Merge branch 'master' into cuda-device-id-selection
This commit is contained in:
AUTOMATIC1111
GitHub
commit
1fa53dab2c
17 changed files with 736 additions and 616 deletions
3
.gitignore
vendored
3
.gitignore
vendored
|
|
@ -27,4 +27,5 @@ __pycache__
|
|||
notification.mp3
|
||||
/SwinIR
|
||||
/textual_inversion
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||||
.vscode
|
||||
.vscode
|
||||
/extensions
|
||||
|
|
|
|||
11
README.md
11
README.md
|
|
@ -83,8 +83,17 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- Estimated completion time in progress bar
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||||
- API
|
||||
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
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- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
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||||
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
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||||
## Where are Aesthetic Gradients?!?!
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||||
Aesthetic Gradients are now an extension. You can install it using git:
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||||
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```commandline
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||||
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
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||||
```
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||||
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After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
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the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
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||||
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||||
## Installation and Running
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||||
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
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|
|
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|||
0
extensions/put extension here.txt
Normal file
0
extensions/put extension here.txt
Normal file
|
|
@ -17,14 +17,6 @@ var images_history_click_image = function(){
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|||
images_history_set_image_info(this);
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||||
}
|
||||
|
||||
var images_history_click_tab = function(){
|
||||
var tabs_box = gradioApp().getElementById("images_history_tab");
|
||||
if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
|
||||
gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
|
||||
tabs_box.classList.add(this.getAttribute("tabname"))
|
||||
}
|
||||
}
|
||||
|
||||
function images_history_disabled_del(){
|
||||
gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
|
||||
btn.setAttribute('disabled','disabled');
|
||||
|
|
@ -43,7 +35,6 @@ function images_history_get_parent_by_tagname(item, tagname){
|
|||
var parent = item.parentElement;
|
||||
tagname = tagname.toUpperCase()
|
||||
while(parent.tagName != tagname){
|
||||
console.log(parent.tagName, tagname)
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||||
parent = parent.parentElement;
|
||||
}
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||||
return parent;
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||||
|
|
@ -88,15 +79,15 @@ function images_history_set_image_info(button){
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|||
|
||||
}
|
||||
|
||||
function images_history_get_current_img(tabname, image_path, files){
|
||||
function images_history_get_current_img(tabname, img_index, files){
|
||||
return [
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||||
gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),
|
||||
image_path,
|
||||
tabname,
|
||||
gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),
|
||||
files
|
||||
];
|
||||
}
|
||||
|
||||
function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
|
||||
function images_history_delete(del_num, tabname, image_index){
|
||||
image_index = parseInt(image_index);
|
||||
var tab = gradioApp().getElementById(tabname + '_images_history');
|
||||
var set_btn = tab.querySelector(".images_history_set_index");
|
||||
|
|
@ -107,6 +98,7 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
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|||
}
|
||||
});
|
||||
var img_num = buttons.length / 2;
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||||
del_num = Math.min(img_num - image_index, del_num)
|
||||
if (img_num <= del_num){
|
||||
setTimeout(function(tabname){
|
||||
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
|
||||
|
|
@ -114,30 +106,28 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
|
|||
} else {
|
||||
var next_img
|
||||
for (var i = 0; i < del_num; i++){
|
||||
if (image_index + i < image_index + img_num){
|
||||
buttons[image_index + i].style.display = 'none';
|
||||
buttons[image_index + img_num + 1].style.display = 'none';
|
||||
next_img = image_index + i + 1
|
||||
}
|
||||
buttons[image_index + i].style.display = 'none';
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||||
buttons[image_index + i + img_num].style.display = 'none';
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next_img = image_index + i + 1
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||||
}
|
||||
var bnt;
|
||||
if (next_img >= img_num){
|
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btn = buttons[image_index - del_num];
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btn = buttons[image_index - 1];
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} else {
|
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btn = buttons[next_img];
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||||
}
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setTimeout(function(btn){btn.click()}, 30, btn);
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}
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images_history_disabled_del();
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return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
|
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|
||||
}
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|
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function images_history_turnpage(img_path, page_index, image_index, tabname){
|
||||
function images_history_turnpage(tabname){
|
||||
gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
|
||||
var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
|
||||
buttons.forEach(function(elem) {
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elem.style.display = 'block';
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||||
})
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||||
return [img_path, page_index, image_index, tabname];
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})
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}
|
||||
|
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function images_history_enable_del_buttons(){
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|
|
@ -147,60 +137,64 @@ function images_history_enable_del_buttons(){
|
|||
}
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||||
|
||||
function images_history_init(){
|
||||
var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
|
||||
if (load_txt2img_button){
|
||||
var tabnames = gradioApp().getElementById("images_history_tabnames_list")
|
||||
if (tabnames){
|
||||
images_history_tab_list = tabnames.querySelector("textarea").value.split(",")
|
||||
for (var i in images_history_tab_list ){
|
||||
tab = images_history_tab_list[i];
|
||||
var tab = images_history_tab_list[i];
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gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
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||||
gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
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gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
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gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");
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||||
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gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");
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gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px");
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}
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var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
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tabs_box.setAttribute("id", "images_history_tab");
|
||||
var tab_btns = tabs_box.querySelectorAll("button");
|
||||
for (var i in images_history_tab_list){
