Instructions to use mignonjia/hyworld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mignonjia/hyworld 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("mignonjia/hyworld", 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 vae/config.json from mignonjia/hyworld: direct link, hf CLI and curl.
- Browser
- Download file 420 Bytes
-
https://huggingface.co/mignonjia/hyworld/resolve/main/vae/config.json
- Command line
-
hf download hf://mignonjia/hyworld/vae/config.json
-
curl -L -o config.json https://huggingface.co/mignonjia/hyworld/resolve/main/vae/config.json
420 Bytes
| { | |
| "_class_name": "AutoencoderKLHYWorld", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "block_out_channels": [ | |
| 128, | |
| 256, | |
| 512, | |
| 1024, | |
| 1024 | |
| ], | |
| "downsample_match_channel": true, | |
| "in_channels": 3, | |
| "latent_channels": 32, | |
| "layers_per_block": 2, | |
| "out_channels": 3, | |
| "scaling_factor": 1.03682, | |
| "spatial_compression_ratio": 16, | |
| "temporal_compression_ratio": 4, | |
| "upsample_match_channel": true | |
| } | |