Unverified Commit 7b7561f6 authored by MalumaDev's avatar MalumaDev Committed by GitHub

Merge branch 'master' into test_resolve_conflicts

parents 37d7ffb4 d3ffc962
# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
name: Run Linting/Formatting on Pull Requests
on:
- push
- pull_request
# See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
# if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
# pull_request:
# branches:
# - master
# branches-ignore:
# - development
jobs:
lint:
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Set up Python 3.10
uses: actions/setup-python@v3
with:
python-version: 3.10.6
- name: Install PyLint
run: |
python -m pip install --upgrade pip
pip install pylint
# This lets PyLint check to see if it can resolve imports
- name: Install dependencies
run : |
export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
python launch.py
- name: Analysing the code with pylint
run: |
pylint $(git ls-files '*.py')
# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
[MESSAGES CONTROL]
disable=C,R,W,E,I
...@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) { ...@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
window.document.addEventListener('dragover', e => { window.document.addEventListener('dragover', e => {
const target = e.composedPath()[0]; const target = e.composedPath()[0];
const imgWrap = target.closest('[data-testid="image"]'); const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap ) { if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
return; return;
} }
e.stopPropagation(); e.stopPropagation();
...@@ -53,6 +53,9 @@ window.document.addEventListener('dragover', e => { ...@@ -53,6 +53,9 @@ window.document.addEventListener('dragover', e => {
window.document.addEventListener('drop', e => { window.document.addEventListener('drop', e => {
const target = e.composedPath()[0]; const target = e.composedPath()[0];
if (target.placeholder.indexOf("Prompt") == -1) {
return;
}
const imgWrap = target.closest('[data-testid="image"]'); const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap ) { if ( !imgWrap ) {
return; return;
......
...@@ -2,6 +2,8 @@ addEventListener('keydown', (event) => { ...@@ -2,6 +2,8 @@ addEventListener('keydown', (event) => {
let target = event.originalTarget || event.composedPath()[0]; let target = event.originalTarget || event.composedPath()[0];
if (!target.hasAttribute("placeholder")) return; if (!target.hasAttribute("placeholder")) return;
if (!target.placeholder.toLowerCase().includes("prompt")) return; if (!target.placeholder.toLowerCase().includes("prompt")) return;
if (! (event.metaKey || event.ctrlKey)) return;
let plus = "ArrowUp" let plus = "ArrowUp"
let minus = "ArrowDown" let minus = "ArrowDown"
......
...@@ -16,6 +16,8 @@ titles = { ...@@ -16,6 +16,8 @@ titles = {
"\u{1f3a8}": "Add a random artist to the prompt.", "\u{1f3a8}": "Add a random artist to the prompt.",
"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
"\u{1f4c2}": "Open images output directory", "\u{1f4c2}": "Open images output directory",
"\u{1f4be}": "Save style",
"\u{1f4cb}": "Apply selected styles to current prompt",
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
...@@ -87,8 +89,8 @@ titles = { ...@@ -87,8 +89,8 @@ titles = {
"Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.", "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
"Weighted Sum": "Result = A * (1 - M) + B * M", "Weighted sum": "Result = A * (1 - M) + B * M",
"Add difference": "Result = A + (B - C) * (1 - M)", "Add difference": "Result = A + (B - C) * M",
} }
......
window.onload = (function(){
window.addEventListener('drop', e => {
const target = e.composedPath()[0];
const idx = selected_gallery_index();
if (target.placeholder.indexOf("Prompt") == -1) return;
let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
e.stopPropagation();
e.preventDefault();
const imgParent = gradioApp().getElementById(prompt_target);
const files = e.dataTransfer.files;
const fileInput = imgParent.querySelector('input[type="file"]');
if ( fileInput ) {
fileInput.files = files;
fileInput.dispatchEvent(new Event('change'));
}
});
});
var images_history_click_image = function(){
if (!this.classList.contains("transform")){
var gallery = images_history_get_parent_by_class(this, "images_history_cantainor");
var buttons = gallery.querySelectorAll(".gallery-item");
var i = 0;
var hidden_list = [];
buttons.forEach(function(e){
if (e.style.display == "none"){
hidden_list.push(i);
}
i += 1;
})
if (hidden_list.length > 0){
setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
}
}
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');
});
}
function images_history_get_parent_by_class(item, class_name){
var parent = item.parentElement;
while(!parent.classList.contains(class_name)){
parent = parent.parentElement;
}
return parent;
}
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;
}
function images_history_hide_buttons(hidden_list, gallery){
var buttons = gallery.querySelectorAll(".gallery-item");
var num = 0;
buttons.forEach(function(e){
if (e.style.display == "none"){
num += 1;
}
});
if (num == hidden_list.length){
setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
}
for( i in hidden_list){
buttons[hidden_list[i]].style.display = "none";
}
}
function images_history_set_image_info(button){
var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item");
var index = -1;
var i = 0;
buttons.forEach(function(e){
if(e == button){
index = i;
}
if(e.style.display != "none"){
i += 1;
}
});
var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
var set_btn = gallery.querySelector(".images_history_set_index");
var curr_idx = set_btn.getAttribute("img_index", index);
if (curr_idx != index) {
set_btn.setAttribute("img_index", index);
images_history_disabled_del();
}
set_btn.click();
}
function images_history_get_current_img(tabname, image_path, files){
return [
gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),
image_path,
files
];
}
function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
image_index = parseInt(image_index);
var tab = gradioApp().getElementById(tabname + '_images_history');
var set_btn = tab.querySelector(".images_history_set_index");
var buttons = [];
tab.querySelectorAll(".gallery-item").forEach(function(e){
if (e.style.display != 'none'){
buttons.push(e);
}
});
var img_num = buttons.length / 2;
if (img_num <= del_num){
setTimeout(function(tabname){
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
}, 30, tabname);
} 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
}
}
var bnt;
if (next_img >= img_num){
btn = buttons[image_index - del_num];
} 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){
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(){
gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
btn.removeAttribute('disabled');
})
}
function images_history_init(){
var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
if (load_txt2img_button){
for (var i in images_history_tab_list ){
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");
}
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"];
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);
}*/
}
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
});
// code related to showing and updating progressbar shown as the image is being made // code related to showing and updating progressbar shown as the image is being made
global_progressbars = {} global_progressbars = {}
galleries = {}
galleryObservers = {}
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
var progressbar = gradioApp().getElementById(id_progressbar) var progressbar = gradioApp().getElementById(id_progressbar)
...@@ -31,13 +33,24 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip ...@@ -31,13 +33,24 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
preview.style.width = gallery.clientWidth + "px" preview.style.width = gallery.clientWidth + "px"
preview.style.height = gallery.clientHeight + "px" preview.style.height = gallery.clientHeight + "px"
//only watch gallery if there is a generation process going on
check_gallery(id_gallery);
var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
if(!progressDiv){ if(!progressDiv){
if (skip) { if (skip) {
skip.style.display = "none" skip.style.display = "none"
} }
interrupt.style.display = "none" interrupt.style.display = "none"
//disconnect observer once generation finished, so user can close selected image if they want
if (galleryObservers[id_gallery]) {
galleryObservers[id_gallery].disconnect();
galleries[id_gallery] = null;
}
} }
} }
window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500) window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
...@@ -46,6 +59,28 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip ...@@ -46,6 +59,28 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
} }
} }
function check_gallery(id_gallery){
let gallery = gradioApp().getElementById(id_gallery)
// if gallery has no change, no need to setting up observer again.