|
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var tabname = images_history_tab_list[i]
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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);
|
||||
}
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tabs_box.classList.add(images_history_tab_list[0]);
|
||||
|
||||
// same as above, at page load
|
||||
//load_txt2img_button.click();
|
||||
//preload
|
||||
if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){
|
||||
var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
|
||||
tabs_box.setAttribute("id", "images_history_tab");
|
||||
var tab_btns = tabs_box.querySelectorAll("button");
|
||||
for (var i in images_history_tab_list){
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||||
var tabname = images_history_tab_list[i]
|
||||
tab_btns[i].setAttribute("tabname", tabname);
|
||||
tab_btns[i].addEventListener('click', function(){
|
||||
var tabs_box = gradioApp().getElementById("images_history_tab");
|
||||
if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
|
||||
gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
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||||
tabs_box.classList.add(this.getAttribute("tabname"))
|
||||
}
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||||
});
|
||||
}
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||||
tab_btns[0].click()
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||||
}
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||||
} else {
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||||
setTimeout(images_history_init, 500);
|
||||
}
|
||||
}
|
||||
|
||||
var images_history_tab_list = ["txt2img", "img2img", "extras"];
|
||||
var images_history_tab_list = "";
|
||||
setTimeout(images_history_init, 500);
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
var mutationObserver = new MutationObserver(function(m){
|
||||
for (var i in images_history_tab_list ){
|
||||
let tabname = images_history_tab_list[i]
|
||||
var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
|
||||
buttons.forEach(function(bnt){
|
||||
bnt.addEventListener('click', images_history_click_image, true);
|
||||
});
|
||||
if (images_history_tab_list != ""){
|
||||
for (var i in images_history_tab_list ){
|
||||
let tabname = images_history_tab_list[i]
|
||||
var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
|
||||
buttons.forEach(function(bnt){
|
||||
bnt.addEventListener('click', images_history_click_image, true);
|
||||
});
|
||||
|
||||
// same as load_txt2img_button.click() above
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||||
/*
|
||||
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);
|
||||
}*/
|
||||
var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
|
||||
if (cls_btn){
|
||||
cls_btn.addEventListener('click', function(){
|
||||
gradioApp().getElementById(tabname + '_images_history_renew_page').click();
|
||||
}, false);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
|
||||
|
||||
mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
|
||||
});
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,241 +0,0 @@
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import copy
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import itertools
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import os
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from pathlib import Path
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||||
import html
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||||
import gc
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||||
|
||||
import gradio as gr
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import torch
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from PIL import Image
|
||||
from torch import optim
|
||||
|
||||
from modules import shared
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||||
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
|
||||
from tqdm.auto import tqdm, trange
|
||||
from modules.shared import opts, device
|
||||
|
||||
|
||||
def get_all_images_in_folder(folder):
|
||||
return [os.path.join(folder, f) for f in os.listdir(folder) if
|
||||
os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
|
||||
|
||||
|
||||
def check_is_valid_image_file(filename):
|
||||
return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
|
||||
|
||||
|
||||
def batched(dataset, total, n=1):
|
||||
for ndx in range(0, total, n):
|
||||
yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
|
||||
|
||||
|
||||
def iter_to_batched(iterable, n=1):
|
||||
it = iter(iterable)
|
||||
while True:
|
||||
chunk = tuple(itertools.islice(it, n))
|
||||
if not chunk:
|
||||
return
|
||||
yield chunk
|
||||
|
||||
|
||||
def create_ui():
|
||||
import modules.ui
|
||||
|
||||
with gr.Group():
|
||||
with gr.Accordion("Open for Clip Aesthetic!", open=False):
|
||||
with gr.Row():
|
||||
aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
|
||||
value=0.9)
|
||||
aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
|
||||
|
||||
with gr.Row():
|
||||
aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
|
||||
placeholder="Aesthetic learning rate", value="0.0001")
|
||||
aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
|
||||
aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
|
||||
label="Aesthetic imgs embedding",
|
||||
value="None")
|
||||
|
||||
modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
|
||||
|
||||
with gr.Row():
|
||||
aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
|
||||
placeholder="This text is used to rotate the feature space of the imgs embs",
|
||||
value="")
|
||||
aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
|
||||
value=0.1)
|
||||
aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
|
||||
|
||||
return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
|
||||
|
||||
|
||||
aesthetic_clip_model = None
|
||||
|
||||
|
||||
def aesthetic_clip():
|
||||
global aesthetic_clip_model
|
||||
|
||||
if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
|
||||
aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
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||||
aesthetic_clip_model.cpu()
|
||||
|
||||
return aesthetic_clip_model
|
||||
|
||||
|
||||
def generate_imgs_embd(name, folder, batch_size):
|
||||
model = aesthetic_clip().to(device)
|
||||
processor = CLIPProcessor.from_pretrained(model.name_or_path)
|
||||
|
||||
with torch.no_grad():
|
||||
embs = []
|
||||
for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
|
||||
desc=f"Generating embeddings for {name}"):
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
|
||||
outputs = model.get_image_features(**inputs).cpu()
|
||||
embs.append(torch.clone(outputs))
|
||||
inputs.to("cpu")
|
||||
del inputs, outputs
|
||||
|
||||
embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
|
||||
|
||||
# The generated embedding will be located here
|
||||
path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
|
||||
torch.save(embs, path)
|
||||
|
||||
model.cpu()
|
||||
del processor
|
||||
del embs
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
res = f"""
|
||||
Done generating embedding for {name}!
|
||||
Aesthetic embedding saved to {html.escape(path)}
|
||||
"""
|
||||
shared.update_aesthetic_embeddings()
|
||||
return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
|
||||
value="None"), \
|
||||
gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
|
||||
label="Imgs embedding",
|
||||
value="None"), res, ""
|
||||
|
||||
|
||||
def slerp(low, high, val):
|
||||
low_norm = low / torch.norm(low, dim=1, keepdim=True)
|
||||
high_norm = high / torch.norm(high, dim=1, keepdim=True)
|
||||
omega = torch.acos((low_norm * high_norm).sum(1))
|
||||
so = torch.sin(omega)
|
||||
res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
|
||||
return res
|
||||
|
||||
|
||||
class AestheticCLIP:
|
||||
def __init__(self):
|
||||
self.skip = False
|
||||
self.aesthetic_steps = 0
|
||||
self.aesthetic_weight = 0
|
||||
self.aesthetic_lr = 0
|
||||
self.slerp = False
|
||||
self.aesthetic_text_negative = ""
|
||||
self.aesthetic_slerp_angle = 0
|
||||
self.aesthetic_imgs_text = ""
|
||||
|
||||
self.image_embs_name = None
|
||||
self.image_embs = None
|
||||
self.load_image_embs(None)
|
||||
|
||||
def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
|
||||
aesthetic_slerp=True, aesthetic_imgs_text="",
|
||||
aesthetic_slerp_angle=0.15,
|
||||
aesthetic_text_negative=False):
|
||||
self.aesthetic_imgs_text = aesthetic_imgs_text
|
||||
self.aesthetic_slerp_angle = aesthetic_slerp_angle
|
||||
self.aesthetic_text_negative = aesthetic_text_negative
|
||||
self.slerp = aesthetic_slerp
|
||||
self.aesthetic_lr = aesthetic_lr
|
||||
self.aesthetic_weight = aesthetic_weight
|
||||
self.aesthetic_steps = aesthetic_steps
