154 lines
4.2 KiB
Python
154 lines
4.2 KiB
Python
import os
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import threading
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from modules.paths import script_path
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import torch
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import numpy as np
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from omegaconf import OmegaConf
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from PIL import Image
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import signal
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from ldm.util import instantiate_from_config
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.ui
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from modules.ui import plaintext_to_html
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import modules.scripts
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import modules.processing as processing
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import modules.sd_hijack
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import modules.gfpgan_model as gfpgan
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import modules.realesrgan_model as realesrgan
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import modules.images as images
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import modules.lowvram
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import modules.txt2img
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import modules.img2img
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shared.sd_upscalers = {
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"RealESRGAN": lambda img: realesrgan.upscale_with_realesrgan(img, 2, 0),
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"Lanczos": lambda img: img.resize((img.width*2, img.height*2), resample=images.LANCZOS),
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"None": lambda img: img
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}
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realesrgan.setup_realesrgan()
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gfpgan.setup_gfpgan()
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.eval()
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return model
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def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
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processing.torch_gc()
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image = image.convert("RGB")
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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if gfpgan.have_gfpgan is not None and GFPGAN_strength > 0:
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restored_img = gfpgan.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if GFPGAN_strength < 1.0:
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res = Image.blend(image, res, GFPGAN_strength)
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image = res
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if realesrgan.have_realesrgan and RealESRGAN_upscaling != 1.0:
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image = realesrgan.upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
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images.save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True, no_prompt=True)
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return image, '', ''
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def run_pnginfo(image):
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info = ''
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for key, text in image.info.items():
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info += f"""
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<div>
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<p><b>{plaintext_to_html(str(key))}</b></p>
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<p>{plaintext_to_html(str(text))}</p>
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</div>
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""".strip()+"\n"
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if len(info) == 0:
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message = "Nothing found in the image."
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info = f"<div><p>{message}<p></div>"
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return '', '', info
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queue_lock = threading.Lock()
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def wrap_gradio_gpu_call(func):
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def f(*args, **kwargs):
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with queue_lock:
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res = func(*args, **kwargs)
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shared.state.job = ""
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return res
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return modules.ui.wrap_gradio_call(f)
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except Exception:
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pass
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sd_config = OmegaConf.load(cmd_opts.config)
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shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
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shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
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if cmd_opts.lowvram or cmd_opts.medvram:
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modules.lowvram.setup_for_low_vram(shared.sd_model, cmd_opts.medvram)
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else:
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shared.sd_model = shared.sd_model.to(shared.device)
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modules.sd_hijack.model_hijack.hijack(shared.sd_model)
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modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
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# make the program just exit at ctrl+c without waiting for anything
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def sigint_handler(sig, frame):
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print(f'Interrupted with singal {sig} in {frame}')
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os._exit(0)
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signal.signal(signal.SIGINT, sigint_handler)
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demo = modules.ui.create_ui(
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txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
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img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
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run_extras=wrap_gradio_gpu_call(run_extras),
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run_pnginfo=run_pnginfo
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)
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demo.launch(share=cmd_opts.share)
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