if (gallery && galleries[id_gallery] !== gallery){
galleries[id_gallery] = gallery;
if(galleryObservers[id_gallery]){
galleryObservers[id_gallery].disconnect();
}
let prevSelectedIndex = selected_gallery_index();
galleryObservers[id_gallery] = new MutationObserver(function (){
let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
//automatically re-open previously selected index (if exists)
galleryButtons[prevSelectedIndex].click();
showGalleryImage();
}
})
galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
}
}
onUiUpdate(function(){ onUiUpdate(function(){
check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
......
...@@ -141,7 +141,7 @@ function submit_img2img(){ ...@@ -141,7 +141,7 @@ function submit_img2img(){
function ask_for_style_name(_, prompt_text, negative_prompt_text) { function ask_for_style_name(_, prompt_text, negative_prompt_text) {
name_ = prompt('Style name:') name_ = prompt('Style name:')
return name_ === null ? [null, null, null]: [name_, prompt_text, negative_prompt_text] return [name_, prompt_text, negative_prompt_text]
} }
...@@ -187,12 +187,10 @@ onUiUpdate(function(){ ...@@ -187,12 +187,10 @@ onUiUpdate(function(){
if (!txt2img_textarea) { if (!txt2img_textarea) {
txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea"); txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button")); txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
} }
if (!img2img_textarea) { if (!img2img_textarea) {
img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea"); img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button")); img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
} }
}) })
...@@ -220,14 +218,6 @@ function update_token_counter(button_id) { ...@@ -220,14 +218,6 @@ function update_token_counter(button_id) {
token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
} }
function submit_prompt(event, generate_button_id) {
if (event.altKey && event.keyCode === 13) {
event.preventDefault();
gradioApp().getElementById(generate_button_id).click();
return;
}
}
function restart_reload(){ function restart_reload(){
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>'; document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
setTimeout(function(){location.reload()},2000) setTimeout(function(){location.reload()},2000)
......
...@@ -9,6 +9,7 @@ import platform ...@@ -9,6 +9,7 @@ import platform
dir_repos = "repositories" dir_repos = "repositories"
python = sys.executable python = sys.executable
git = os.environ.get('GIT', "git") git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
def extract_arg(args, name): def extract_arg(args, name):
...@@ -57,7 +58,8 @@ def run_python(code, desc=None, errdesc=None): ...@@ -57,7 +58,8 @@ def run_python(code, desc=None, errdesc=None):
def run_pip(args, desc=None): def run_pip(args, desc=None):
return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}") 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}")
def check_run_python(code): def check_run_python(code):
...@@ -76,7 +78,7 @@ def git_clone(url, dir, name, commithash=None): ...@@ -76,7 +78,7 @@ def git_clone(url, dir, name, commithash=None):
return return
run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}") run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}") run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
return return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}") run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
......
...@@ -102,7 +102,7 @@ def get_deepbooru_tags_model(): ...@@ -102,7 +102,7 @@ def get_deepbooru_tags_model():
tags = dd.project.load_tags_from_project(model_path) tags = dd.project.load_tags_from_project(model_path)
model = dd.project.load_model_from_project( model = dd.project.load_model_from_project(
model_path, compile_model=True model_path, compile_model=False
) )
return model, tags return model, tags
......
...@@ -34,7 +34,7 @@ def enable_tf32(): ...@@ -34,7 +34,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32") errors.run(enable_tf32, "Enabling TF32")
device = 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 = get_optimal_device()
dtype = torch.float16 dtype = torch.float16
dtype_vae = torch.float16 dtype_vae = torch.float16
......