|
||||
self.load_image_embs(image_embs_name)
|
||||
|
||||
if self.image_embs_name is not None:
|
||||
p.extra_generation_params.update({
|
||||
"Aesthetic LR": aesthetic_lr,
|
||||
"Aesthetic weight": aesthetic_weight,
|
||||
"Aesthetic steps": aesthetic_steps,
|
||||
"Aesthetic embedding": self.image_embs_name,
|
||||
"Aesthetic slerp": aesthetic_slerp,
|
||||
"Aesthetic text": aesthetic_imgs_text,
|
||||
"Aesthetic text negative": aesthetic_text_negative,
|
||||
"Aesthetic slerp angle": aesthetic_slerp_angle,
|
||||
})
|
||||
|
||||
def set_skip(self, skip):
|
||||
self.skip = skip
|
||||
|
||||
def load_image_embs(self, image_embs_name):
|
||||
if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
|
||||
image_embs_name = None
|
||||
self.image_embs_name = None
|
||||
if image_embs_name is not None and self.image_embs_name != image_embs_name:
|
||||
self.image_embs_name = image_embs_name
|
||||
self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
|
||||
self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
|
||||
self.image_embs.requires_grad_(False)
|
||||
|
||||
def __call__(self, z, remade_batch_tokens):
|
||||
if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
|
||||
tokenizer = shared.sd_model.cond_stage_model.tokenizer
|
||||
if not opts.use_old_emphasis_implementation:
|
||||
remade_batch_tokens = [
|
||||
[tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
|
||||
remade_batch_tokens]
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
|
||||
model = copy.deepcopy(aesthetic_clip()).to(device)
|
||||
model.requires_grad_(True)
|
||||
if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
|
||||
text_embs_2 = model.get_text_features(
|
||||
**tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
|
||||
if self.aesthetic_text_negative:
|
||||
text_embs_2 = self.image_embs - text_embs_2
|
||||
text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
|
||||
img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
|
||||
else:
|
||||
img_embs = self.image_embs
|
||||
|
||||
with torch.enable_grad():
|
||||
|
||||
# We optimize the model to maximize the similarity
|
||||
optimizer = optim.Adam(
|
||||
model.text_model.parameters(), lr=self.aesthetic_lr
|
||||
)
|
||||
|
||||
for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
|
||||
text_embs = model.get_text_features(input_ids=tokens)
|
||||
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
|
||||
sim = text_embs @ img_embs.T
|
||||
loss = -sim
|
||||
optimizer.zero_grad()
|
||||
loss.mean().backward()
|
||||
optimizer.step()
|
||||
|
||||
zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
if opts.CLIP_stop_at_last_layers > 1:
|
||||
zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
zn = model.text_model.final_layer_norm(zn)
|
||||
else:
|
||||
zn = zn.last_hidden_state
|
||||
model.cpu()
|
||||
del model
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
|
||||
if self.slerp:
|
||||
z = slerp(z, zn, self.aesthetic_weight)
|
||||
else:
|
||||
z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
|
||||
|
||||
return z
|
||||
|
|
@ -1,183 +1,424 @@
|
|||
import os
|
||||
import shutil
|
||||
import sys
|
||||
import time
|
||||
import hashlib
|
||||
import gradio
|
||||
system_bak_path = "webui_log_and_bak"
|
||||
custom_tab_name = "custom fold"
|
||||
faverate_tab_name = "favorites"
|
||||
tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
|
||||
def is_valid_date(date):
|
||||
try:
|
||||
time.strptime(date, "%Y%m%d")
|
||||
return True
|
||||
except:
|
||||
return False
|
||||
|
||||
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)
|
||||
def reduplicative_file_move(src, dst):
|
||||
def same_name_file(basename, path):
|
||||
name, ext = os.path.splitext(basename)
|
||||
f_list = os.listdir(path)
|
||||
max_num = 0
|
||||
for f in f_list:
|
||||
if len(f) <= len(basename):
|
||||
continue
|
||||
f_ext = f[-len(ext):] if len(ext) > 0 else ""
|
||||
if f[:len(name)] == name and f_ext == ext:
|
||||
if f[len(name)] == "(" and f[-len(ext)-1] == ")":
|
||||
number = f[len(name)+1:-len(ext)-1]
|
||||
if number.isdigit():
|
||||
if int(number) > max_num:
|
||||
max_num = int(number)
|
||||
return f"{name}({max_num + 1}){ext}"
|
||||
name = os.path.basename(src)
|
||||
save_name = os.path.join(dst, name)
|
||||
if not os.path.exists(save_name):
|
||||
shutil.move(src, dst)
|
||||
else:
|
||||
name = same_name_file(name, dst)
|
||||
shutil.move(src, os.path.join(dst, name))
|
||||
|
||||
def traverse_all_files(curr_path, image_list, all_type=False):
|
||||
try:
|
||||
f_list = os.listdir(curr_path)
|
||||
except:
|
||||
if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
|
||||
image_list.append(curr_dir)
|
||||
if all_type or (curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt" and curr_path[-4:] != ".csv"):
|
||||
image_list.append(curr_path)
|
||||
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":
|
||||
file = os.path.join(curr_path, file)
|
||||
if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
|
||||
pass
|
||||
elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
|
||||
elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
|
||||
image_list.append(file)
|
||||
else:
|
||||
image_list = traverse_all_files(output_dir, image_list, file)
|
||||
image_list = traverse_all_files(file, image_list)
|
||||
return image_list
|
||||
|
||||
def auto_sorting(dir_name):
|
||||
bak_path = os.path.join(dir_name, system_bak_path)
|
||||
if not os.path.exists(bak_path):
|
||||
os.mkdir(bak_path)
|
||||
log_file = None
|
||||
files_list = []
|
||||
f_list = os.listdir(dir_name)
|
||||
for file in f_list:
|
||||
if file == system_bak_path:
|
||||
continue
|
||||
file_path = os.path.join(dir_name, file)
|
||||
if not is_valid_date(file):
|
||||
if file[-10:].rfind(".") > 0:
|
||||
files_list.append(file_path)
|
||||
else:
|
||||
files_list = traverse_all_files(file_path, files_list, all_type=True)
|
||||
|
||||
def get_recent_images(dir_name, page_index, step, image_index, tabname):
|
||||
page_index = int(page_index)
|
||||
image_list = []
|
||||
if not os.path.exists(dir_name):
|
||||
pass
|
||||
elif os.path.isdir(dir_name):
|
||||
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)))
|
||||
for file in files_list:
|
||||
date_str = time.strftime("%Y%m%d",time.localtime(os.path.getmtime(file)))
|
||||
file_path = os.path.dirname(file)
|
||||
hash_path = hashlib.md5(file_path.encode()).hexdigest()
|
||||
path = os.path.join(dir_name, date_str, hash_path)
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
if log_file is None:
|
||||
log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a")
|
||||
log_file.write(f"{hash_path},{file_path}\n")
|
||||
reduplicative_file_move(file, path)
|
||||
|
||||
date_list = []
|
||||
f_list = os.listdir(dir_name)
|
||||
for f in f_list:
|
||||
if is_valid_date(f):
|
||||
date_list.append(f)
|
||||
elif f == system_bak_path:
|
||||
continue
|
||||
else:
|
||||
try:
|
||||
reduplicative_file_move(os.path.join(dir_name, f), bak_path)
|
||||
except:
|
||||
pass
|
||||
|
||||
today = time.strftime("%Y%m%d",time.localtime(time.time()))
|
||||
if today not in date_list:
|
||||
date_list.append(today)
|
||||
return sorted(date_list, reverse=True)
|
||||
|
||||
def archive_images(dir_name, date_to):
|
||||
filenames = []
|
||||
batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
|
||||
if batch_size <= 0:
|
||||
batch_size = opts.images_history_num_per_page * 6
|
||||
today = time.strftime("%Y%m%d",time.localtime(time.time()))
|
||||
date_to = today if date_to is None or date_to == "" else date_to
|
||||
date_to_bak = date_to
|
||||
if False: #opts.images_history_reconstruct_directory:
|
||||
date_list = auto_sorting(dir_name)
|
||||
for date in date_list:
|
||||
if date <= date_to:
|
||||
path = os.path.join(dir_name, date)
|
||||
if date == today and not os.path.exists(path):
|
||||
continue
|
||||
filenames = traverse_all_files(path, filenames)
|
||||
if len(filenames) > batch_size:
|
||||
break
|
||||
filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
|
||||
else:
|
||||
print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
|
||||
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, ""
|
||||
filenames = traverse_all_files(dir_name, filenames)
|
||||
total_num = len(filenames)
|
||||
tmparray = [(os.path.getmtime(file), file) for file in filenames ]
|
||||
date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400
|
||||
filenames = []
|
||||
date_list = {date_to:None}
|
||||
date = time.strftime("%Y%m%d",time.localtime(time.time()))
|
||||
for t, f in tmparray:
|
||||
date = time.strftime("%Y%m%d",time.localtime(t))
|
||||
date_list[date] = None
|
||||
if t <= date_stamp:
|
||||
filenames.append((t, f ,date))
|
||||
date_list = sorted(list(date_list.keys()), reverse=True)
|
||||
sort_array = sorted(filenames, key=lambda x:-x[0])
