from collections import OrderedDict import torch from .common import MODEL_FOLDER, load_sd_inpainting_model, download_file model_dict = { 'sd15_inp': { 'sd_version': 1, 'diffusers_ckpt': True, 'model_path': OrderedDict([ ('unet', 'sd-1-5-inpainting/unet.fp16.safetensors'), ('encoder', 'sd-1-5-inpainting/encoder.fp16.safetensors'), ('vae', 'sd-1-5-inpainting/vae.fp16.safetensors') ]), 'download_url': OrderedDict([ ('unet', 'https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors?download=true'), ('encoder', 'https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/text_encoder/model.fp16.safetensors?download=true'), ('vae', 'https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors?download=true') ]) }, 'ds8_inp': { 'sd_version': 1, 'diffusers_ckpt': True, 'model_path': OrderedDict([ ('unet', 'ds-8-inpainting/unet.fp16.safetensors'), ('encoder', 'ds-8-inpainting/encoder.fp16.safetensors'), ('vae', 'ds-8-inpainting/vae.fp16.safetensors') ]), 'download_url': OrderedDict([ ('unet', 'https://huggingface.co/Lykon/dreamshaper-8-inpainting/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors?download=true'), ('encoder', 'https://huggingface.co/Lykon/dreamshaper-8-inpainting/resolve/main/text_encoder/model.fp16.safetensors?download=true'), ('vae', 'https://huggingface.co/Lykon/dreamshaper-8-inpainting/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors?download=true') ]) }, 'sd2_inp': { 'sd_version': 2, 'diffusers_ckpt': False, 'model_path': 'sd-2-0-inpainting/512-inpainting-ema.safetensors', 'download_url': 'https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.safetensors?download=true' } } model_cache = {} def pre_download_inpainting_models(): for model_id, model_details in model_dict.items(): download_url = model_details['download_url'] model_path = model_details["model_path"] if type(download_url) == str and type(model_path) == str: download_file(download_url, f'{MODEL_FOLDER}/{model_path}') elif type(download_url) == OrderedDict and type(model_path) == OrderedDict: for key in download_url.keys(): download_file(download_url[key], f'{MODEL_FOLDER}/{model_path[key]}') else: raise Exception('download_url definition type is not supported') def load_inpainting_model(model_id, dtype=torch.float16, device='cuda:0', cache=False): if cache and model_id in model_cache: return model_cache[model_id] else: if model_id not in model_dict: raise Exception(f'Unsupported model-id. Choose one from {list(model_dict.keys())}.') model = load_sd_inpainting_model( **model_dict[model_id], dtype=dtype, device=device ) if cache: model_cache[model_id] = model return model