...@@ -159,24 +159,12 @@ def run_pnginfo(image): ...@@ -159,24 +159,12 @@ def run_pnginfo(image):
return '', geninfo, info return '', geninfo, info
def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name): def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
def weighted_sum(theta0, theta1, theta2, alpha): def weighted_sum(theta0, theta1, theta2, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1) return ((1 - alpha) * theta0) + (alpha * theta1)
# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
def sigmoid(theta0, theta1, theta2, alpha):
alpha = alpha * alpha * (3 - (2 * alpha))
return theta0 + ((theta1 - theta0) * alpha)
# Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
def inv_sigmoid(theta0, theta1, theta2, alpha):
import math
alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
return theta0 + ((theta1 - theta0) * alpha)
def add_difference(theta0, theta1, theta2, alpha): def add_difference(theta0, theta1, theta2, alpha):
return theta0 + (theta1 - theta2) * (1.0 - alpha) return theta0 + (theta1 - theta2) * alpha
primary_model_info = sd_models.checkpoints_list[primary_model_name] primary_model_info = sd_models.checkpoints_list[primary_model_name]
secondary_model_info = sd_models.checkpoints_list[secondary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
...@@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam ...@@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
theta_2 = None theta_2 = None
theta_funcs = { theta_funcs = {
"Weighted Sum": weighted_sum, "Weighted sum": weighted_sum,
"Sigmoid": sigmoid,
"Inverse Sigmoid": inv_sigmoid,
"Add difference": add_difference, "Add difference": add_difference,
} }
theta_func = theta_funcs[interp_method] theta_func = theta_funcs[interp_method]
...@@ -209,7 +195,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam ...@@ -209,7 +195,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
for key in tqdm.tqdm(theta_0.keys()): for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1: if 'model' in key and key in theta_1:
theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint t2 = (theta_2 or {}).get(key)
if t2 is None:
t2 = torch.zeros_like(theta_0[key])
theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
if save_as_half: if save_as_half:
theta_0[key] = theta_0[key].half() theta_0[key] = theta_0[key].half()
...@@ -222,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam ...@@ -222,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
filename = filename if custom_name == '' else (custom_name + '.ckpt') filename = filename if custom_name == '' else (custom_name + '.ckpt')
output_modelname = os.path.join(ckpt_dir, filename) output_modelname = os.path.join(ckpt_dir, filename)
......
...@@ -5,6 +5,7 @@ import os ...@@ -5,6 +5,7 @@ import os
import sys import sys
import traceback import traceback
import tqdm import tqdm
import csv
import torch import torch
...@@ -14,6 +15,7 @@ import torch ...@@ -14,6 +15,7 @@ import torch
from torch import einsum from torch import einsum
from einops import rearrange, repeat from einops import rearrange, repeat
import modules.textual_inversion.dataset import modules.textual_inversion.dataset
from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler from modules.textual_inversion.learn_schedule import LearnRateScheduler
...@@ -180,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): ...@@ -180,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
return self.to_out(out) return self.to_out(out)
def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): def stack_conds(conds):
if len(conds) == 1:
return torch.stack(conds)
# same as in reconstruct_multicond_batch
token_count = max([x.shape[0] for x in conds])
for i in range(len(conds)):
if conds[i].shape[0] != token_count:
last_vector = conds[i][-1:]
last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
conds[i] = torch.vstack([conds[i], last_vector_repeated])
return torch.stack(conds)
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
assert hypernetwork_name, 'hypernetwork not selected' assert hypernetwork_name, 'hypernetwork not selected'
path = shared.hypernetworks.get(hypernetwork_name, None) path = shared.hypernetworks.get(hypernetwork_name, None)
...@@ -209,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ...@@ -209,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"): with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
if unload: if unload:
shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.cond_stage_model.to(devices.cpu)
...@@ -233,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ...@@ -233,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, entry in pbar: for i, entries in pbar:
hypernetwork.step = i + ititial_step hypernetwork.step = i + ititial_step
scheduler.apply(optimizer, hypernetwork.step) scheduler.apply(optimizer, hypernetwork.step)
...@@ -244,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ...@@ -244,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
break break
with torch.autocast("cuda"): with torch.autocast("cuda"):
cond = entry.cond.to(devices.device) c = stack_conds([entry.cond for entry in entries]).to(devices.device)
x = entry.latent.to(devices.device) # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), cond)[0] x = torch.stack([entry.latent for entry in entries]).to(devices.device)
loss = shared.sd_model(x, c)[0]
del x del x
del cond del c
losses[hypernetwork.step % losses.shape[0]] = loss.item() losses[hypernetwork.step % losses.shape[0]] = loss.item()
...@@ -262,23 +279,39 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ...@@ -262,23 +279,39 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
hypernetwork.save(last_saved_file) hypernetwork.save(last_saved_file)
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate
})
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
optimizer.zero_grad() optimizer.zero_grad()
shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device)
p = processing.StableDiffusionProcessingTxt2Img( p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model, sd_model=shared.sd_model,
prompt=preview_text,
steps=20,
do_not_save_grid=True, do_not_save_grid=True,
do_not_save_samples=True, do_not_save_samples=True,
) )
if preview_from_txt2img:
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width
p.height = preview_height
else:
p.prompt = entries[0].cond_text
p.steps = 20
preview_text = p.prompt
processed = processing.process_images(p) processed = processing.process_images(p)
image = processed.images[0] if len(processed.images)>0 else None image = processed.images[0] if len(processed.images)>0 else None
...@@ -297,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ...@@ -297,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
<p> <p>
Loss: {losses.mean():.7f}<br/> Loss: {losses.mean():.7f}<br/>
Step: {hypernetwork.step}<br/> Step: {hypernetwork.step}<br/>
Last prompt: {html.escape(entry.cond_text)}<br/> Last prompt: {html.escape(entries[0].cond_text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/> Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/> Last saved image: {html.escape(last_saved_image)}<br/>
</p> </p>
......