|
||||
if len(sort_array) > batch_size:
|
||||
date = sort_array[batch_size][2]
|
||||
filenames = [x[1] for x in sort_array]
|
||||
else:
|
||||
date = date_to if len(sort_array) == 0 else sort_array[-1][2]
|
||||
filenames = [x[1] for x in sort_array]
|
||||
filenames = [x[1] for x in sort_array if x[2]>= date]
|
||||
num = len(filenames)
|
||||
last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
|
||||
date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
|
||||
date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
|
||||
load_info = "<div style='color:#999' align='center'>"
|
||||
load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
|
||||
load_info += "</div>"
|
||||
_, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
|
||||
return (
|
||||
date_to,
|
||||
load_info,
|
||||
filenames,
|
||||
1,
|
||||
image_list,
|
||||
"",
|
||||
"",
|
||||
visible_num,
|
||||
last_date_from,
|
||||
gradio.update(visible=total_num > num)
|
||||
)
|
||||
|
||||
|
||||
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):
|
||||
def delete_image(delete_num, name, filenames, image_index, visible_num):
|
||||
if name == "":
|
||||
return filenames, delete_num
|
||||
else:
|
||||
delete_num = int(delete_num)
|
||||
visible_num = int(visible_num)
|
||||
image_index = int(image_index)
|
||||
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(name):
|
||||
if visible_num == image_index:
|
||||
new_file_list.append(name)
|
||||
i += 1
|
||||
continue
|
||||
print(f"Delete file {name}")
|
||||
os.remove(name)
|
||||
visible_num -= 1
|
||||
txt_file = os.path.splitext(name)[0] + ".txt"
|
||||
if os.path.exists(txt_file):
|
||||
os.remove(txt_file)
|
||||
else:
|
||||
print(f"Not exists file {path}")
|
||||
print(f"Not exists file {name}")
|
||||
else:
|
||||
new_file_list.append(name)
|
||||
i += 1
|
||||
return new_file_list, 1
|
||||
return new_file_list, 1, visible_num
|
||||
|
||||
def save_image(file_name):
|
||||
if file_name is not None and os.path.exists(file_name):
|
||||
shutil.copy(file_name, opts.outdir_save)
|
||||
|
||||
def get_recent_images(page_index, step, filenames):
|
||||
page_index = int(page_index)
|
||||
num_of_imgs_per_page = int(opts.images_history_num_per_page)
|
||||
max_page_index = len(filenames) // num_of_imgs_per_page + 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_of_imgs_per_page
|
||||
image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
|
||||
length = len(filenames)
|
||||
visible_num = num_of_imgs_per_page if idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page
|
||||
visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
|
||||
return page_index, image_list, "", "", visible_num
|
||||
|
||||
def loac_batch_click(date_to):
|
||||
if date_to is None:
|
||||
return time.strftime("%Y%m%d",time.localtime(time.time())), []
|
||||
else:
|
||||
return None, []
|
||||
def forward_click(last_date_from, date_to_recorder):
|
||||
if len(date_to_recorder) == 0:
|
||||
return None, []
|
||||
if last_date_from == date_to_recorder[-1]:
|
||||
date_to_recorder = date_to_recorder[:-1]
|
||||
if len(date_to_recorder) == 0:
|
||||
return None, []
|
||||
return date_to_recorder[-1], date_to_recorder[:-1]
|
||||
|
||||
def backward_click(last_date_from, date_to_recorder):
|
||||
if last_date_from is None or last_date_from == "":
|
||||
return time.strftime("%Y%m%d",time.localtime(time.time())), []
|
||||
if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
|
||||
date_to_recorder.append(last_date_from)
|
||||
return last_date_from, date_to_recorder
|
||||
|
||||
|
||||
def first_page_click(page_index, filenames):
|
||||
return get_recent_images(1, 0, filenames)
|
||||
|
||||
def end_page_click(page_index, filenames):
|
||||
return get_recent_images(-1, 0, filenames)
|
||||
|
||||
def prev_page_click(page_index, filenames):
|
||||
return get_recent_images(page_index, -1, filenames)
|
||||
|
||||
def next_page_click(page_index, filenames):
|
||||
return get_recent_images(page_index, 1, filenames)
|
||||
|
||||
def page_index_change(page_index, filenames):
|
||||
return get_recent_images(page_index, 0, filenames)
|
||||
|
||||
def show_image_info(tabname_box, num, page_index, filenames):
|
||||
file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]
|
||||
tm = "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
|
||||
return file, tm, num, file
|
||||
|
||||
def enable_page_buttons():
|
||||
return gradio.update(visible=True)
|
||||
|
||||
def change_dir(img_dir, date_to):
|
||||
warning = None
|
||||
try:
|
||||
if os.path.exists(img_dir):
|
||||
try:
|
||||
f = os.listdir(img_dir)
|
||||
except:
|
||||
warning = f"'{img_dir} is not a directory"
|
||||
else:
|
||||
warning = "The directory is not exist"
|
||||
except:
|
||||
warning = "The format of the directory is incorrect"
|
||||
if warning is None:
|
||||
today = time.strftime("%Y%m%d",time.localtime(time.time()))
|
||||
return gradio.update(visible=False), gradio.update(visible=True), None, None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
|
||||
else:
|
||||
return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
|
||||
|
||||
def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
|
||||
if opts.outdir_samples != "":
|
||||
dir_name = opts.outdir_samples
|
||||
elif tabname == "txt2img":
|
||||
custom_dir = False
|
||||
if 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
|
||||
elif tabname == faverate_tab_name:
|
||||
dir_name = opts.outdir_save
|
||||
else:
|
||||
return
|
||||
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():
|
||||
custom_dir = True
|
||||
dir_name = None
|
||||
|
||||
if not custom_dir:
|
||||
d = dir_name.split("/")
|
||||
dir_name = d[0]
|
||||
for p in d[1:]:
|
||||
dir_name = os.path.join(dir_name, p)
|
||||
if not os.path.exists(dir_name):
|
||||
os.makedirs(dir_name)
|
||||
|
||||
with gr.Column() as page_panel:
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1, visible=not custom_dir) as load_batch_box:
|
||||
load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)
|
||||
with gr.Column(scale=4):
|
||||
with gr.Row():
|
||||
img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)
|
||||
with gr.Row():
|
||||
with gr.Column(visible=False, scale=1) as batch_panel:
|
||||
with gr.Row():
|
||||
forward = gr.Button('Prev batch')
|
||||
backward = gr.Button('Next batch')
|
||||
with gr.Column(scale=3):
|
||||
load_info = gr.HTML(visible=not custom_dir)
|
||||
with gr.Row(visible=False) as warning:
|
||||
warning_box = gr.Textbox("Message", interactive=False)
|
||||
|
||||
with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
|
||||
with gr.Column(scale=2):
|
||||
with gr.Row(visible=True) as turn_page_buttons:
|
||||
#date_to = gr.Dropdown(label="Date to")
|
||||
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')
|
||||
|
||||
history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
|
||||
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():
|
||||
with gr.Column():
|
||||
img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
|
||||
gr.HTML("<hr>")
|
||||
img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
|
||||
img_file_time= gr.HTML()
|
||||
with gr.Row():
|
||||
if tabname != faverate_tab_name:
|
||||
save_btn = gr.Button('Collect')
|
||||
pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
|
||||
pnginfo_send_to_img2img = gr.Button('Send to img2img')
|
||||
|
||||
|
||||
# hiden items
|
||||
with gr.Row(visible=False):
|
||||
renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
|
||||
batch_date_to = gr.Textbox(label="Date to")
|
||||
visible_img_num = gr.Number()
|
||||
date_to_recorder = gr.State([])
|
||||
last_date_from = gr.Textbox()
|
||||
tabname_box = gr.Textbox(tabname)
|
||||
image_index = gr.Textbox(value=-1)
|
||||
set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
|
||||
filenames = gr.State()
|
||||
all_images_list = gr.State()
|
||||
hidden = gr.Image(type="pil")
|
||||
info1 = gr.Textbox()
|
||||
info2 = gr.Textbox()
|
||||
|
||||
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)
|
||||
img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
|
||||
|
||||
# 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]
|
||||
#change batch
|
||||
change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from, batch_panel]
|
||||
|
||||
batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)
|
||||
batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])
|
||||
batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
|
||||
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])
|
||||
load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
|
||||
forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
|
||||
backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
|
||||
|
||||
|
||||
#delete
|
||||
delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
|
