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"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" 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.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")) # make the program just exit at ctrl+c without waiting for anything def sigint_handler(sig, frame): print(f'Interrupted with singal {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)