import datetime import datetime
import io
import math import math
import os import os
from collections import namedtuple from collections import namedtuple
...@@ -23,6 +24,10 @@ def image_grid(imgs, batch_size=1, rows=None): ...@@ -23,6 +24,10 @@ def image_grid(imgs, batch_size=1, rows=None):
rows = opts.n_rows rows = opts.n_rows
elif opts.n_rows == 0: elif opts.n_rows == 0:
rows = batch_size rows = batch_size
elif opts.grid_prevent_empty_spots:
rows = math.floor(math.sqrt(len(imgs)))
while len(imgs) % rows != 0:
rows -= 1
else: else:
rows = math.sqrt(len(imgs)) rows = math.sqrt(len(imgs))
rows = round(rows) rows = round(rows)
...@@ -463,3 +468,22 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i ...@@ -463,3 +468,22 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
txt_fullfn = None txt_fullfn = None
return fullfn, txt_fullfn return fullfn, txt_fullfn
def image_data(data):
try:
image = Image.open(io.BytesIO(data))
textinfo = image.text["parameters"]
return textinfo, None
except Exception:
pass
try:
text = data.decode('utf8')
assert len(text) < 10000
return text, None
except Exception:
pass
return '', None
import os
import shutil
def traverse_all_files(output_dir, image_list, curr_dir=None):
curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
try:
f_list = os.listdir(curr_path)
except:
if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
image_list.append(curr_dir)
return image_list
for file in f_list:
file = file if curr_dir is None else os.path.join(curr_dir, file)
file_path = os.path.join(curr_path, file)
if file[-4:] == ".txt":
pass
elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
image_list.append(file)
else:
image_list = traverse_all_files(output_dir, image_list, file)
return image_list
def get_recent_images(dir_name, page_index, step, image_index, tabname):
page_index = int(page_index)
f_list = os.listdir(dir_name)
image_list = []
image_list = traverse_all_files(dir_name, image_list)
image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
num = 48 if tabname != "extras" else 12
max_page_index = len(image_list) // num + 1
page_index = max_page_index if page_index == -1 else page_index + step
page_index = 1 if page_index < 1 else page_index
page_index = max_page_index if page_index > max_page_index else page_index
idx_frm = (page_index - 1) * num
image_list = image_list[idx_frm:idx_frm + num]
image_index = int(image_index)
if image_index < 0 or image_index > len(image_list) - 1:
current_file = None
hidden = None
else:
current_file = image_list[int(image_index)]
hidden = os.path.join(dir_name, current_file)
return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
def first_page_click(dir_name, page_index, image_index, tabname):
return get_recent_images(dir_name, 1, 0, image_index, tabname)
def end_page_click(dir_name, page_index, image_index, tabname):
return get_recent_images(dir_name, -1, 0, image_index, tabname)
def prev_page_click(dir_name, page_index, image_index, tabname):
return get_recent_images(dir_name, page_index, -1, image_index, tabname)
def next_page_click(dir_name, page_index, image_index, tabname):
return get_recent_images(dir_name, page_index, 1, image_index, tabname)
def page_index_change(dir_name, page_index, image_index, tabname):
return get_recent_images(dir_name, page_index, 0, image_index, tabname)
def show_image_info(num, image_path, filenames):
# print(f"select image {num}")
file = filenames[int(num)]
return file, num, os.path.join(image_path, file)
def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
if name == "":
return filenames, delete_num
else:
delete_num = int(delete_num)
index = list(filenames).index(name)
i = 0
new_file_list = []
for name in filenames:
if i >= index and i < index + delete_num:
path = os.path.join(dir_name, name)
if os.path.exists(path):
print(f"Delete file {path}")
os.remove(path)
txt_file = os.path.splitext(path)[0] + ".txt"
if os.path.exists(txt_file):
os.remove(txt_file)
else:
print(f"Not exists file {path}")
else:
new_file_list.append(name)
i += 1
return new_file_list, 1
def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
if opts.outdir_samples != "":
dir_name = opts.outdir_samples
elif tabname == "txt2img":
dir_name = opts.outdir_txt2img_samples
elif tabname == "img2img":
dir_name = opts.outdir_img2img_samples
elif tabname == "extras":
dir_name = opts.outdir_extras_samples
d = dir_name.split("/")
dir_name = "/" if dir_name.startswith("/") else d[0]
for p in d[1:]:
dir_name = os.path.join(dir_name, p)
with gr.Row():
renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
first_page = gr.Button('First Page')
prev_page = gr.Button('Prev Page')
page_index = gr.Number(value=1, label="Page Index")
next_page = gr.Button('Next Page')
end_page = gr.Button('End Page')
with gr.Row(elem_id=tabname + "_images_history"):
with gr.Row():
with gr.Column(scale=2):
history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
with gr.Row():
delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
with gr.Column():
with gr.Row():
pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
pnginfo_send_to_img2img = gr.Button('Send to img2img')
with gr.Row():
with gr.Column():
img_file_info = gr.Textbox(label="Generate Info", interactive=False)
img_file_name = gr.Textbox(label="File Name", interactive=False)
with gr.Row():
# hiden items
img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
tabname_box = gr.Textbox(tabname, visible=False)
image_index = gr.Textbox(value=-1, visible=False)
set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
filenames = gr.State()
hidden = gr.Image(type="pil", visible=False)
info1 = gr.Textbox(visible=False)
info2 = gr.Textbox(visible=False)
# turn pages
gallery_inputs = [img_path, page_index, image_index, tabname_box]
gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
# page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index])
# other funcitons
set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
# pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
with gr.Blocks(analytics_enabled=False) as images_history:
with gr.Tabs() as tabs:
with gr.Tab("txt2img history"):
with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
with gr.Tab("img2img history"):
with gr.Blocks(analytics_enabled=False) as images_history_img2img:
show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
with gr.Tab("extras history"):
with gr.Blocks(analytics_enabled=False) as images_history_img2img:
show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
return images_history
...@@ -55,7 +55,7 @@ class InterrogateModels: ...@@ -55,7 +55,7 @@ class InterrogateModels:
model, preprocess = clip.load(clip_model_name) model, preprocess = clip.load(clip_model_name)
model.eval() model.eval()
model = model.to(shared.device) model = model.to(devices.device_interrogate)
return model, preprocess return model, preprocess
...@@ -65,14 +65,14 @@ class InterrogateModels: ...@@ -65,14 +65,14 @@ class InterrogateModels:
if not shared.cmd_opts.no_half: if not shared.cmd_opts.no_half:
self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.half()
self.blip_model = self.blip_model.to(shared.device) self.blip_model = self.blip_model.to(devices.device_interrogate)
if self.clip_model is None: if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model() self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.cmd_opts.no_half: if not shared.cmd_opts.no_half:
self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.half()
self.clip_model = self.clip_model.to(shared.device) self.clip_model = self.clip_model.to(devices.device_interrogate)
self.dtype = next(self.clip_model.parameters()).dtype self.dtype = next(self.clip_model.parameters()).dtype
...@@ -99,11 +99,11 @@ class InterrogateModels: ...@@ -99,11 +99,11 @@ class InterrogateModels:
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)] text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
top_count = min(top_count, len(text_array)) top_count = min(top_count, len(text_array))
text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device) text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype) text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = torch.zeros((1, len(text_array))).to(shared.device) similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
for i in range(image_features.shape[0]): for i in range(image_features.shape[0]):
similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1) similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
similarity /= image_features.shape[0] similarity /= image_features.shape[0]
...@@ -116,7 +116,7 @@ class InterrogateModels: ...@@ -116,7 +116,7 @@ class InterrogateModels:
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(), transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
with torch.no_grad(): with torch.no_grad():
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length) caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
...@@ -140,7 +140,7 @@ class InterrogateModels: ...@@ -140,7 +140,7 @@ class InterrogateModels:
res = caption res = caption
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
with torch.no_grad(), precision_scope("cuda"): with torch.no_grad(), precision_scope("cuda"):
......