||||
delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None)
|
||||
if tabname != faverate_tab_name:
|
||||
save_btn.click(save_image, inputs=[img_file_name], outputs=None)
|
||||
|
||||
#turn page
|
||||
gallery_inputs = [page_index, filenames]
|
||||
gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
|
||||
first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
|
||||
|
||||
first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
|
||||
|
||||
# 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])
|
||||
set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
|
||||
img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
|
||||
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):
|
||||
def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
|
||||
global opts;
|
||||
opts = sys_opts
|
||||
loads_files_num = int(opts.images_history_num_per_page)
|
||||
num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
|
||||
if cmp_ops.browse_all_images:
|
||||
tabs_list.append(custom_tab_name)
|
||||
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)
|
||||
for tab in tabs_list:
|
||||
with gr.Tab(tab):
|
||||
with gr.Blocks(analytics_enabled=False) :
|
||||
show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
|
||||
gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)
|
||||
gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)
|
||||
|
||||
return images_history
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
|
|||
processed_image.save(os.path.join(output_dir, filename))
|
||||
|
||||
|
||||
def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
|
||||
def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
|
||||
is_inpaint = mode == 1
|
||||
is_batch = mode == 2
|
||||
|
||||
|
|
@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
|
|||
inpainting_mask_invert=inpainting_mask_invert,
|
||||
)
|
||||
|
||||
shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args
|
||||
|
||||
if shared.cmd_opts.enable_console_prompts:
|
||||
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
||||
|
|
|
|||
|
|
@ -104,6 +104,12 @@ class StableDiffusionProcessing():
|
|||
self.seed_resize_from_h = 0
|
||||
self.seed_resize_from_w = 0
|
||||
|
||||
self.scripts = None
|
||||
self.script_args = None
|
||||
self.all_prompts = None
|
||||
self.all_seeds = None
|
||||
self.all_subseeds = None
|
||||
|
||||
|
||||
def init(self, all_prompts, all_seeds, all_subseeds):
|
||||
pass
|
||||
|
|
@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
shared.prompt_styles.apply_styles(p)
|
||||
|
||||
if type(p.prompt) == list:
|
||||
all_prompts = p.prompt
|
||||
p.all_prompts = p.prompt
|
||||
else:
|
||||
all_prompts = p.batch_size * p.n_iter * [p.prompt]
|
||||
p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
|
||||
|
||||
if type(seed) == list:
|
||||
all_seeds = seed
|
||||
p.all_seeds = seed
|
||||
else:
|
||||
all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
|
||||
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
|
||||
|
||||
if type(subseed) == list:
|
||||
all_subseeds = subseed
|
||||
p.all_subseeds = subseed
|
||||
else:
|
||||
all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
|
||||
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
|
||||
|
||||
def infotext(iteration=0, position_in_batch=0):
|
||||
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
|
||||
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
|
||||
|
||||
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
||||
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.run_alwayson_scripts(p)
|
||||
|
||||
infotexts = []
|
||||
output_images = []
|
||||
|
||||
with torch.no_grad(), p.sd_model.ema_scope():
|
||||
with devices.autocast():
|
||||
p.init(all_prompts, all_seeds, all_subseeds)
|
||||
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
||||
|
||||
if state.job_count == -1:
|
||||
state.job_count = p.n_iter
|
||||
|
|
@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
if state.interrupted:
|
||||
break
|
||||
|
||||
prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
|
||||
if (len(prompts) == 0):
|
||||
break
|
||||
|
|
@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
index_of_first_image = 1
|
||||
|
||||
if opts.grid_save:
|
||||
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
||||
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
||||
|
||||
devices.torch_gc()
|
||||
return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
|
||||
|
||||
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
|
|
|||
42
modules/script_callbacks.py
Normal file
42
modules/script_callbacks.py
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
|
||||
callbacks_model_loaded = []
|
||||
callbacks_ui_tabs = []
|
||||
|
||||
|
||||
def clear_callbacks():
|
||||
callbacks_model_loaded.clear()
|
||||
callbacks_ui_tabs.clear()
|
||||
|
||||
|
||||
def model_loaded_callback(sd_model):
|
||||
for callback in callbacks_model_loaded:
|
||||
callback(sd_model)
|
||||
|
||||
|
||||
def ui_tabs_callback():
|
||||
res = []
|
||||
|
||||
for callback in callbacks_ui_tabs:
|
||||
res += callback() or []
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def on_model_loaded(callback):
|
||||
"""register a function to be called when the stable diffusion model is created; the model is
|
||||
passed as an argument"""
|
||||
callbacks_model_loaded.append(callback)
|
||||
|
||||
|
||||
def on_ui_tabs(callback):
|
||||
"""register a function to be called when the UI is creating new tabs.
|
||||
The function must either return a None, which means no new tabs to be added, or a list, where
|
||||
each element is a tuple:
|
||||
(gradio_component, title, elem_id)
|
||||
|
||||
gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
|
||||
title is tab text displayed to user in the UI
|
||||
elem_id is HTML id for the tab
|
||||
"""
|
||||
callbacks_ui_tabs.append(callback)
|
||||
|
||||
|
|
@ -1,86 +1,175 @@
|
|||
import os
|
||||
import sys
|
||||
import traceback
|
||||
from collections import namedtuple
|
||||
|
||||
import modules.ui as ui
|
||||
import gradio as gr
|
||||
|
||||
from modules.processing import StableDiffusionProcessing
|
||||
from modules import shared
|
||||
from modules import shared, paths, script_callbacks
|
||||
|
||||
AlwaysVisible = object()
|
||||
|
||||
|
||||
class Script:
|
||||
filename = None
|
||||
args_from = None
|
||||
args_to = None
|
||||
alwayson = False
|
||||
|
||||
infotext_fields = None
|
||||
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
|
||||
parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
|
||||
"""
|
||||
|
||||
# The title of the script. This is what will be displayed in the dropdown menu.
|
||||
def title(self):
|
||||
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
|
||||
|
||||
raise NotImplementedError()
|
||||
|
||||
# How the script is displayed in the UI. See https://gradio.app/docs/#components
|
||||
# for the different UI components you can use and how to create them.
|
||||
# Most UI components can return a value, such as a boolean for a checkbox.
|
||||
# The returned values are passed to the run method as parameters.
|
||||
def ui(self, is_img2img):
|
||||
"""this function should create gradio UI elements. See https://gradio.app/docs/#components
|
||||
The return value should be an array of all components that are used in processing.
|
||||
Values of those returned componenbts will be passed to run() and process() functions.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
# Determines when the script should be shown in the dropdown menu via the
|
||||
# returned value. As an example:
|
||||
# is_img2img is True if the current tab is img2img, and False if it is txt2img.
|
||||
# Thus, return is_img2img to only show the script on the img2img tab.
|
||||
def show(self, is_img2img):
|
||||
"""
|
||||
is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
|
||||
|
||||
This function should return:
|
||||
- False if the script should not be shown in UI at all
|
||||
- True if the script should be shown in UI if it's scelected in the scripts drowpdown
|
||||
- script.AlwaysVisible if the script should be shown in UI at all times
|
||||
"""
|
||||
|
||||
return True
|
||||
|
||||
# This is where the additional processing is implemented. The parameters include
|
||||
# self, the model object "p" (a StableDiffusionProcessing class, see
|
||||
# processing.py), and the parameters returned by the ui method.
|
||||
# Custom functions can be defined here, and additional libraries can be imported
|
||||
# to be used in processing. The return value should be a Processed object, which is
|
||||
# what is returned by the process_images method.
|
||||
def run(self, *args):
|
||||
def run(self, p, *args):
|
||||
"""
|
||||
This function is called if the script has been selected in the script dropdown.
