Instructions to use TencentARC/WorldCrafter-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TencentARC/WorldCrafter-Base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TencentARC/WorldCrafter-Base", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Download model_index.json from TencentARC/WorldCrafter-Base: direct link, hf CLI and curl.
- Browser
- Download file 417 Bytes
-
https://huggingface.co/TencentARC/WorldCrafter-Base/resolve/main/model_index.json
- Command line
-
hf download hf://TencentARC/WorldCrafter-Base/model_index.json
-
curl -L -o model_index.json https://huggingface.co/TencentARC/WorldCrafter-Base/resolve/main/model_index.json
417 Bytes
| { | |
| "_class_name": "WorldCrafterPipeline", | |
| "_diffusers_version": "0.37.0", | |
| "scheduler": [ | |
| "worldcrafter", | |
| "WorldCrafterScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "UMT5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ], | |
| "transformer": [ | |
| "worldcrafter", | |
| "WorldCrafterTransformer3DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLWan" | |
| ] | |
| } | |