| import os | |
| import comfy.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 = comfy.sd.load_unet(unet_path) | |
| clip = None | |
| if output_clip: | |
| clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) | |
| vae = None | |
| if output_vae: | |
| sd = comfy.utils.load_torch_file(vae_path) | |
| vae = comfy.sd.VAE(sd=sd) | |
| return (unet, clip, vae) | |