|
||||
It must do all processing and return the Processed object with results, same as
|
||||
one returned by processing.process_images.
|
||||
|
||||
Usually the processing is done by calling the processing.process_images function.
|
||||
|
||||
args contains all values returned by components from ui()
|
||||
"""
|
||||
|
||||
raise NotImplementedError()
|
||||
|
||||
# The description method is currently unused.
|
||||
# To add a description that appears when hovering over the title, amend the "titles"
|
||||
# dict in script.js to include the script title (returned by title) as a key, and
|
||||
# your description as the value.
|
||||
def process(self, p, *args):
|
||||
"""
|
||||
This function is called before processing begins for AlwaysVisible scripts.
|
||||
scripts. You can modify the processing object (p) here, inject hooks, etc.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def describe(self):
|
||||
"""unused"""
|
||||
return ""
|
||||
|
||||
|
||||
current_basedir = paths.script_path
|
||||
|
||||
|
||||
def basedir():
|
||||
"""returns the base directory for the current script. For scripts in the main scripts directory,
|
||||
this is the main directory (where webui.py resides), and for scripts in extensions directory
|
||||
(ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
|
||||
"""
|
||||
return current_basedir
|
||||
|
||||
|
||||
scripts_data = []
|
||||
ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
|
||||
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])
|
||||
|
||||
|
||||
def load_scripts(basedir):
|
||||
if not os.path.exists(basedir):
|
||||
return
|
||||
def list_scripts(scriptdirname, extension):
|
||||
scripts_list = []
|
||||
|
||||
for filename in sorted(os.listdir(basedir)):
|
||||
path = os.path.join(basedir, filename)
|
||||
basedir = os.path.join(paths.script_path, scriptdirname)
|
||||
if os.path.exists(basedir):
|
||||
for filename in sorted(os.listdir(basedir)):
|
||||
scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
|
||||
|
||||
if os.path.splitext(path)[1].lower() != '.py':
|
||||
extdir = os.path.join(paths.script_path, "extensions")
|
||||
if os.path.exists(extdir):
|
||||
for dirname in sorted(os.listdir(extdir)):
|
||||
dirpath = os.path.join(extdir, dirname)
|
||||
scriptdirpath = os.path.join(dirpath, scriptdirname)
|
||||
|
||||
if not os.path.isdir(scriptdirpath):
|
||||
continue
|
||||
|
||||
for filename in sorted(os.listdir(scriptdirpath)):
|
||||
scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
|
||||
|
||||
scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
|
||||
|
||||
return scripts_list
|
||||
|
||||
|
||||
def list_files_with_name(filename):
|
||||
res = []
|
||||
|
||||
dirs = [paths.script_path]
|
||||
|
||||
extdir = os.path.join(paths.script_path, "extensions")
|
||||
if os.path.exists(extdir):
|
||||
dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
|
||||
|
||||
for dirpath in dirs:
|
||||
if not os.path.isdir(dirpath):
|
||||
continue
|
||||
|
||||
if not os.path.isfile(path):
|
||||
continue
|
||||
path = os.path.join(dirpath, filename)
|
||||
if os.path.isfile(filename):
|
||||
res.append(path)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def load_scripts():
|
||||
global current_basedir
|
||||
scripts_data.clear()
|
||||
script_callbacks.clear_callbacks()
|
||||
|
||||
scripts_list = list_scripts("scripts", ".py")
|
||||
|
||||
syspath = sys.path
|
||||
|
||||
for scriptfile in sorted(scripts_list):
|
||||
try:
|
||||
with open(path, "r", encoding="utf8") as file:
|
||||
if scriptfile.basedir != paths.script_path:
|
||||
sys.path = [scriptfile.basedir] + sys.path
|
||||
current_basedir = scriptfile.basedir
|
||||
|
||||
with open(scriptfile.path, "r", encoding="utf8") as file:
|
||||
text = file.read()
|
||||
|
||||
from types import ModuleType
|
||||
compiled = compile(text, path, 'exec')
|
||||
module = ModuleType(filename)
|
||||
compiled = compile(text, scriptfile.path, 'exec')
|
||||
module = ModuleType(scriptfile.filename)
|
||||
exec(compiled, module.__dict__)
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
scripts_data.append((script_class, path))
|
||||
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))
|
||||
|
||||
except Exception:
|
||||
print(f"Error loading script: {filename}", file=sys.stderr)
|
||||
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
finally:
|
||||
sys.path = syspath
|
||||
current_basedir = paths.script_path
|
||||
|
||||
|
||||
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
||||
try:
|
||||
|
|
@ -96,56 +185,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
|||
class ScriptRunner:
|
||||
def __init__(self):
|
||||
self.scripts = []
|
||||
self.selectable_scripts = []
|
||||
self.alwayson_scripts = []
|
||||
self.titles = []
|
||||
self.infotext_fields = []
|
||||
|
||||
def setup_ui(self, is_img2img):
|
||||
for script_class, path in scripts_data:
|
||||
for script_class, path, basedir in scripts_data:
|
||||
script = script_class()
|
||||
script.filename = path
|
||||
|
||||
if not script.show(is_img2img):
|
||||
continue
|
||||
visibility = script.show(is_img2img)
|
||||
|
||||
self.scripts.append(script)
|
||||
if visibility == AlwaysVisible:
|
||||
self.scripts.append(script)
|
||||
self.alwayson_scripts.append(script)
|
||||
script.alwayson = True
|
||||
|
||||
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
|
||||
elif visibility:
|
||||
self.scripts.append(script)
|
||||
self.selectable_scripts.append(script)
|
||||
|
||||
dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
|
||||
dropdown.save_to_config = True
|
||||
inputs = [dropdown]
|
||||
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
|
||||
|
||||
for script in self.scripts:
|
||||
inputs = [None]
|
||||
inputs_alwayson = [True]
|
||||
|
||||
def create_script_ui(script, inputs, inputs_alwayson):
|
||||
script.args_from = len(inputs)
|
||||
script.args_to = len(inputs)
|
||||
|
||||
controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
|
||||
|
||||
if controls is None:
|
||||
continue
|
||||
return
|
||||
|
||||
for control in controls:
|
||||
control.custom_script_source = os.path.basename(script.filename)
|
||||
control.visible = False
|
||||
if not script.alwayson:
|
||||
control.visible = False
|
||||
|
||||
if script.infotext_fields is not None:
|
||||
self.infotext_fields += script.infotext_fields
|
||||
|
||||
inputs += controls
|
||||
inputs_alwayson += [script.alwayson for _ in controls]
|
||||
script.args_to = len(inputs)
|
||||
|
||||
for script in self.alwayson_scripts:
|
||||
with gr.Group():
|
||||
create_script_ui(script, inputs, inputs_alwayson)
|
||||
|
||||
dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
|
||||
dropdown.save_to_config = True
|
||||
inputs[0] = dropdown
|
||||
|
||||
for script in self.selectable_scripts:
|
||||
create_script_ui(script, inputs, inputs_alwayson)
|
||||
|
||||
def select_script(script_index):
|
||||
if 0 < script_index <= len(self.scripts):
|
||||
script = self.scripts[script_index-1]
|
||||
if 0 < script_index <= len(self.selectable_scripts):
|
||||
script = self.selectable_scripts[script_index-1]
|
||||
args_from = script.args_from
|
||||
args_to = script.args_to
|
||||
else:
|
||||
args_from = 0
|
||||
args_to = 0
|
||||
|
||||
return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
|
||||
return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
|
||||
|
||||
def init_field(title):
|
||||
if title == 'None':
|
||||
return
|
||||
script_index = self.titles.index(title)
|
||||
script = self.scripts[script_index]
|
||||
script = self.selectable_scripts[script_index]
|
||||
for i in range(script.args_from, script.args_to):
|
||||
inputs[i].visible = True
|
||||
|
||||
|
|
@ -164,7 +277,7 @@ class ScriptRunner:
|
|||
if script_index == 0:
