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 step_llm/step1_chat_tokenizer.model from FastVideo/stepvideo-t2v-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 1.05 MB
-
https://huggingface.co/FastVideo/stepvideo-t2v-diffusers/resolve/main/step_llm/step1_chat_tokenizer.model
- Command line
-
hf download hf://FastVideo/stepvideo-t2v-diffusers/step_llm/step1_chat_tokenizer.model
-
curl -L -o step1_chat_tokenizer.model https://huggingface.co/FastVideo/stepvideo-t2v-diffusers/resolve/main/step_llm/step1_chat_tokenizer.model
1.05 MB
- Xet hash:
- d84ea9d21eb5d079a47c4955db3cefa97fbe6742e57d9c7bc331dd9d3be1781d
- Size of remote file:
- 1.05 MB
- SHA256:
- ae238f74162b98d3384eb1ceef143ed67f822528a6b0d6c5e351f87d7f4a651a
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