...@@ -145,9 +145,8 @@ class Processed: ...@@ -145,9 +145,8 @@ class Processed:
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
self.subseed = int( self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
self.all_prompts = all_prompts or [self.prompt] self.all_prompts = all_prompts or [self.prompt]
self.all_seeds = all_seeds or [self.seed] self.all_seeds = all_seeds or [self.seed]
...@@ -541,16 +540,15 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh ...@@ -541,16 +540,15 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None sampler = None
firstphase_width = 0
firstphase_height = 0
firstphase_width_truncated = 0
firstphase_height_truncated = 0
def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs): def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
super().__init__(**kwargs) super().__init__(**kwargs)
self.enable_hr = enable_hr self.enable_hr = enable_hr
self.scale_latent = scale_latent
self.denoising_strength = denoising_strength self.denoising_strength = denoising_strength
self.firstphase_width = firstphase_width
self.firstphase_height = firstphase_height
self.truncate_x = 0
self.truncate_y = 0
def init(self, all_prompts, all_seeds, all_subseeds): def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr: if self.enable_hr:
...@@ -559,14 +557,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): ...@@ -559,14 +557,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else: else:
state.job_count = state.job_count * 2 state.job_count = state.job_count * 2
desired_pixel_count = 512 * 512 if self.firstphase_width == 0 or self.firstphase_height == 0:
actual_pixel_count = self.width * self.height desired_pixel_count = 512 * 512
scale = math.sqrt(desired_pixel_count / actual_pixel_count) actual_pixel_count = self.width * self.height
scale = math.sqrt(desired_pixel_count / actual_pixel_count)
self.firstphase_width = math.ceil(scale * self.width / 64) * 64
self.firstphase_height = math.ceil(scale * self.height / 64) * 64
firstphase_width_truncated = int(scale * self.width)
firstphase_height_truncated = int(scale * self.height)
else:
width_ratio = self.width / self.firstphase_width
height_ratio = self.height / self.firstphase_height
if width_ratio > height_ratio:
firstphase_width_truncated = self.firstphase_width
firstphase_height_truncated = self.firstphase_width * self.height / self.width
else:
firstphase_width_truncated = self.firstphase_height * self.width / self.height
firstphase_height_truncated = self.firstphase_height
self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
self.firstphase_width = math.ceil(scale * self.width / 64) * 64
self.firstphase_height = math.ceil(scale * self.height / 64) * 64
self.firstphase_width_truncated = int(scale * self.width)
self.firstphase_height_truncated = int(scale * self.height)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
...@@ -585,37 +600,27 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): ...@@ -585,37 +600,27 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
seed_resize_from_w=self.seed_resize_from_w, p=self) seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2, if opts.use_scale_latent_for_hires_fix:
truncate_x // 2:samples.shape[3] - truncate_x // 2] samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
mode="bilinear")
else: else:
decoded_samples = decode_first_stage(self.sd_model, samples) decoded_samples = decode_first_stage(self.sd_model, samples)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": batch_images = []
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), for i, x_sample in enumerate(lowres_samples):
mode="bilinear") x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
else: x_sample = x_sample.astype(np.uint8)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) image = Image.fromarray(x_sample)
image = images.resize_image(0, image, self.width, self.height)
batch_images = [] image = np.array(image).astype(np.float32) / 255.0
for i, x_sample in enumerate(lowres_samples): image = np.moveaxis(image, 2, 0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) batch_images.append(image)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample) decoded_samples = torch.from_numpy(np.array(batch_images))
image = images.resize_image(0, image, self.width, self.height) decoded_samples = decoded_samples.to(shared.device)
image = np.array(image).astype(np.float32) / 255.0 decoded_samples = 2. * decoded_samples - 1.
image = np.moveaxis(image, 2, 0)
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device)
decoded_samples = 2. * decoded_samples - 1.
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
......
...@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs): ...@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs):
if not shared.cmd_opts.disable_safe_unpickle: if not shared.cmd_opts.disable_safe_unpickle:
check_pt(filename) check_pt(filename)
except pickle.UnpicklingError:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
return None
except Exception: except Exception:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr) print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr) print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
return None return None
return unsafe_torch_load(filename, *args, **kwargs) return unsafe_torch_load(filename, *args, **kwargs)
......
import glob import collections
import os.path import os.path
import sys import sys
from collections import namedtuple from collections import namedtuple
...@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir)) ...@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
checkpoints_list = {} checkpoints_list = {}
checkpoints_loaded = collections.OrderedDict()
try: try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
...@@ -132,38 +133,45 @@ def load_model_weights(model, checkpoint_info): ...@@ -132,38 +133,45 @@ def load_model_weights(model, checkpoint_info):
checkpoint_file = checkpoint_info.filename checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash sd_model_hash = checkpoint_info.hash
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") if checkpoint_info not in checkpoints_loaded:
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
pl_sd = torch.load(checkpoint_file, map_location="cpu") pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
if "global_step" in pl_sd: if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}") print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd) sd = get_state_dict_from_checkpoint(pl_sd)
model.load_state_dict(sd, strict=False)
model.load_state_dict(sd, strict=False) if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
if shared.cmd_opts.opt_channelslast: if not shared.cmd_opts.no_half:
model.to(memory_format=torch.channels_last) model.half()
if not shared.cmd_opts.no_half: devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
model.half() devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
vae_file = shared.cmd_opts.vae_path
if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: if os.path.exists(vae_file):
vae_file = shared.cmd_opts.vae_path 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"}
model.first_stage_model.load_state_dict(vae_dict)
if os.path.exists(vae_file): model.first_stage_model.to(devices.dtype_vae)
print(f"Loading VAE weights from: {vae_file}")
vae_ckpt = torch.load(vae_file, map_location="cpu")
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
model.first_stage_model.load_state_dict(vae_dict) checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
model.first_stage_model.to(devices.dtype_vae) checkpoints_loaded.popitem(last=False) # LRU
else:
print(f"Loading weights [{sd_model_hash}] from cache")
checkpoints_loaded.move_to_end(checkpoint_info)
model.load_state_dict(checkpoints_loaded[checkpoint_info])
model.sd_model_hash = sd_model_hash model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file model.sd_model_checkpoint = checkpoint_file
...@@ -202,6 +210,7 @@ def reload_model_weights(sd_model, info=None): ...@@ -202,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
return return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config: if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
checkpoints_loaded.clear()
shared.sd_model = load_model() shared.sd_model = load_model()
return shared.sd_model return shared.sd_model
......