|
||||
return None
|
||||
|
||||
script = self.scripts[script_index-1]
|
||||
script = self.selectable_scripts[script_index-1]
|
||||
|
||||
if script is None:
|
||||
return None
|
||||
|
|
@ -176,7 +289,16 @@ class ScriptRunner:
|
|||
|
||||
return processed
|
||||
|
||||
def reload_sources(self):
|
||||
def run_alwayson_scripts(self, p):
|
||||
for script in self.alwayson_scripts:
|
||||
try:
|
||||
script_args = p.script_args[script.args_from:script.args_to]
|
||||
script.process(p, *script_args)
|
||||
except Exception:
|
||||
print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def reload_sources(self, cache):
|
||||
for si, script in list(enumerate(self.scripts)):
|
||||
with open(script.filename, "r", encoding="utf8") as file:
|
||||
args_from = script.args_from
|
||||
|
|
@ -186,9 +308,12 @@ class ScriptRunner:
|
|||
|
||||
from types import ModuleType
|
||||
|
||||
compiled = compile(text, filename, 'exec')
|
||||
module = ModuleType(script.filename)
|
||||
exec(compiled, module.__dict__)
|
||||
module = cache.get(filename, None)
|
||||
if module is None:
|
||||
compiled = compile(text, filename, 'exec')
|
||||
module = ModuleType(script.filename)
|
||||
exec(compiled, module.__dict__)
|
||||
cache[filename] = module
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
|
|
@ -197,19 +322,22 @@ class ScriptRunner:
|
|||
self.scripts[si].args_from = args_from
|
||||
self.scripts[si].args_to = args_to
|
||||
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
|
||||
|
||||
def reload_script_body_only():
|
||||
scripts_txt2img.reload_sources()
|
||||
scripts_img2img.reload_sources()
|
||||
cache = {}
|
||||
scripts_txt2img.reload_sources(cache)
|
||||
scripts_img2img.reload_sources(cache)
|
||||
|
||||
|
||||
def reload_scripts(basedir):
|
||||
def reload_scripts():
|
||||
global scripts_txt2img, scripts_img2img
|
||||
|
||||
scripts_data.clear()
|
||||
load_scripts(basedir)
|
||||
load_scripts()
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
|
||||
|
|
|
|||
|
|
@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
multipliers.append([1.0] * 75)
|
||||
|
||||
z1 = self.process_tokens(tokens, multipliers)
|
||||
z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
|
||||
z = z1 if z is None else torch.cat((z, z1), axis=-2)
|
||||
|
||||
remade_batch_tokens = rem_tokens
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from omegaconf import OmegaConf
|
|||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import shared, modelloader, devices
|
||||
from modules import shared, modelloader, devices, script_callbacks
|
||||
from modules.paths import models_path
|
||||
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
|
||||
|
||||
|
|
@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
|
|||
sd_hijack.model_hijack.hijack(sd_model)
|
||||
|
||||
sd_model.eval()
|
||||
shared.sd_model = sd_model
|
||||
|
||||
script_callbacks.model_loaded_callback(sd_model)
|
||||
|
||||
print(f"Model loaded.")
|
||||
return sd_model
|
||||
|
|
@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
|
|||
|
||||
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
|
||||
checkpoints_loaded.clear()
|
||||
shared.sd_model = load_model(checkpoint_info)
|
||||
load_model(checkpoint_info)
|
||||
return shared.sd_model
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
|
|
|
|||
|
|
@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
|
|||
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
|
||||
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
|
||||
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
|
||||
parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
|
||||
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
|
||||
parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
|
||||
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
|
||||
|
|
@ -81,6 +80,7 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
|
|||
parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
|
||||
parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
|
||||
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
|
||||
parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
restricted_opts = [
|
||||
|
|
@ -109,21 +109,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
|
|||
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
|
||||
loaded_hypernetwork = None
|
||||
|
||||
|
||||
os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)
|
||||
aesthetic_embeddings = {}
|
||||
|
||||
|
||||
def update_aesthetic_embeddings():
|
||||
global aesthetic_embeddings
|
||||
aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
|
||||
os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
|
||||
aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
|
||||
|
||||
|
||||
update_aesthetic_embeddings()
|
||||
|
||||
|
||||
def reload_hypernetworks():
|
||||
global hypernetworks
|
||||
|
||||
|
|
@ -333,6 +318,14 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
|||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('images-history', "Images Browser"), {
|
||||
#"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
|
||||
"images_history_preload": OptionInfo(False, "Preload images at startup"),
|
||||
"images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
|
||||
"images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),
|
||||
"images_history_grid_num": OptionInfo(6, "Number of grids in each row"),
|
||||
|
||||
}))
|
||||
|
||||
class Options:
|
||||
data = None
|
||||
|
|
@ -407,9 +400,6 @@ sd_model = None
|
|||
|
||||
clip_model = None
|
||||
|
||||
from modules.aesthetic_clip import AestheticCLIP
|
||||
aesthetic_clip = AestheticCLIP()
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ import modules.processing as processing
|
|||
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, 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, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
|
||||
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, *args):
|
||||
p = StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
|
||||
|
|
@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
|||
firstphase_height=firstphase_height if enable_hr else None,
|
||||
)
|
||||
|
||||
shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
p.script_args = args
|
||||
|
||||
if cmd_opts.enable_console_prompts:
|
||||
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
|
||||
|
|
|
|||
106
modules/ui.py
106
modules/ui.py
|
|
@ -23,10 +23,10 @@ import gradio as gr
|
|||
import gradio.utils
|
||||
import gradio.routes
|
||||
|
||||
from modules import sd_hijack, sd_models, localization
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks
|
||||
from modules.paths import script_path
|
||||
|
||||
from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings
|
||||
from modules.shared import opts, cmd_opts, restricted_opts
|
||||
|
||||
if cmd_opts.deepdanbooru:
|
||||
from modules.deepbooru import get_deepbooru_tags
|
||||
|
|
@ -44,7 +44,6 @@ from modules.images import save_image
|
|||
import modules.textual_inversion.ui
|
||||
import modules.hypernetworks.ui
|
||||
|
||||
import modules.aesthetic_clip as aesthetic_clip
|
||||
import modules.images_history as img_his
|
||||
|
||||
|
||||
|
|
@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
||||
|
||||
aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
|
||||
|
||||
with gr.Group():
|
||||
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
|
||||
|
||||
|
|
@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
denoising_strength,
|
||||
firstphase_width,
|
||||
firstphase_height,
|
||||
aesthetic_lr,
|
||||
aesthetic_weight,
|
||||
aesthetic_steps,
|
||||
aesthetic_imgs,
|
||||
aesthetic_slerp,
|
||||
aesthetic_imgs_text,
|
||||
aesthetic_slerp_angle,
|
||||
