Will Lin
d891771ba3
GPU verification (Wan2.1-Fun... no: FastWan2.1-T2V-1.3B) failed at load: 'Parameter blocks.0.to_gate_compress.bias not found in custom model state dict' — the FastVideo/* DMD-distilled checkpoints carry gated-attention params (to_gate_compress) that the generic WanTransformer3DModel loader can't map. v2/registry.py's select_by_architecture ALREADY rejects WanDMDPipeline for exactly this reason; my explicit ModelEntry wrongly bypassed it. Reverted FastWan (1.3B + 14B-480P) and the unverified VSA-14B alias (same FastVideo/* risk). Kept the official Wan2.1-T2V-14B (standard weights, same loader path as the GPU-verified 1.3B). Lesson recorded in V2_PORTING_STATUS.md: FastWan/Turbo/VSA need a param-mapping fix (like LTX-2.3 did), not just a schedule — bucket-C-effort. GPU-verify every port before claiming support. CPU mini 211/2.
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()