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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.images as images
import modules.lowvram
import modules.txt2img
import modules.img2img
shared.sd_upscalers = {
"RealESRGAN": lambda img: realesrgan.upscale_with_realesrgan(img, 2, 0),
"Lanczos": lambda img: img.resize((img.width*2, img.height*2), resample=images.LANCZOS),
"None": lambda img: img
}
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
def draw_xy_grid(xs, ys, x_label, y_label, cell):
res = []
ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
for y in ys:
for x in xs:
state.job = f"{x + y * len(xs)} out of {len(xs) * len(ys)}"
res.append(cell(x, y))
grid = images.image_grid(res, rows=len(ys))
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
return grid
def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
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 realesrgan.have_realesrgan and RealESRGAN_upscaling != 1.0:
image = realesrgan.upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
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):
with queue_lock:
res = func(*args, **kwargs)
shared.state.job = ""
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.medram)
else:
shared.sd_model = shared.sd_model.to(shared.device)
modules.sd_hijack.model_hijack.hijack(shared.sd_model)
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(signal, frame):
print('Interrupted')
os._exit(0)
signal.signal(signal.SIGINT, sigint_handler)
demo = modules.ui.create_ui(
opts=opts,
cmd_opts=cmd_opts,
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)
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