Unverified Commit af6fba24 authored by KyuSeok Jung's avatar KyuSeok Jung Committed by GitHub

Merge branch 'master' into master

parents 467cae16 95c6308c
......@@ -3,8 +3,21 @@ global_progressbars = {}
galleries = {}
galleryObservers = {}
// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
timeoutIds = {}
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
var progressbar = gradioApp().getElementById(id_progressbar)
// gradio 3.8's enlightened approach allows them to create two nested div elements inside each other with same id
// every time you use gr.HTML(elem_id='xxx'), so we handle this here
var progressbar = gradioApp().querySelector("#"+id_progressbar+" #"+id_progressbar)
var progressbarParent
if(progressbar){
progressbarParent = gradioApp().querySelector("#"+id_progressbar)
} else{
progressbar = gradioApp().getElementById(id_progressbar)
progressbarParent = null
}
var skip = id_skip ? gradioApp().getElementById(id_skip) : null
var interrupt = gradioApp().getElementById(id_interrupt)
......@@ -26,18 +39,26 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
global_progressbars[id_progressbar] = progressbar
var mutationObserver = new MutationObserver(function(m){
if(timeoutIds[id_part]) return;
preview = gradioApp().getElementById(id_preview)
gallery = gradioApp().getElementById(id_gallery)
if(preview != null && gallery != null){
preview.style.width = gallery.clientWidth + "px"
preview.style.height = gallery.clientHeight + "px"
if(progressbarParent) progressbar.style.width = progressbarParent.clientWidth + "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(progressDiv){
timeoutIds[id_part] = window.setTimeout(function() {
timeoutIds[id_part] = null
requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt)
}, 500)
} else{
if (skip) {
skip.style.display = "none"
}
......@@ -49,11 +70,8 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
galleries[id_gallery] = null;
}
}
}
window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
});
mutationObserver.observe( progressbar, { childList:true, subtree:true })
}
......
......@@ -70,7 +70,7 @@
"None": "Nichts",
"Prompt matrix": "Promptmatrix",
"Prompts from file or textbox": "Prompts aus Datei oder Textfeld",
"X/Y plot": "X/Y Graf",
"X/Y plot": "X/Y Graph",
"Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
"Iterate seed every line": "Iterate seed every line",
"List of prompt inputs": "List of prompt inputs",
......
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import base64
import io
import time
import uvicorn
from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
from fastapi import APIRouter, Depends, HTTPException
import modules.shared as shared
from modules import devices
......@@ -29,6 +31,12 @@ def setUpscalers(req: dict):
return reqDict
def encode_pil_to_base64(image):
buffer = io.BytesIO()
image.save(buffer, format="png")
return base64.b64encode(buffer.getvalue())
class Api:
def __init__(self, app, queue_lock):
self.router = APIRouter()
......@@ -40,6 +48,7 @@ class Api:
self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
sampler_index = sampler_to_index(txt2imgreq.sampler_index)
......@@ -176,6 +185,11 @@ class Api:
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
def interruptapi(self):
shared.state.interrupt()
return {}
def launch(self, server_name, port):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port)
......@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
info_hash=hash(info),
args_hash=hash(upscale_args))
args_hash=hash((upscale_args, upscale_first)))
cached_entry = cached_images.get(cache_key)
if cached_entry is None:
res = upscale(image, *upscale_args)
......
......@@ -510,6 +510,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
if opts.enable_pnginfo:
for k, v in params.pnginfo.items():
pnginfo_data.add_text(k, str(v))
......
......@@ -137,6 +137,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
if processed is None:
processed = process_images(p)
p.close()
shared.total_tqdm.clear()
generation_info_js = processed.js()
......
......@@ -56,9 +56,9 @@ class InterrogateModels:
import clip
if self.running_on_cpu:
model, preprocess = clip.load(clip_model_name, device="cpu")
model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
else:
model, preprocess = clip.load(clip_model_name)
model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
model.eval()
model = model.to(devices.device_interrogate)
......
......@@ -202,6 +202,10 @@ class StableDiffusionProcessing():
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
raise NotImplementedError()
def close(self):
self.sd_model = None
self.sampler = None
class Processed:
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
......@@ -597,9 +601,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.postprocess(p, res)
p.sd_model = None
p.sampler = None
return res
......
......@@ -26,6 +26,24 @@ class ImageSaveParams:
"""dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
class CFGDenoiserParams:
def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
self.x = x
"""Latent image representation in the process of being denoised"""
self.image_cond = image_cond
"""Conditioning image"""
self.sigma = sigma
"""Current sigma noise step value"""
self.sampling_step = sampling_step
"""Current Sampling step number"""
self.total_sampling_steps = total_sampling_steps
"""Total number of sampling steps planned"""
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
callbacks_app_started = []
callbacks_model_loaded = []
......@@ -33,6 +51,7 @@ callbacks_ui_tabs = []
callbacks_ui_settings = []
callbacks_before_image_saved = []
callbacks_image_saved = []
callbacks_cfg_denoiser = []
def clear_callbacks():
......@@ -41,7 +60,7 @@ def clear_callbacks():
callbacks_ui_settings.clear()
callbacks_before_image_saved.clear()
callbacks_image_saved.clear()
callbacks_cfg_denoiser.clear()
def app_started_callback(demo: Blocks, app: FastAPI):
for c in callbacks_app_started:
......@@ -95,6 +114,14 @@ def image_saved_callback(params: ImageSaveParams):
report_exception(c, 'image_saved_callback')
def cfg_denoiser_callback(params: CFGDenoiserParams):
for c in callbacks_cfg_denoiser:
try:
c.callback(params)
except Exception:
report_exception(c, 'cfg_denoiser_callback')
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
......@@ -147,3 +174,12 @@ def on_image_saved(callback):
- params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
"""
add_callback(callbacks_image_saved, callback)
def on_cfg_denoiser(callback):
"""register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
The callback is called with one argument:
- params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
"""
add_callback(callbacks_cfg_denoiser, callback)
......@@ -12,6 +12,7 @@ from modules import prompt_parser, devices, processing, images
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
......@@ -280,6 +281,12 @@ class CFGDenoiser(torch.nn.Module):
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
sigma_in = denoiser_params.sigma
if tensor.shape[1] == uncond.shape[1]:
cond_in = torch.cat([tensor, uncond])
......
......@@ -51,6 +51,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
......@@ -288,11 +289,12 @@ 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 hypernetwork. Saves VRAM."),
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
......
......@@ -235,6 +235,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
unload = shared.opts.unload_models_when_training
if save_embedding_every > 0:
embedding_dir = os.path.join(log_directory, "embeddings")
......@@ -272,6 +273,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
......@@ -328,6 +331,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{embedding_name}-{steps_done}'
last_saved_image = os.path.join(images_dir, forced_filename)
shared.sd_model.first_stage_model.to(devices.device)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
......@@ -355,6 +361,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
processed = processing.process_images(p)
image = processed.images[0]
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
shared.state.current_image = image
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
......@@ -400,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
shared.sd_model.first_stage_model.to(devices.device)
return embedding, filename
......
......@@ -25,7 +25,9 @@ def train_embedding(*args):
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
apply_optimizations = shared.opts.training_xattention_optimizations
try:
if not apply_optimizations:
sd_hijack.undo_optimizations()
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
......@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
except Exception:
raise
finally:
if not apply_optimizations:
sd_hijack.apply_optimizations()
......@@ -47,6 +47,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
if processed is None:
processed = process_images(p)
p.close()
shared.total_tqdm.clear()
generation_info_js = processed.js()
......
......@@ -671,6 +671,8 @@ def create_ui(wrap_gradio_gpu_call):
import modules.img2img
import modules.txt2img
reload_javascript()
parameters_copypaste.reset()
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
......@@ -1573,8 +1575,7 @@ def create_ui(wrap_gradio_gpu_call):
reload_script_bodies.click(
fn=reload_scripts,
inputs=[],
outputs=[],
_js='function(){}'
outputs=[]
)
def request_restart():
......@@ -1586,7 +1587,7 @@ def create_ui(wrap_gradio_gpu_call):
fn=request_restart,
inputs=[],
outputs=[],
_js='function(){restart_reload()}'
_js='restart_reload'
)
if column is not None:
......@@ -1785,4 +1786,3 @@ def load_javascript(raw_response):
reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
reload_javascript()
......@@ -4,7 +4,7 @@ fairscale==0.4.4
fonts
font-roboto
gfpgan
gradio==3.5
gradio==3.8
invisible-watermark
numpy
omegaconf
......@@ -12,7 +12,7 @@ opencv-python
requests
piexif
Pillow
pytorch_lightning
pytorch_lightning==1.7.7
realesrgan
scikit-image>=0.19
timm==0.4.12
......
......@@ -2,7 +2,7 @@ transformers==4.19.2
diffusers==0.3.0
basicsr==1.4.2
gfpgan==1.3.8
gradio==3.5
gradio==3.8
numpy==1.23.3
Pillow==9.2.0
realesrgan==0.3.0
......
......@@ -96,6 +96,7 @@ class Script(scripts.Script):
def ui(self, is_img2img):
checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False)
prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
file = gr.File(label="Upload prompt inputs", type='bytes')
......@@ -106,9 +107,9 @@ class Script(scripts.Script):
# We don't shrink back to 1, because that causes the control to ignore [enter], and it may
# be unclear to the user that shift-enter is needed.
prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
return [checkbox_iterate, file, prompt_txt]
return [checkbox_iterate, checkbox_iterate_batch, file, prompt_txt]
def run(self, p, checkbox_iterate, file, prompt_txt: str):
def run(self, p, checkbox_iterate, checkbox_iterate_batch, file, prompt_txt: str):
lines = [x.strip() for x in prompt_txt.splitlines()]
lines = [x for x in lines if len(x) > 0]
......@@ -137,7 +138,7 @@ class Script(scripts.Script):
jobs.append(args)
print(f"Will process {len(lines)} lines in {job_count} jobs.")
if (checkbox_iterate and p.seed == -1):
if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
p.seed = int(random.randrange(4294967294))
state.job_count = job_count
......@@ -153,7 +154,7 @@ class Script(scripts.Script):
proc = process_images(copy_p)
images += proc.images
if (checkbox_iterate):
if checkbox_iterate:
p.seed = p.seed + (p.batch_size * p.n_iter)
......
......@@ -260,6 +260,16 @@ input[type="range"]{
#txt2img_negative_prompt, #img2img_negative_prompt{
}
/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
.transition.opacity-20 {
opacity: 1 !important;
}
/* more gradio's garbage cleanup */
.min-h-\[4rem\] {
min-height: unset !important;
}
#txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
position: absolute;
z-index: 1000;
......
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