...@@ -36,6 +36,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_ ...@@ -36,6 +36,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram") parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
...@@ -56,7 +57,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en ...@@ -56,7 +57,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
...@@ -78,10 +79,11 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl ...@@ -78,10 +79,11 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
cmd_opts = parser.parse_args() cmd_opts = parser.parse_args()
devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
device = devices.device device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu"
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
...@@ -184,6 +186,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" ...@@ -184,6 +186,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_format": OptionInfo('png', 'File format for grids'), "grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
...@@ -224,6 +227,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { ...@@ -224,6 +227,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
"SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}), "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
"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]}),
"use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
})) }))
options_templates.update(options_section(('face-restoration', "Face restoration"), { options_templates.update(options_section(('face-restoration', "Face restoration"), {
...@@ -242,11 +246,13 @@ options_templates.update(options_section(('training', "Training"), { ...@@ -242,11 +246,13 @@ options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
})) }))
options_templates.update(options_section(('sd', "Stable Diffusion"), { options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
...@@ -260,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { ...@@ -260,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"filter_nsfw": OptionInfo(False, "Filter NSFW content"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
})) }))
options_templates.update(options_section(('interrogate', "Interrogate Options"), { options_templates.update(options_section(('interrogate', "Interrogate Options"), {
...@@ -288,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), { ...@@ -288,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), {
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."), "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
})) }))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), { options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
......
...@@ -24,11 +24,12 @@ class DatasetEntry: ...@@ -24,11 +24,12 @@ class DatasetEntry:
class PersonalizedBase(Dataset): class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
self.placeholder_token = placeholder_token self.placeholder_token = placeholder_token
self.batch_size = batch_size
self.width = width self.width = width
self.height = height self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.flip = transforms.RandomHorizontalFlip(p=flip_p)
...@@ -78,13 +79,14 @@ class PersonalizedBase(Dataset): ...@@ -78,13 +79,14 @@ class PersonalizedBase(Dataset):
if include_cond: if include_cond:
entry.cond_text = self.create_text(filename_text) entry.cond_text = self.create_text(filename_text)
entry.cond = cond_model([entry.cond_text]).to(devices.cpu) entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry) self.dataset.append(entry)
self.length = len(self.dataset) * repeats assert len(self.dataset) > 1, "No images have been found in the dataset."
self.length = len(self.dataset) * repeats // batch_size
self.initial_indexes = np.arange(self.length) % len(self.dataset) self.initial_indexes = np.arange(len(self.dataset))
self.indexes = None self.indexes = None
self.shuffle() self.shuffle()
...@@ -101,13 +103,19 @@ class PersonalizedBase(Dataset): ...@@ -101,13 +103,19 @@ class PersonalizedBase(Dataset):
return self.length return self.length
def __getitem__(self, i): def __getitem__(self, i):
if i % len(self.dataset) == 0: res = []
self.shuffle()
index = self.indexes[i % len(self.indexes)] for j in range(self.batch_size):
entry = self.dataset[index] position = i * self.batch_size + j
if position % len(self.indexes) == 0:
self.shuffle()
if entry.cond is None: index = self.indexes[position % len(self.indexes)]
entry.cond_text = self.create_text(entry.filename_text) entry = self.dataset[index]
return entry if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
res.append(entry)
return res
...@@ -6,6 +6,7 @@ import torch ...@@ -6,6 +6,7 @@ import torch
import tqdm import tqdm
import html import html
import datetime import datetime
import csv
from PIL import Image, PngImagePlugin from PIL import Image, PngImagePlugin
...@@ -172,15 +173,33 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): ...@@ -172,15 +173,33 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn return fn
def batched(dataset, total, n=1): def write_loss(log_directory, filename, step, epoch_len, values):
for ndx in range(0, total, n): if shared.opts.training_write_csv_every == 0:
yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] return
if step % shared.opts.training_write_csv_every != 0:
return
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding,
preview_image_prompt, batch_size=1, with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
gradient_accumulation=1): csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
if write_csv_header:
csv_writer.writeheader()
epoch = step // epoch_len
epoch_step = step - epoch * epoch_len
csv_writer.writerow({
"step": step + 1,
"epoch": epoch + 1,
"epoch_step": epoch_step + 1,
**values,
})
def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
assert embedding_name, 'embedding not selected' assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..." shared.state.textinfo = "Initializing textual inversion training..."
