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Stable Diffusion Webui
Commit ea8aa170 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
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Merge branch 'master' into master

parents 4d19f3b7 a13af34b
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Showing with 219 additions and 139 deletions
javascript/images_history.js
View file @ ea8aa170
......@@ -163,10 +163,15 @@ function images_history_init(){
for (var i in images_history_tab_list){
var tabname = images_history_tab_list[i]
tab_btns[i].setAttribute("tabname", tabname);
tab_btns[i].addEventListener('click', images_history_click_tab);
// 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]);
load_txt2img_button.click();
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);
}
......@@ -182,12 +187,15 @@ document.addEventListener("DOMContentLoaded", function() {
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);
}
}*/
}
});
......
javascript/progressbar.js
View file @ ea8aa170
// code related to showing and updating progressbar shown as the image is being made
global_progressbars = {}
galleries = {}
galleryObservers = {}
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
var progressbar = gradioApp().getElementById(id_progressbar)
......@@ -31,13 +33,24 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
preview.style.width = gallery.clientWidth + "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;
if(!progressDiv){
if (skip) {
skip.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)
......@@ -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(){
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')
......
javascript/ui.js
View file @ ea8aa170
......@@ -187,12 +187,10 @@ onUiUpdate(function(){
if (!txt2img_textarea) {
txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
}
if (!img2img_textarea) {
img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
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) {
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(){
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
setTimeout(function(){location.reload()},2000)
......
launch.py
View file @ ea8aa170
......@@ -9,6 +9,7 @@ import platform
dir_repos = "repositories"
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL',"")
def extract_arg(args, name):
......@@ -57,7 +58,7 @@ def run_python(code, desc=None, errdesc=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}")
return run(f'"{python}" -m pip {args} --prefer-binary{f' --index-url {index_url}' if index_url!='' else ''}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
def check_run_python(code):
......
modules/hypernetworks/hypernetwork.py
View file @ ea8aa170
......@@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
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_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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'
path = shared.hypernetworks.get(hypernetwork_name, None)
......@@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
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)
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:
shared.sd_model.cond_stage_model.to(devices.cpu)
......@@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, entry in pbar:
for i, entries in pbar:
hypernetwork.step = i + ititial_step
scheduler.apply(optimizer, hypernetwork.step)
......@@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
break
with torch.autocast("cuda"):
cond = entry.cond.to(devices.device)
x = entry.latent.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), cond)[0]
c = stack_conds([entry.cond for entry in entries]).to(devices.device)
# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
loss = shared.sd_model(x, c)[0]
del x
del cond
del c
losses[hypernetwork.step % losses.shape[0]] = loss.item()
......@@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
p.width = preview_width
p.height = preview_height
else:
p.prompt = entry.cond_text
p.prompt = entries[0].cond_text
p.steps = 20
preview_text = p.prompt
......@@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
<p>
Loss: {losses.mean():.7f}<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 image: {html.escape(last_saved_image)}<br/>
</p>
......
modules/images_history.py
View file @ ea8aa170
This diff is collapsed.
modules/processing.py
View file @ ea8aa170
......@@ -140,7 +140,7 @@ class Processed:
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
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.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 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]
......
modules/textual_inversion/dataset.py
View file @ ea8aa170
......@@ -24,11 +24,12 @@ class DatasetEntry:
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):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
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
self.placeholder_token = placeholder_token
self.batch_size = batch_size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
......@@ -78,14 +79,14 @@ class PersonalizedBase(Dataset):
if include_cond:
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)
assert len(self.dataset) > 1, "No images have been found in the dataset."
self.length = len(self.dataset) * repeats
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.shuffle()
......@@ -102,13 +103,19 @@ class PersonalizedBase(Dataset):
return self.length
def __getitem__(self, i):
if i % len(self.dataset) == 0:
self.shuffle()
res = []
for j in range(self.batch_size):
position = i * self.batch_size + j
if position % len(self.indexes) == 0:
self.shuffle()
index = self.indexes[position % len(self.indexes)]
entry = self.dataset[index]
index = self.indexes[i % len(self.indexes)]
entry = self.dataset[index]
if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
res.append(entry)
return entry
return res
modules/textual_inversion/textual_inversion.py
View file @ ea8aa170
......@@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
})
def train_embedding(embedding_name, learn_rate, 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):
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'
shared.state.textinfo = "Initializing textual inversion training..."
......@@ -231,7 +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)}..."
with torch.autocast("cuda"):
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)
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)
hijack = sd_hijack.model_hijack
......@@ -251,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, entry in pbar:
for i, entries in pbar:
embedding.step = i + ititial_step
scheduler.apply(optimizer, embedding.step)
......@@ -262,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
break
with torch.autocast("cuda"):
c = cond_model([entry.cond_text])
x = entry.latent.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
c = cond_model([entry.cond_text for entry in entries])
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
loss = shared.sd_model(x, c)[0]
del x
losses[embedding.step % losses.shape[0]] = loss.item()
......@@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
p.width = preview_width
p.height = preview_height
else:
p.prompt = entry.cond_text
p.prompt = entries[0].cond_text
p.steps = 20
p.width = training_width
p.height = training_height
......@@ -348,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
<p>
Loss: {losses.mean():.7f}<br/>
Step: {embedding.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 image: {html.escape(last_saved_image)}<br/>
</p>
......
modules/ui.py
View file @ ea8aa170
......@@ -433,7 +433,10 @@ def create_toprow(is_img2img):
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
)
with gr.Column(scale=1, elem_id="roll_col"):
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
paste = gr.Button(value=paste_symbol, elem_id="paste")
......@@ -446,7 +449,10 @@ def create_toprow(is_img2img):
with gr.Row():
with gr.Column(scale=8):
with gr.Row():
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
)
with gr.Column(scale=1, elem_id="roll_col"):
sh = gr.Button(elem_id="sh", visible=True)
......@@ -567,8 +573,8 @@ def create_ui(wrap_gradio_gpu_call):
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
with gr.Row(visible=False) as hr_options:
firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)
firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)
firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row(equal_height=True):
......@@ -1090,7 +1096,7 @@ def create_ui(wrap_gradio_gpu_call):
"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, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
with gr.Blocks() as modelmerger_interface:
with gr.Row().style(equal_height=False):
......@@ -1166,6 +1172,7 @@ def create_ui(wrap_gradio_gpu_call):
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
batch_size = gr.Number(label='Batch size', value=1, precision=0)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
......@@ -1244,6 +1251,7 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
train_embedding_name,
learn_rate,
batch_size,
dataset_directory,
log_directory,
training_width,
......@@ -1268,6 +1276,7 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
train_hypernetwork_name,
learn_rate,
batch_size,
dataset_directory,
log_directory,
steps,
......@@ -1487,7 +1496,7 @@ Requested path was: {f}
(img2img_interface, "img2img", "img2img"),
(extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
#(images_history, "History", "images_history"),
(images_history, "History", "images_history"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
(train_interface, "Train", "ti"),
(settings_interface, "Settings", "settings"),
......
script.js
View file @ ea8aa170
......@@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() {
document.addEventListener('keydown', function(e) {
var handled = false;
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) {
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
}
if (handled) {
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
......
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