aesthetic_text_negative
|
||||
] + custom_inputs,
|
||||
|
||||
outputs=[
|
||||
|
|
@ -804,14 +793,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
|
||||
(firstphase_width, "First pass size-1"),
|
||||
(firstphase_height, "First pass size-2"),
|
||||
(aesthetic_lr, "Aesthetic LR"),
|
||||
(aesthetic_weight, "Aesthetic weight"),
|
||||
(aesthetic_steps, "Aesthetic steps"),
|
||||
(aesthetic_imgs, "Aesthetic embedding"),
|
||||
(aesthetic_slerp, "Aesthetic slerp"),
|
||||
(aesthetic_imgs_text, "Aesthetic text"),
|
||||
(aesthetic_text_negative, "Aesthetic text negative"),
|
||||
(aesthetic_slerp_angle, "Aesthetic slerp angle"),
|
||||
*modules.scripts.scripts_txt2img.infotext_fields
|
||||
]
|
||||
|
||||
txt2img_preview_params = [
|
||||
|
|
@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
||||
|
||||
aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
|
||||
|
||||
with gr.Group():
|
||||
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
|
||||
|
||||
|
|
@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
inpainting_mask_invert,
|
||||
img2img_batch_input_dir,
|
||||
img2img_batch_output_dir,
|
||||
aesthetic_lr_im,
|
||||
aesthetic_weight_im,
|
||||
aesthetic_steps_im,
|
||||
aesthetic_imgs_im,
|
||||
aesthetic_slerp_im,
|
||||
aesthetic_imgs_text_im,
|
||||
aesthetic_slerp_angle_im,
|
||||
aesthetic_text_negative_im,
|
||||
] + custom_inputs,
|
||||
outputs=[
|
||||
img2img_gallery,
|
||||
|
|
@ -1087,14 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
(seed_resize_from_w, "Seed resize from-1"),
|
||||
(seed_resize_from_h, "Seed resize from-2"),
|
||||
(denoising_strength, "Denoising strength"),
|
||||
(aesthetic_lr_im, "Aesthetic LR"),
|
||||
(aesthetic_weight_im, "Aesthetic weight"),
|
||||
(aesthetic_steps_im, "Aesthetic steps"),
|
||||
(aesthetic_imgs_im, "Aesthetic embedding"),
|
||||
(aesthetic_slerp_im, "Aesthetic slerp"),
|
||||
(aesthetic_imgs_text_im, "Aesthetic text"),
|
||||
(aesthetic_text_negative_im, "Aesthetic text negative"),
|
||||
(aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
|
||||
*modules.scripts.scripts_img2img.infotext_fields
|
||||
]
|
||||
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
|
||||
|
||||
|
|
@ -1217,12 +1182,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
)
|
||||
#images history
|
||||
images_history_switch_dict = {
|
||||
"fn":modules.generation_parameters_copypaste.connect_paste,
|
||||
"t2i":txt2img_paste_fields,
|
||||
"i2i":img2img_paste_fields
|
||||
"fn": modules.generation_parameters_copypaste.connect_paste,
|
||||
"t2i": txt2img_paste_fields,
|
||||
"i2i": img2img_paste_fields
|
||||
}
|
||||
|
||||
images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
||||
images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
||||
|
||||
with gr.Blocks() as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
|
@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
with gr.Column():
|
||||
create_embedding = gr.Button(value="Create embedding", variant='primary')
|
||||
|
||||
with gr.Tab(label="Create aesthetic images embedding"):
|
||||
|
||||
new_embedding_name_ae = gr.Textbox(label="Name")
|
||||
process_src_ae = gr.Textbox(label='Source directory')
|
||||
batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
|
||||
|
||||
with gr.Tab(label="Create hypernetwork"):
|
||||
new_hypernetwork_name = gr.Textbox(label="Name")
|
||||
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
|
||||
|
|
@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
]
|
||||
)
|
||||
|
||||
create_embedding_ae.click(
|
||||
fn=aesthetic_clip.generate_imgs_embd,
|
||||
inputs=[
|
||||
new_embedding_name_ae,
|
||||
process_src_ae,
|
||||
batch_ae
|
||||
],
|
||||
outputs=[
|
||||
aesthetic_imgs,
|
||||
aesthetic_imgs_im,
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
create_hypernetwork.click(
|
||||
fn=modules.hypernetworks.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
|
|
@ -1580,10 +1518,10 @@ Requested path was: {f}
|
|||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return gr.update(visible=True), opts.dumpjson()
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
||||
return gr.update(value=oldval), opts.dumpjson()
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
opts.data[key] = value
|
||||
|
||||
if oldval != value:
|
||||
|
|
@ -1689,19 +1627,24 @@ Requested path was: {f}
|
|||
(img2img_interface, "img2img", "img2img"),
|
||||
(extras_interface, "Extras", "extras"),
|
||||
(pnginfo_interface, "PNG Info", "pnginfo"),
|
||||
(images_history, "History", "images_history"),
|
||||
(images_history, "Image Browser", "images_history"),
|
||||
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
||||
(train_interface, "Train", "ti"),
|
||||
(settings_interface, "Settings", "settings"),
|
||||
]
|
||||
|
||||
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
|
||||
css = file.read()
|
||||
interfaces += script_callbacks.ui_tabs_callback()
|
||||
|
||||
interfaces += [(settings_interface, "Settings", "settings")]
|
||||
|
||||
css = ""
|
||||
|
||||
for cssfile in modules.scripts.list_files_with_name("style.css"):
|
||||
with open(cssfile, "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "user.css")):
|
||||
with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
|
||||
usercss = file.read()
|
||||
css += usercss
|
||||
css += file.read() + "\n"
|
||||
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
css += css_hide_progressbar
|
||||
|
|
@ -1924,9 +1867,9 @@ def load_javascript(raw_response):
|
|||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
||||
javascript = f'<script>{jsfile.read()}</script>'
|
||||
|
||||
jsdir = os.path.join(script_path, "javascript")
|
||||
for filename in sorted(os.listdir(jsdir)):
|
||||
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
||||
scripts_list = modules.scripts.list_scripts("javascript", ".js")
|
||||
for basedir, filename, path in scripts_list:
|
||||
with open(path, "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
|
||||
|
||||
if cmd_opts.theme is not None:
|
||||
|
|
@ -1944,6 +1887,5 @@ def load_javascript(raw_response):
|
|||
gradio.routes.templates.TemplateResponse = template_response
|
||||
|
||||
|
||||
reload_javascript = partial(load_javascript,
|
||||
gradio.routes.templates.TemplateResponse)
|
||||
reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
|
||||
reload_javascript()
|
||||
|
|
|
|||
|
|
@ -477,7 +477,7 @@ input[type="range"]{
|
|||
padding: 0;
|
||||
}
|
||||
|
||||
#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{
|
||||
#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
|
||||
max-width: 2.5em;
|
||||
min-width: 2.5em;
|
||||
height: 2.4em;
|
||||
|
|
|
|||
7
webui.py
7
webui.py
|
|
@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
|
|||
|
||||
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
|
||||
|
||||
|
||||
def initialize():
|
||||
modelloader.cleanup_models()
|
||||
modules.sd_models.setup_model()
|
||||
|
|
@ -79,9 +80,9 @@ def initialize():
|
|||
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
|
||||
modelloader.load_upscalers()
|
||||
|
||||
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
||||
modules.scripts.load_scripts()
|
||||
|
||||
shared.sd_model = modules.sd_models.load_model()
|
||||
modules.sd_models.load_model()
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
|
||||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
|
||||
|
|
@ -145,7 +146,7 @@ def webui():
|
|||
sd_samplers.set_samplers()
|
||||
|
||||
print('Reloading Custom Scripts')
|
||||
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
|
||||
modules.scripts.reload_scripts()
|
||||
print('Reloading modules: modules.ui')
|
||||
importlib.reload(modules.ui)
|
||||
print('Refreshing Model List')
|
||||
|
|
|
|||
Loading…
Reference in a new issue