state_dict['HRconv.weight']
crt_net['model.8.bias'] = state_dict['HRconv.bias']
crt_net['model.10.weight'] = state_dict['conv_last.weight']
crt_net['model.10.bias'] = state_dict['conv_last.bias']
state_dict = crt_net
return state_dict
def resrgan2normal(state_dict, nb=23):
# this code is copied from https://github.com/victorca25/iNNfer
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
crt_net = {}
items = []
for k, v in state_dict.items():
items.append(k)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
for k in items.copy():
if "rdb" in k:
ori_k = k.replace('body.', 'model.1.sub.')
ori_k = ori_k.replace('.rdb', '.RDB')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[ori_k] = state_dict[k]
items.remove(k)
crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
crt_net['model.3.weight'] = state_dict['conv_up1.weight']
crt_net['model.3.bias'] = state_dict['conv_up1.bias']
crt_net['model.6.weight'] = state_dict['conv_up2.weight']
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
crt_net['model.8.weight'] = state_dict['conv_hr.weight']
crt_net['model.8.bias'] = state_dict['conv_hr.bias']
crt_net['model.10.weight'] = state_dict['conv_last.weight']
crt_net['model.10.bias'] = state_dict['conv_last.bias']
state_dict = crt_net
return state_dict
def infer_params(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
scale2x = 0
scalemin = 6
n_uplayer = 0
plus = False
for block in list(state_dict):
parts = block.split(".")
n_parts = len(parts)
if n_parts == 5 and parts[2] == "sub":
nb = int(parts[3])
elif n_parts == 3:
part_num = int(parts[1])
if (part_num > scalemin
and parts[0] == "model"
and parts[2] == "weight"):
scale2x += 1
if part_num > n_uplayer:
n_uplayer = part_num
out_nc = state_dict[block].shape[0]
if not plus and "conv1x1" in block:
plus = True
nf = state_dict["model.0.weight"].shape[0]
in_nc = state_dict["model.0.weight"].shape[1]
out_nc = out_nc
scale = 2 ** scale2x
return in_nc, out_nc, nf, nb, plus, scale
class UpscalerESRGAN(Upscaler):
def __init__(self, dirname):
......@@ -109,20 +156,39 @@ class UpscalerESRGAN(Upscaler):
print("Unable to load %s from %s" % (self.model_path, filename))
return None
pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
if "params_ema" in state_dict:
state_dict = state_dict["params_ema"]
elif "params" in state_dict:
state_dict = state_dict["params"]
num_conv = 16 if "realesr-animevideov3" in filename else 32
model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
model.load_state_dict(state_dict)
model.eval()
return model
if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
state_dict = resrgan2normal(state_dict, nb)
elif "conv_first.weight" in state_dict:
state_dict = mod2normal(state_dict)
elif "model.0.weight" not in state_dict:
raise Exception("The file is not a recognized ESRGAN model.")
in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
crt_model.load_state_dict(pretrained_net)
crt_model.eval()
model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
model.load_state_dict(state_dict)
model.eval()
return crt_model
return model
def upscale_without_tiling(model, img):
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad():
......
This diff is collapsed.
......@@ -39,9 +39,12 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
if input_dir == '':
return outputs, "Please select an input directory.", ''
image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
for img in image_list:
image = Image.open(img)
try:
image = Image.open(img)
except Exception:
continue
imageArr.append(image)
imageNameArr.append(img)
else:
......@@ -118,10 +121,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
while len(cached_images) > 2:
del cached_images[next(iter(cached_images.keys()))]
if opts.use_original_name_batch and image_name != None:
basename = os.path.splitext(os.path.basename(image_name))[0]
else:
basename = ''
images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
forced_filename=image_name if opts.use_original_name_batch else None)
images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
if opts.enable_pnginfo:
image.info = existing_pnginfo
......@@ -216,8 +223,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
if theta_func1:
for key in tqdm.tqdm(theta_1.keys()):
if 'model' in key:
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
theta_1[key] = theta_func1(theta_1[key], t2)
if key in theta_2:
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
theta_1[key] = theta_func1(theta_1[key], t2)
else:
theta_1[key] = torch.zeros_like(theta_1[key])
del theta_2, teritary_model
for key in tqdm.tqdm(theta_0.keys()):
......
