import json import os import ldm_patched.modules.sd def first_file(path, filenames): for f in filenames: p = os.path.join(path, f) if os.path.exists(p): return p return None def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) text_encoder_paths = [text_encoder1_path] if text_encoder2_path is not None: text_encoder_paths.append(text_encoder2_path) unet = ldm_patched.modules.sd.load_unet(unet_path) clip = None if output_clip: clip = ldm_patched.modules.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) vae = None if output_vae: sd = ldm_patched.modules.utils.load_torch_file(vae_path) vae = ldm_patched.modules.sd.VAE(sd=sd) return (unet, clip, vae)