| import gc |
| import os |
|
|
| import torch |
| from safetensors.torch import load_file |
|
|
| from .clip import CLIPModel |
| from .t5 import T5EncoderModel |
| from .transformer_flowcache import WanModel |
| from .vae import WanVAE |
|
|
|
|
| def download_model(model_id): |
| if not os.path.exists(model_id): |
| from huggingface_hub import snapshot_download |
|
|
| model_id = snapshot_download(repo_id=model_id) |
| return model_id |
|
|
|
|
| def get_vae(model_path, device="cuda", weight_dtype=torch.float32) -> WanVAE: |
| vae = WanVAE(model_path).to(device).to(weight_dtype) |
| vae.vae.requires_grad_(False) |
| vae.vae.eval() |
| gc.collect() |
| torch.cuda.empty_cache() |
| return vae |
|
|
|
|
| def get_transformer(model_path, device="cuda", weight_dtype=torch.bfloat16, use_kvrange=False): |
| config_path = os.path.join(model_path, "config.json") |
| transformer = WanModel.from_config(config_path).to(weight_dtype).to(device) |
|
|
| for file in os.listdir(model_path): |
| if file.endswith(".safetensors"): |
| file_path = os.path.join(model_path, file) |
| state_dict = load_file(file_path) |
| transformer.load_state_dict(state_dict, strict=False) |
| del state_dict |
| gc.collect() |
| torch.cuda.empty_cache() |
|
|
| transformer.requires_grad_(False) |
| transformer.eval() |
| gc.collect() |
| torch.cuda.empty_cache() |
| return transformer |
|
|
|
|
| def get_text_encoder(model_path, device="cuda", weight_dtype=torch.bfloat16) -> T5EncoderModel: |
| t5_model = os.path.join(model_path, "models_t5_umt5-xxl-enc-bf16.pth") |
| tokenizer_path = os.path.join(model_path, "google", "umt5-xxl") |
| text_encoder = T5EncoderModel(checkpoint_path=t5_model, tokenizer_path=tokenizer_path).to(device).to(weight_dtype) |
| text_encoder.requires_grad_(False) |
| text_encoder.eval() |
| gc.collect() |
| torch.cuda.empty_cache() |
| return text_encoder |
|
|
|
|
| def get_image_encoder(model_path, device="cuda", weight_dtype=torch.bfloat16) -> CLIPModel: |
| checkpoint_path = os.path.join(model_path, "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth") |
| tokenizer_path = os.path.join(model_path, "xlm-roberta-large") |
| image_enc = CLIPModel(checkpoint_path, tokenizer_path).to(weight_dtype).to(device) |
| image_enc.requires_grad_(False) |
| image_enc.eval() |
| gc.collect() |
| torch.cuda.empty_cache() |
| return image_enc |
|
|
