Instructions to use FastVideo/Waypoint-1-Small-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/Waypoint-1-Small-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/Waypoint-1-Small-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from FastVideo/Waypoint-1-Small-Diffusers: direct link, hf CLI and curl.
- Browser
- Download file 444 Bytes
-
https://huggingface.co/FastVideo/Waypoint-1-Small-Diffusers/resolve/main/.gitattributes
- Command line
-
hf download hf://FastVideo/Waypoint-1-Small-Diffusers/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/FastVideo/Waypoint-1-Small-Diffusers/resolve/main/.gitattributes
444 Bytes
| transformer/diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| vae/diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| text_encoder/pytorch_model-00001-of-00002.bin filter=lfs diff=lfs merge=lfs -text | |
| text_encoder/pytorch_model-00002-of-00002.bin filter=lfs diff=lfs merge=lfs -text | |
| tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| tokenizer/spiece.model filter=lfs diff=lfs merge=lfs -text | |