Will Lin
5f1d2ef7d2
First bucket-B port toward 'support every fastvideo model': Wan2.1-T2V-14B reuses the Wan recipe + torch adapter unchanged (same WanTransformer3DModel/AutoencoderKLWan/UMT5) — only a registry entry + build_wan_t2v_14b_card (720p, flow_shift 5.0) + SamplingDefaults differ. Without the entry the arch fallback would give it the 1.3B 480p defaults; the explicit entry gives 50 steps / 720x1280. Also recorded the full backlog in V2_PORTING_STATUS.md: 63 fastvideo models = 8 ported / 21 bucket-B (reuse Wan/Causal/LTX2 arch — registry+recipe+defaults, no new adapter) / 34 bucket-C (13 new architectures needing a TorchComponent adapter). Updated the stale 'how to add a model' steps to the post-redesign structure (v2/recipes/, torch_backend.py, SamplingDefaults). CPU mini 211 pass, 2 skip.
Basic Video Generation Tutorial
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.
Requirements
- At least a single NVIDIA GPU with CUDA 12.4.
- Python 3.10-3.12
Installation
If you have not installed FastVideo, please following these instructions first.
Usage
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.
# if you have not cloned the directory:
git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
python examples/inference/basic/basic.py
For an example on Apple silicon:
python examples/inference/basic/basic_mps.py
For an example running DMD+VSA inference:
python examples/inference/basic/basic_dmd.py
For the typed config/request path added during the inference API refactor:
python examples/inference/basic/basic_dmd_new_api.py
Basic Walkthrough
All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
from fastvideo import VideoGenerator
def main():
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
num_gpus=1,
)
prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
"wide with interest. The playful yet serene atmosphere is complemented by soft "
"natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
video = generator.generate_video(prompt)
if __name__ == "__main__":
main()