...@@ -212,11 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini ...@@ -212,11 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"): with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
height=training_height,
repeats=shared.opts.training_image_repeats_per_epoch,
placeholder_token=embedding_name, model=shared.sd_model,
device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack hijack = sd_hijack.model_hijack
...@@ -235,8 +250,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini ...@@ -235,8 +250,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step) pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, entry in pbar: for i, entries in pbar:
embedding.step = i + ititial_step embedding.step = i + ititial_step
scheduler.apply(optimizer, embedding.step) scheduler.apply(optimizer, embedding.step)
...@@ -247,11 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini ...@@ -247,11 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
break break
with torch.autocast("cuda"): with torch.autocast("cuda"):
c = cond_model([e.cond_text for e in entry]) c = cond_model([entry.cond_text for entry in entries])
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
x = torch.stack([e.latent for e in entry]).to(devices.device)
loss = shared.sd_model(x, c)[0] loss = shared.sd_model(x, c)[0]
del x del x
losses[embedding.step % losses.shape[0]] = loss.item() losses[embedding.step % losses.shape[0]] = loss.item()
...@@ -271,21 +284,37 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini ...@@ -271,21 +284,37 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
embedding.save(last_saved_file) embedding.save(last_saved_file)
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate
})
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt
p = processing.StableDiffusionProcessingTxt2Img( p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model, sd_model=shared.sd_model,
prompt=preview_text,
steps=20,
height=training_height,
width=training_width,
do_not_save_grid=True, do_not_save_grid=True,
do_not_save_samples=True, do_not_save_samples=True,
) )
if preview_from_txt2img:
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width
p.height = preview_height
else:
p.prompt = entries[0].cond_text
p.steps = 20
p.width = training_width
p.height = training_height
preview_text = p.prompt
processed = processing.process_images(p) processed = processing.process_images(p)
image = processed.images[0] image = processed.images[0]
...@@ -320,7 +349,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini ...@@ -320,7 +349,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
<p> <p>
Loss: {losses.mean():.7f}<br/> Loss: {losses.mean():.7f}<br/>
Step: {embedding.step}<br/> Step: {embedding.step}<br/>
Last prompt: {html.escape(entry[-1].cond_text)}<br/> Last prompt: {html.escape(entries[0].cond_text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/> Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/> Last saved image: {html.escape(last_saved_image)}<br/>
</p> </p>
......
...@@ -6,18 +6,13 @@ import modules.processing as processing ...@@ -6,18 +6,13 @@ import modules.processing as processing
from modules.ui import plaintext_to_html from modules.ui import plaintext_to_html
def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0,
restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float,
aesthetic_lr=0,
aesthetic_weight=0, aesthetic_steps=0, aesthetic_weight=0, aesthetic_steps=0,
aesthetic_imgs=None, aesthetic_imgs=None,
aesthetic_slerp=False, aesthetic_slerp=False,
aesthetic_imgs_text="", aesthetic_imgs_text="",
aesthetic_slerp_angle=0.15, aesthetic_slerp_angle=0.15,
aesthetic_text_negative=False, aesthetic_text_negative=False, *args):
*args):
p = StableDiffusionProcessingTxt2Img( p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model, sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
...@@ -41,8 +36,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: ...@@ -41,8 +36,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
restore_faces=restore_faces, restore_faces=restore_faces,
tiling=tiling, tiling=tiling,
enable_hr=enable_hr, enable_hr=enable_hr,
scale_latent=scale_latent if enable_hr else None,
denoising_strength=denoising_strength if enable_hr else None, denoising_strength=denoising_strength if enable_hr else None,
firstphase_width=firstphase_width if enable_hr else None,
firstphase_height=firstphase_height if enable_hr else None,
) )
if cmd_opts.enable_console_prompts: if cmd_opts.enable_console_prompts:
......
This diff is collapsed.
...@@ -2,7 +2,7 @@ transformers==4.19.2 ...@@ -2,7 +2,7 @@ transformers==4.19.2
diffusers==0.3.0 diffusers==0.3.0
basicsr==1.4.2 basicsr==1.4.2
gfpgan==1.3.8 gfpgan==1.3.8
gradio==3.4.1 gradio==3.5
numpy==1.23.3 numpy==1.23.3
Pillow==9.2.0 Pillow==9.2.0
realesrgan==0.3.0 realesrgan==0.3.0
......
...@@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() { ...@@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() {
document.addEventListener('keydown', function(e) { document.addEventListener('keydown', function(e) {
var handled = false; var handled = false;
if (e.key !== undefined) { if (e.key !== undefined) {
if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true; if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
} else if (e.keyCode !== undefined) { } else if (e.keyCode !== undefined) {
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true; if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
} }
if (handled) { if (handled) {
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]'); button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
......
import copy
import math import math
import os import os
import sys import sys
import traceback import traceback
import shlex
import modules.scripts as scripts import modules.scripts as scripts
import gradio as gr import gradio as gr
...@@ -10,6 +12,75 @@ from modules.processing import Processed, process_images ...@@ -10,6 +12,75 @@ from modules.processing import Processed, process_images
from PIL import Image from PIL import Image
from modules.shared import opts, cmd_opts, state from modules.shared import opts, cmd_opts, state
def process_string_tag(tag):
return tag
def process_int_tag(tag):
return int(tag)
def process_float_tag(tag):
return float(tag)
def process_boolean_tag(tag):
return True if (tag == "true") else False
prompt_tags = {
"sd_model": None,
"outpath_samples": process_string_tag,
"outpath_grids": process_string_tag,
"prompt_for_display": process_string_tag,
"prompt": process_string_tag,
"negative_prompt": process_string_tag,
"styles": process_string_tag,
"seed": process_int_tag,
"subseed_strength": process_float_tag,
"subseed": process_int_tag,
"seed_resize_from_h": process_int_tag,
"seed_resize_from_w": process_int_tag,
"sampler_index": process_int_tag,
"batch_size": process_int_tag,
"n_iter": process_int_tag,
"steps": process_int_tag,
"cfg_scale": process_float_tag,
"width": process_int_tag,
"height": process_int_tag,
"restore_faces": process_boolean_tag,
"tiling": process_boolean_tag,
"do_not_save_samples": process_boolean_tag,
"do_not_save_grid": process_boolean_tag
}
def cmdargs(line):
args = shlex.split(line)
pos = 0
res = {}
while pos < len(args):
arg = args[pos]
assert arg.startswith("--"), f'must start with "--": {arg}'
tag = arg[2:]
func = prompt_tags.get(tag, None)
assert func, f'unknown commandline option: {arg}'
assert pos+1 < len(args), f'missing argument for command line option {arg}'
val = args[pos+1]
res[tag] = func(val)
pos += 2
return res
class Script(scripts.Script): class Script(scripts.Script):
def title(self): def title(self):
return "Prompts from file or textbox" return "Prompts from file or textbox"
...@@ -32,26 +103,48 @@ class Script(scripts.Script): ...@@ -32,26 +103,48 @@ class Script(scripts.Script):
return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ] return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str): def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
if (checkbox_txt): if checkbox_txt:
lines = [x.strip() for x in prompt_txt.splitlines()] lines = [x.strip() for x in prompt_txt.splitlines()]
else: else:
lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")] lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
lines = [x for x in lines if len(x) > 0] lines = [x for x in lines if len(x) > 0]
img_count = len(lines) * p.n_iter
batch_count = math.ceil(img_count / p.batch_size)
loop_count = math.ceil(batch_count / p.n_iter)
print(f"Will process {img_count} images in {batch_count} batches.")
