Will LinandClaude Fable 5
9df19be719
basic_fast_minimax_h3.py runs FastVideo/FastVideo-Minimax-H3-Preview-v0.1, the data-free-DMD2 distillation of MiniMax-H3: 4 denoising steps on the release sampler's shift-12 schedule (vs the base model's 50), synchronized video+audio in one pipeline call, guidance_scale 1.0. The script always requests the VSA-H3 attention backend through the typed boot-time route (pipeline.experimental -> FastVideoArgs.attention_backend): the student checkpoint carries trained to_gate_compress gates, which only exist under that backend. --vsa-sparsity defaults to 0.0 (every tile selected — exactly dense attention); --vsa-tile-size defaults to 64, the geometry the student was trained with, and is forwarded even at sparsity 0 because the gate-compress branch pools per tile. The HF repo is private while the MiniMax H3 Community License review completes; --model-path accepts a local snapshot meanwhile (noted in the script and README). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
63 lines
2.2 KiB
Markdown
63 lines
2.2 KiB
Markdown
# Basic Video Generation Tutorial
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The `VideoGenerator` class provides the primary Python interface for doing offline video generation, which is interacting with a diffusion pipeline without using a separate inference api server.
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## Requirements
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- At least a single NVIDIA GPU with CUDA 12.4.
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- Python 3.10-3.12
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## Installation
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If you have not installed FastVideo, please following these [instructions](https://hao-ai-lab.github.io/FastVideo/getting_started/installation) first.
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## Usage
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The first script in this example shows the most basic usage of FastVideo. If you are new to Python and FastVideo, you should start here.
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```bash
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# if you have not cloned the directory:
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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python examples/inference/basic/basic.py
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```
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For an example on Apple silicon:
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```
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python examples/inference/basic/basic_mps.py
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```
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For an example running DMD+VSA inference:
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```
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python examples/inference/basic/basic_dmd.py
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```
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For the typed config/request path added during the inference API refactor:
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```
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python examples/inference/basic/basic_dmd_new_api.py
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```
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For the few-step (4-step, DMD2-distilled) MiniMax-H3 preview, generating synchronized video and audio, optionally with block-sparse VSA attention:
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```
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python examples/inference/basic/basic_fast_minimax_h3.py --prompt "your prompt" [--vsa-sparsity 0.9]
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```
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The default checkpoint `FastVideo/FastVideo-Minimax-H3-Preview-v0.1` is private on the Hub while its license review completes; until it flips public, pass `--model-path` with a local snapshot of the release.
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## Basic Walkthrough
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All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
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```python
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from fastvideo import VideoGenerator
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def main():
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=1,
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)
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prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
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"wide with interest. The playful yet serene atmosphere is complemented by soft "
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"natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
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video = generator.generate_video(prompt)
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if __name__ == "__main__":
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main()
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```
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