......@@ -4,13 +4,22 @@ import gradio as gr
from modules.shared import script_path
from modules import shared
re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
type_of_gr_update = type(gr.update())
def quote(text):
if ',' not in str(text):
return text
text = str(text)
text = text.replace('\\', '\\\\')
text = text.replace('"', '\\"')
return f'"{text}"'
def parse_generation_parameters(x: str):
"""parses generation parameters string, the one you see in text field under the picture in UI:
```
......@@ -45,11 +54,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
else:
prompt += ("" if prompt == "" else "\n") + line
if len(prompt) > 0:
res["Prompt"] = prompt
if len(negative_prompt) > 0:
res["Negative prompt"] = negative_prompt
res["Prompt"] = prompt
res["Negative prompt"] = negative_prompt
for k, v in re_param.findall(lastline):
m = re_imagesize.match(v)
......@@ -86,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None):
else:
try:
valtype = type(output.value)
val = valtype(v)
if valtype == bool and v == "False":
val = False
else:
val = valtype(v)
res.append(gr.update(value=val))
except Exception:
res.append(gr.update())
......
This diff is collapsed.
import html
import os
import re
import gradio as gr
import modules.textual_inversion.textual_inversion
import modules.textual_inversion.preprocess
from modules import sd_hijack, shared, devices
import modules.textual_inversion.textual_inversion
from modules import devices, sd_hijack, shared
from modules.hypernetworks import hypernetwork
def create_hypernetwork(name, enable_sizes):
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
assert not os.path.exists(fn), f"file {fn} already exists"
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
if not overwrite_old:
assert not os.path.exists(fn), f"file {fn} already exists"
if type(layer_structure) == str:
layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
name=name,
enable_sizes=[int(x) for x in enable_sizes],
layer_structure=layer_structure,
activation_func=activation_func,
add_layer_norm=add_layer_norm,
use_dropout=use_dropout,
)
hypernet.save(fn)
shared.reload_hypernetworks()
......
This diff is collapsed.
......@@ -109,6 +109,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
inpainting_mask_invert=inpainting_mask_invert,
)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
......
......@@ -28,9 +28,11 @@ class InterrogateModels:
clip_preprocess = None
categories = None
dtype = None
running_on_cpu = None
def __init__(self, content_dir):
self.categories = []
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
if os.path.exists(content_dir):
for filename in os.listdir(content_dir):
......@@ -53,7 +55,11 @@ class InterrogateModels:
def load_clip_model(self):
import clip
model, preprocess = clip.load(clip_model_name)
if self.running_on_cpu:
model, preprocess = clip.load(clip_model_name, device="cpu")
else:
model, preprocess = clip.load(clip_model_name)
model.eval()
model = model.to(devices.device_interrogate)
......@@ -62,14 +68,14 @@ class InterrogateModels:
def load(self):
if self.blip_model is None:
self.blip_model = self.load_blip_model()
if not shared.cmd_opts.no_half:
if not shared.cmd_opts.no_half and not self.running_on_cpu:
self.blip_model = self.blip_model.half()
self.blip_model = self.blip_model.to(devices.device_interrogate)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.cmd_opts.no_half:
if not shared.cmd_opts.no_half and not self.running_on_cpu:
self.clip_model = self.clip_model.half()
self.clip_model = self.clip_model.to(devices.device_interrogate)
......
import torch
from modules.devices import get_optimal_device
from modules import devices
module_in_gpu = None
cpu = torch.device("cpu")
device = gpu = get_optimal_device()
def send_everything_to_cpu():
......@@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
if module_in_gpu is not None:
module_in_gpu.to(cpu)
module.to(gpu)
module.to(devices.device)
module_in_gpu = module
# see below for register_forward_pre_hook;
......@@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
# send the model to GPU. Then put modules back. the modules will be in CPU.
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
sd_model.to(device)
sd_model.to(devices.device)
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
# register hooks for those the first two models
......@@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
# so that only one of them is in GPU at a time
stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
sd_model.model.to(device)
sd_model.model.to(devices.device)
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
# install hooks for bits of third model
......
This diff is collapsed.
......@@ -275,7 +275,7 @@ re_attention = re.compile(r"""
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
......
callbacks_model_loaded = []
callbacks_ui_tabs = []
callbacks_ui_settings = []
def clear_callbacks():
callbacks_model_loaded.clear()
callbacks_ui_tabs.clear()
def model_loaded_callback(sd_model):
for callback in callbacks_model_loaded:
callback(sd_model)
def ui_tabs_callback():
res = []
for callback in callbacks_ui_tabs:
res += callback() or []
return res
def ui_settings_callback():
for callback in callbacks_ui_settings:
callback()
def on_model_loaded(callback):
"""register a function to be called when the stable diffusion model is created; the model is
passed as an argument"""
callbacks_model_loaded.append(callback)
def on_ui_tabs(callback):
"""register a function to be called when the UI is creating new tabs.
The function must either return a None, which means no new tabs to be added, or a list, where
each element is a tuple:
(gradio_component, title, elem_id)
gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
title is tab text displayed to user in the UI
elem_id is HTML id for the tab
"""
callbacks_ui_tabs.append(callback)
def on_ui_settings(callback):
"""register a function to be called before UI settings are populated; add your settings
by using shared.opts.add_option(shared.OptionInfo(...)) """
callbacks_ui_settings.append(callback)
This diff is collapsed.
......@@ -19,6 +19,7 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
def apply_optimizations():
undo_optimizations()
......@@ -167,11 +168,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
remade_tokens = remade_tokens[:last_comma]
length = len(remade_tokens)
rem = int(math.ceil(length / 75)) * 75 - length
remade_tokens += [id_end] * rem + reloc_tokens
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
if embedding is None:
remade_tokens.append(token)
multipliers.append(weight)
......@@ -223,7 +224,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def process_text_old(self, text):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
......@@ -280,7 +280,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
token_count = len(remade_tokens)
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
......@@ -290,7 +290,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
hijack_fixes.append(fixes)
batch_multipliers.append(multipliers)
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
use_old = opts.use_old_emphasis_implementation
if use_old:
......@@ -302,11 +302,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
if use_old:
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)
z = None
i = 0
while max(map(len, remade_batch_tokens)) != 0:
......@@ -320,7 +320,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
if fix[0] == i:
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
tokens = []
multipliers = []
for j in range(len(remade_batch_tokens)):
......@@ -333,19 +333,18 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
z1 = self.process_tokens(tokens, multipliers)
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
batch_multipliers = rem_multipliers
i += 1
return z
def process_tokens(self, remade_batch_tokens, batch_multipliers):
if not opts.use_old_emphasis_implementation:
remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
tokens = torch.asarray(remade_batch_tokens).to(device)
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
......@@ -385,8 +384,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, embedding in fixes:
emb = embedding.vec
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
vecs.append(tensor)
......
This diff is collapsed.
......@@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v):
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
# Divide factor of safety as there's copying and fragmentation
return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
def einsum_op(q, k, v):
if q.device.type == 'cuda':
......
......@@ -7,8 +7,9 @@ from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
from modules import shared, modelloader, devices
from modules import shared, modelloader, devices, script_callbacks
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
......@@ -20,7 +21,7 @@ checkpoints_loaded = collections.OrderedDict()
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
from transformers import logging, CLIPModel
logging.set_verbosity_error()
except Exception:
......@@ -122,13 +123,41 @@ def select_checkpoint():
return checkpoint_info
chckpoint_dict_replacements = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}
def transform_checkpoint_dict_key(k):
for text, replacement in chckpoint_dict_replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
return k
def get_state_dict_from_checkpoint(pl_sd):
if "state_dict" in pl_sd:
return pl_sd["state_dict"]
pl_sd = pl_sd["state_dict"]
sd = {}
for k, v in pl_sd.items():
new_key = transform_checkpoint_dict_key(k)
if new_key is not None:
sd[new_key] = v
pl_sd.clear()
pl_sd.update(sd)
return pl_sd
vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
def load_model_weights(model, checkpoint_info):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
......@@ -141,7 +170,7 @@ def load_model_weights(model, checkpoint_info):
print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
model.load_state_dict(sd, strict=False)
missing, extra = model.load_state_dict(sd, strict=False)
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
......@@ -160,7 +189,7 @@ def load_model_weights(model, checkpoint_info):
if os.path.exists(vae_file):
print(f"Loading VAE weights from: {vae_file}")
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
model.first_stage_model.load_state_dict(vae_dict)
model.first_stage_model.to(devices.dtype_vae)
......@@ -178,14 +207,26 @@ def load_model_weights(model, checkpoint_info):
model.sd_checkpoint_info = checkpoint_info
def load_model():
def load_model(checkpoint_info=None):
from modules import lowvram, sd_hijack
checkpoint_info = select_checkpoint()
checkpoint_info = checkpoint_info or select_checkpoint()
if checkpoint_info.config != shared.cmd_opts.config:
print(f"Loading config from: {checkpoint_info.config}")
sd_config = OmegaConf.load(checkpoint_info.config)
if should_hijack_inpainting(checkpoint_info):
# Hardcoded config for now...
sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
sd_config.model.params.use_ema = False
sd_config.model.params.conditioning_key = "hybrid"
sd_config.model.params.unet_config.params.in_channels = 9
# Create a "fake" config with a different name so that we know to unload it when switching models.
checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
do_inpainting_hijack()
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info)
......@@ -197,6 +238,9 @@ def load_model():
sd_hijack.model_hijack.hijack(sd_model)
sd_model.eval()
shared.sd_model = sd_model
script_callbacks.model_loaded_callback(sd_model)
print(f"Model loaded.")
return sd_model
......@@ -209,9 +253,9 @@ def reload_model_weights(sd_model, info=None):
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
checkpoints_loaded.clear()
shared.sd_model = load_model()
load_model(checkpoint_info)
return shared.sd_model
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
......@@ -224,6 +268,7 @@ def reload_model_weights(sd_model, info=None):
load_model_weights(sd_model, checkpoint_info)
sd_hijack.model_hijack.hijack(sd_model)
script_callbacks.model_loaded_callback(sd_model)
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
sd_model.to(devices.device)
......
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......@@ -45,7 +45,7 @@ class StyleDatabase:
if not os.path.exists(path):
return
with open(path, "r", encoding="utf8", newline='') as file:
with open(path, "r", encoding="utf-8-sig", newline='') as file:
reader = csv.DictReader(file)
for row in reader:
# Support loading old CSV format with "name, text"-columns
......@@ -79,7 +79,7 @@ class StyleDatabase:
def save_styles(self, path: str) -> None:
# Write to temporary file first, so we don't nuke the file if something goes wrong
fd, temp_path = tempfile.mkstemp(".csv")
with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
......
......@@ -83,7 +83,7 @@ class PersonalizedBase(Dataset):
self.dataset.append(entry)
assert len(self.dataset) > 1, "No images have been found in the dataset."
assert len(self.dataset) > 0, "No images have been found in the dataset."
self.length = len(self.dataset) * repeats // batch_size
self.initial_indexes = np.arange(len(self.dataset))
......@@ -91,7 +91,7 @@ class PersonalizedBase(Dataset):
self.shuffle()
def shuffle(self):
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
def create_text(self, filename_text):
text = random.choice(self.lines)
......
......@@ -5,6 +5,7 @@ import zlib
from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
from fonts.ttf import Roboto
import torch
from modules.shared import opts
class EmbeddingEncoder(json.JSONEncoder):
......@@ -133,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
from math import cos
image = srcimage.copy()
fontsize = 32
if textfont is None:
try:
textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
......@@ -150,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
draw = ImageDraw.Draw(image)
fontsize = 32
font = ImageFont.truetype(textfont, fontsize)
padding = 10
......
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......@@ -153,7 +153,7 @@ class EmbeddingDatabase:
return None, None
def create_embedding(name, num_vectors_per_token, init_text='*'):
def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
......@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
assert not os.path.exists(fn), f"file {fn} already exists"
if not overwrite_old:
assert not os.path.exists(fn), f"file {fn} already exists"
embedding = Embedding(vec, name)
embedding.step = 0
......@@ -275,6 +276,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
loss.backward()
optimizer.step()
epoch_num = embedding.step // len(ds)
epoch_step = embedding.step - (epoch_num * len(ds)) + 1
......
......@@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess
from modules import sd_hijack, shared
def create_embedding(name, initialization_text, nvpt):
filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
def create_embedding(name, initialization_text, nvpt, overwrite_old):
filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
......
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