p.do_not_save_grid = True p.do_not_save_grid = True
state.job_count = batch_count job_count = 0
jobs = []
for line in lines:
if "--" in line:
try:
args = cmdargs(line)
except Exception:
print(f"Error parsing line [line] as commandline:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
args = {"prompt": line}
else:
args = {"prompt": line}
n_iter = args.get("n_iter", 1)
if n_iter != 1:
job_count += n_iter
else:
job_count += 1
jobs.append(args)
print(f"Will process {len(lines)} lines in {job_count} jobs.")
state.job_count = job_count
images = [] images = []
for loop_no in range(loop_count): for n, args in enumerate(jobs):
state.job = f"{loop_no + 1} out of {loop_count}" state.job = f"{state.job_no + 1} out of {state.job_count}"
p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
proc = process_images(p) copy_p = copy.copy(p)
for k, v in args.items():
setattr(copy_p, k, v)
proc = process_images(copy_p)
images += proc.images images += proc.images
return Processed(p, images, p.seed, "") return Processed(p, images, p.seed, "")
...@@ -12,7 +12,7 @@ import gradio as gr ...@@ -12,7 +12,7 @@ import gradio as gr
from modules import images from modules import images
from modules.hypernetworks import hypernetwork from modules.hypernetworks import hypernetwork
from modules.processing import process_images, Processed, get_correct_sampler from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
from modules.shared import opts, cmd_opts, state from modules.shared import opts, cmd_opts, state
import modules.shared as shared import modules.shared as shared
import modules.sd_samplers import modules.sd_samplers
...@@ -176,7 +176,7 @@ axis_options = [ ...@@ -176,7 +176,7 @@ axis_options = [
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None), AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None), AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None), AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), # as it is now all AxisOptionImg2Img items must go after AxisOption ones AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
] ]
...@@ -338,7 +338,7 @@ class Script(scripts.Script): ...@@ -338,7 +338,7 @@ class Script(scripts.Script):
ys = process_axis(y_opt, y_values) ys = process_axis(y_opt, y_values)
def fix_axis_seeds(axis_opt, axis_list): def fix_axis_seeds(axis_opt, axis_list):
if axis_opt.label == 'Seed': if axis_opt.label in ['Seed','Var. seed']:
return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list] return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
else: else:
return axis_list return axis_list
...@@ -354,6 +354,9 @@ class Script(scripts.Script): ...@@ -354,6 +354,9 @@ class Script(scripts.Script):
else: else:
total_steps = p.steps * len(xs) * len(ys) total_steps = p.steps * len(xs) * len(ys)
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
total_steps *= 2
print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})") print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
shared.total_tqdm.updateTotal(total_steps * p.n_iter) shared.total_tqdm.updateTotal(total_steps * p.n_iter)
......
...@@ -115,7 +115,7 @@ ...@@ -115,7 +115,7 @@
padding: 0.4em 0; padding: 0.4em 0;
} }
#roll, #paste{ #roll, #paste, #style_create, #style_apply{
min-width: 2em; min-width: 2em;
min-height: 2em; min-height: 2em;
max-width: 2em; max-width: 2em;
...@@ -126,14 +126,14 @@ ...@@ -126,14 +126,14 @@
margin: 0.1em 0; margin: 0.1em 0;
} }
#style_apply, #style_create, #interrogate{ #interrogate_col{
margin: 0.75em 0.25em 0.25em 0.25em; min-width: 0 !important;
min-width: 5em; max-width: 8em !important;
} }
#interrogate, #deepbooru{
#style_apply, #style_create, #deepbooru{ margin: 0em 0.25em 0.9em 0.25em;
margin: 0.75em 0.25em 0.25em 0.25em; min-width: 8em;
min-width: 5em; max-width: 8em;
} }
#style_pos_col, #style_neg_col{ #style_pos_col, #style_neg_col{
...@@ -167,18 +167,6 @@ button{ ...@@ -167,18 +167,6 @@ button{
align-self: stretch !important; align-self: stretch !important;
} }
#prompt, #negative_prompt{
border: none !important;
}
#prompt textarea, #negative_prompt textarea{
border: none !important;
}
#img2maskimg .h-60{
height: 30rem;
}
.overflow-hidden, .gr-panel{ .overflow-hidden, .gr-panel{
overflow: visible !important; overflow: visible !important;
} }
...@@ -451,10 +439,6 @@ input[type="range"]{ ...@@ -451,10 +439,6 @@ input[type="range"]{
--tw-bg-opacity: 0 !important; --tw-bg-opacity: 0 !important;
} }
#img2img_image div.h-60{
height: 480px;
}
#context-menu{ #context-menu{
z-index:9999; z-index:9999;
position:absolute; position:absolute;
...@@ -529,3 +513,11 @@ canvas[key="mask"] { ...@@ -529,3 +513,11 @@ canvas[key="mask"] {
.row.gr-compact{ .row.gr-compact{
overflow: visible; overflow: visible;
} }
#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img
{
height: 480px !important;
max-height: 480px !important;
min-height: 480px !important;
}
\ No newline at end of file
...@@ -82,8 +82,8 @@ then ...@@ -82,8 +82,8 @@ then
clone_dir="${PWD##*/}" clone_dir="${PWD##*/}"
fi fi
# Check prequisites # Check prerequisites
for preq in git python3 for preq in "${GIT}" "${python_cmd}"
do do
if ! hash "${preq}" &>/dev/null if ! hash "${preq}" &>/dev/null
then then
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment