mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
182 lines
5.3 KiB
Python
182 lines
5.3 KiB
Python
import os
|
|
import threading
|
|
|
|
from modules.paths import script_path
|
|
|
|
import torch
|
|
import numpy as np
|
|
from omegaconf import OmegaConf
|
|
from PIL import Image
|
|
|
|
import signal
|
|
|
|
from ldm.util import instantiate_from_config
|
|
|
|
from modules.shared import opts, cmd_opts, state
|
|
import modules.shared as shared
|
|
import modules.ui
|
|
from modules.ui import plaintext_to_html
|
|
import modules.scripts
|
|
import modules.processing as processing
|
|
import modules.sd_hijack
|
|
import modules.gfpgan_model as gfpgan
|
|
import modules.realesrgan_model as realesrgan
|
|
import modules.esrgan_model as esrgan
|
|
import modules.images as images
|
|
import modules.lowvram
|
|
import modules.txt2img
|
|
import modules.img2img
|
|
|
|
|
|
esrgan.load_models(cmd_opts.esrgan_models_path)
|
|
realesrgan.setup_realesrgan()
|
|
gfpgan.setup_gfpgan()
|
|
|
|
|
|
def load_model_from_config(config, ckpt, verbose=False):
|
|
print(f"Loading model from {ckpt}")
|
|
pl_sd = torch.load(ckpt, map_location="cpu")
|
|
if "global_step" in pl_sd:
|
|
print(f"Global Step: {pl_sd['global_step']}")
|
|
sd = pl_sd["state_dict"]
|
|
model = instantiate_from_config(config.model)
|
|
m, u = model.load_state_dict(sd, strict=False)
|
|
if len(m) > 0 and verbose:
|
|
print("missing keys:")
|
|
print(m)
|
|
if len(u) > 0 and verbose:
|
|
print("unexpected keys:")
|
|
print(u)
|
|
|
|
model.eval()
|
|
return model
|
|
|
|
cached_images = {}
|
|
|
|
|
|
def run_extras(image, gfpgan_strength, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
|
|
processing.torch_gc()
|
|
|
|
image = image.convert("RGB")
|
|
|
|
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
|
|
|
if gfpgan.have_gfpgan is not None and gfpgan_strength > 0:
|
|
restored_img = gfpgan.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
|
|
res = Image.fromarray(restored_img)
|
|
|
|
if gfpgan_strength < 1.0:
|
|
res = Image.blend(image, res, gfpgan_strength)
|
|
|
|
image = res
|
|
|
|
if upscaling_resize != 1.0:
|
|
def upscale(image, scaler_index, resize):
|
|
small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
|
|
pixels = tuple(np.array(small).flatten().tolist())
|
|
key = (resize, scaler_index, image.width, image.height) + pixels
|
|
|
|
c = cached_images.get(key)
|
|
if c is None:
|
|
upscaler = shared.sd_upscalers[scaler_index]
|
|
c = upscaler.upscale(image, image.width * resize, image.height * resize)
|
|
cached_images[key] = c
|
|
|
|
return c
|
|
|
|
res = upscale(image, extras_upscaler_1, upscaling_resize)
|
|
|
|
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility>0:
|
|
res2 = upscale(image, extras_upscaler_2, upscaling_resize)
|
|
res = Image.blend(res, res2, extras_upscaler_2_visibility)
|
|
|
|
image = res
|
|
|
|
while len(cached_images) > 2:
|
|
del cached_images[next(iter(cached_images.keys()))]
|
|
|
|
images.save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True, no_prompt=True)
|
|
|
|
return image, '', ''
|
|
|
|
|
|
def run_pnginfo(image):
|
|
info = ''
|
|
for key, text in image.info.items():
|
|
info += f"""
|
|
<div>
|
|
<p><b>{plaintext_to_html(str(key))}</b></p>
|
|
<p>{plaintext_to_html(str(text))}</p>
|
|
</div>
|
|
""".strip()+"\n"
|
|
|
|
if len(info) == 0:
|
|
message = "Nothing found in the image."
|
|
info = f"<div><p>{message}<p></div>"
|
|
|
|
return '', '', info
|
|
|
|
|
|
queue_lock = threading.Lock()
|
|
|
|
|
|
def wrap_gradio_gpu_call(func):
|
|
def f(*args, **kwargs):
|
|
shared.state.sampling_step = 0
|
|
shared.state.job_count = -1
|
|
shared.state.job_no = 0
|
|
shared.state.current_latent = None
|
|
shared.state.current_image = None
|
|
shared.state.current_progress_index = 0
|
|
|
|
with queue_lock:
|
|
res = func(*args, **kwargs)
|
|
|
|
shared.state.job = ""
|
|
shared.state.job_count = 0
|
|
|
|
return res
|
|
|
|
return modules.ui.wrap_gradio_call(f)
|
|
|
|
|
|
try:
|
|
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
|
|
|
from transformers import logging
|
|
|
|
logging.set_verbosity_error()
|
|
except Exception:
|
|
pass
|
|
|
|
sd_config = OmegaConf.load(cmd_opts.config)
|
|
shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
|
|
shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
|
|
|
|
if cmd_opts.lowvram or cmd_opts.medvram:
|
|
modules.lowvram.setup_for_low_vram(shared.sd_model, cmd_opts.medvram)
|
|
else:
|
|
shared.sd_model = shared.sd_model.to(shared.device)
|
|
|
|
modules.sd_hijack.model_hijack.hijack(shared.sd_model)
|
|
|
|
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
|
|
|
if __name__ == "__main__":
|
|
# make the program just exit at ctrl+c without waiting for anything
|
|
def sigint_handler(sig, frame):
|
|
print(f'Interrupted with signal {sig} in {frame}')
|
|
os._exit(0)
|
|
|
|
|
|
signal.signal(signal.SIGINT, sigint_handler)
|
|
|
|
demo = modules.ui.create_ui(
|
|
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
|
|
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
|
|
run_extras=wrap_gradio_gpu_call(run_extras),
|
|
run_pnginfo=run_pnginfo
|
|
)
|
|
|
|
demo.launch(share=cmd_opts.share, server_name="0.0.0.0" if cmd_opts.listen else None)
|