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Merge branch 'master' into patch-1

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NoCrypt 2022-11-11 21:14:10 +07:00 committed by GitHub
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10 changed files with 114 additions and 17 deletions
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33
javascript/generationParams.js Normal file
View file

@ -0,0 +1,33 @@
// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
let txt2img_gallery, img2img_gallery, modal = undefined;
onUiUpdate(function(){
if (!txt2img_gallery) {
txt2img_gallery = attachGalleryListeners("txt2img")
}
if (!img2img_gallery) {
img2img_gallery = attachGalleryListeners("img2img")
}
if (!modal) {
modal = gradioApp().getElementById('lightboxModal')
modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
}
});
let modalObserver = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
gradioApp().getElementById(selectedTab+"_generation_info_button").click()
});
});
function attachGalleryListeners(tab_name) {
gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
gallery?.addEventListener('keydown', (e) => {
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
gradioApp().getElementById(tab_name+"_generation_info_button").click()
});
return gallery;
}

14
modules/api/api.py
View file

@ -15,6 +15,9 @@ from modules.sd_models import checkpoints_list
from modules.realesrgan_model import get_realesrgan_models
from typing import List
if shared.cmd_opts.deepdanbooru:
from modules.deepbooru import get_deepbooru_tags
def upscaler_to_index(name: str):
try:
return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@ -220,11 +223,20 @@ class Api:
if image_b64 is None:
raise HTTPException(status_code=404, detail="Image not found")
img = self.__base64_to_image(image_b64)
img = decode_base64_to_image(image_b64)
img = img.convert('RGB')
# Override object param
with self.queue_lock:
if interrogatereq.model == "clip":
processed = shared.interrogator.interrogate(img)
elif interrogatereq.model == "deepdanbooru":
if shared.cmd_opts.deepdanbooru:
processed = get_deepbooru_tags(img)
else:
raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
else:
raise HTTPException(status_code=404, detail="Model not found")
return InterrogateResponse(caption=processed)

1
modules/api/models.py
View file

@ -170,6 +170,7 @@ class ProgressResponse(BaseModel):
class InterrogateRequest(BaseModel):
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
model: str = Field(default="clip", title="Model", description="The interrogate model used.")
class InterrogateResponse(BaseModel):
caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")

11
modules/ngrok.py
View file

@ -1,14 +1,23 @@
from pyngrok import ngrok, conf, exception
def connect(token, port, region):
account = None
if token == None:
token = 'None'
else:
if ':' in token:
# token = authtoken:username:password
account = token.split(':')[1] + ':' + token.split(':')[-1]
token = token.split(':')[0]
config = conf.PyngrokConfig(
auth_token=token, region=region
)
try:
if account == None:
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
else:
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
except exception.PyngrokNgrokError:
print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')

29
modules/sd_models.py
View file

@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
cache_enabled = shared.opts.sd_checkpoint_cache > 0
if cache_enabled:
sd_vae.restore_base_vae(model)
checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
if checkpoint_info not in checkpoints_loaded:
if cache_enabled and checkpoint_info in checkpoints_loaded:
# use checkpoint cache
vae_name = sd_vae.get_filename(vae_file) if vae_file else None
vae_message = f" with {vae_name} VAE" if vae_name else ""
print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
else:
# load from file
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@ -181,6 +189,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
model.load_state_dict(sd, strict=False)
del sd
if cache_enabled:
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
@ -199,14 +211,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
model.first_stage_model.to(devices.dtype_vae)
else:
vae_name = sd_vae.get_filename(vae_file) if vae_file else None
vae_message = f" with {vae_name} VAE" if vae_name else ""
print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
if shared.opts.sd_checkpoint_cache > 0:
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
# clean up cache if limit is reached
if cache_enabled:
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
checkpoints_loaded.popitem(last=False) # LRU
model.sd_model_hash = sd_model_hash

2
modules/shared.py
View file

@ -319,6 +319,8 @@ options_templates.update(options_section(('system', "System"), {
options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
"tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

7
modules/textual_inversion/dataset.py
View file

@ -98,7 +98,12 @@ class PersonalizedBase(Dataset):
def create_text(self, filename_text):
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
text = text.replace("[filewords]", filename_text)
tags = filename_text.split(',')
if shared.opts.tag_drop_out != 0:
tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
if shared.opts.shuffle_tags:
random.shuffle(tags)
text = text.replace("[filewords]", ','.join(tags))
return text
def __len__(self):

22
modules/ui.py
View file

@ -566,6 +566,19 @@ def apply_setting(key, value):
return value
def update_generation_info(args):
generation_info, html_info, img_index = args
try:
generation_info = json.loads(generation_info)
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
return html_info
return plaintext_to_html(generation_info["infotexts"][img_index])
except Exception:
pass
# if the json parse or anything else fails, just return the old html_info
return html_info
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
def refresh():
refresh_method()
@ -638,6 +651,15 @@ Requested path was: {f}
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
if tabname == 'txt2img' or tabname == 'img2img':
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
generation_info_button.click(
fn=update_generation_info,
_js="(x, y) => [x, y, selected_gallery_index()]",
inputs=[generation_info, html_info],
outputs=[html_info],
preprocess=False
)
save.click(
fn=wrap_gradio_call(save_files),

2
scripts/prompt_matrix.py
View file

@ -80,6 +80,8 @@ class Script(scripts.Script):
grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
processed.images.insert(0, grid)
processed.index_of_first_image = 1
processed.infotexts.insert(0, processed.infotexts[0])
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p)

6
scripts/prompts_from_file.py
View file

@ -145,6 +145,8 @@ class Script(scripts.Script):
state.job_count = job_count
images = []
all_prompts = []
infotexts = []
for n, args in enumerate(jobs):
state.job = f"{state.job_no + 1} out of {state.job_count}"
@ -157,5 +159,7 @@ class Script(scripts.Script):
if checkbox_iterate:
p.seed = p.seed + (p.batch_size * p.n_iter)
all_prompts += proc.all_prompts
infotexts += proc.infotexts
return Processed(p, images, p.seed, "")
return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)