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on
A10G
Running
on
A10G
| import importlib | |
| from omegaconf import OmegaConf | |
| import torch | |
| import safetensors | |
| import safetensors.torch | |
| from lib.smplfusion import DDIM, share, scheduler | |
| from .common import * | |
| MODEL_PATH = f'{MODEL_FOLDER}/dreamshaper/dreamshaper_8Inpainting.safetensors' | |
| DOWNLOAD_URL = 'https://civitai.com/api/download/models/131004' | |
| # pre-download | |
| download_file(DOWNLOAD_URL, MODEL_PATH) | |
| def load_model(dtype=torch.float16): | |
| print ("Loading model: Dreamshaper Inpainting V8") | |
| download_file(DOWNLOAD_URL, MODEL_PATH) | |
| state_dict = safetensors.torch.load_file(MODEL_PATH) | |
| config = OmegaConf.load(f'{CONFIG_FOLDER}/ddpm/v1.yaml') | |
| unet = load_obj(f'{CONFIG_FOLDER}/unet/inpainting/v1.yaml').eval().cuda() | |
| vae = load_obj(f'{CONFIG_FOLDER}/vae.yaml').eval().cuda() | |
| encoder = load_obj(f'{CONFIG_FOLDER}/encoders/clip.yaml').eval().cuda() | |
| extract = lambda state_dict, model: {x[len(model)+1:]:y for x,y in state_dict.items() if model in x} | |
| unet_state = extract(state_dict, 'model.diffusion_model') | |
| encoder_state = extract(state_dict, 'cond_stage_model') | |
| vae_state = extract(state_dict, 'first_stage_model') | |
| unet.load_state_dict(unet_state) | |
| encoder.load_state_dict(encoder_state) | |
| vae.load_state_dict(vae_state) | |
| if dtype == torch.float16: | |
| unet.convert_to_fp16() | |
| vae.to(dtype) | |
| encoder.to(dtype) | |
| unet = unet.requires_grad_(False) | |
| encoder = encoder.requires_grad_(False) | |
| vae = vae.requires_grad_(False) | |
| ddim = DDIM(config, vae, encoder, unet) | |
| share.schedule = scheduler.linear(config.timesteps, config.linear_start, config.linear_end) | |
| return ddim | |