Instructions to use FastVideo/stepvideo-t2v-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/stepvideo-t2v-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/stepvideo-t2v-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 vae/config.json from FastVideo/stepvideo-t2v-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 320 Bytes
-
https://huggingface.co/FastVideo/stepvideo-t2v-diffusers/resolve/main/vae/config.json
- Command line
-
hf download hf://FastVideo/stepvideo-t2v-diffusers/vae/config.json
-
curl -L -o config.json https://huggingface.co/FastVideo/stepvideo-t2v-diffusers/resolve/main/vae/config.json
320 Bytes
| { | |
| "_class_name": "AutoencoderKLStepvideo", | |
| "_diffusers_version": "0.31.0", | |
| "in_channels": 3, | |
| "out_channels": 3, | |
| "z_channels": 64, | |
| "num_res_blocks": 2, | |
| "version": 2, | |
| "frame_len": 17, | |
| "spatial_compression_ratio": 16, | |
| "temporal_compression_ratio": 8, | |
| "scaling_factor": 1.0 | |
| } | |