SolitaryThinker
e114fba53f
Upstream daVinci-MagiHuman ships 4 model variants x 2 input modes = 8
inference entrypoints (base / distill / sr_540p / sr_1080p, each in
T2V and TI2V mode). FastVideo currently has working code for the base
T2V path (basic_magi_human.py) and a registered preset for distill
T2V (magi_human_distill, but no example until now).
Add example files for the 7 remaining variants:
* basic_magi_human_distill.py -- runnable T2V example for the
DMD-2 distilled model. Just point conversion at the distill
subfolder and the existing magi_human_distill preset takes over.
* basic_magi_human_ti2v.py
basic_magi_human_distill_ti2v.py -- not-yet-ported TI2V (image
conditioning) variants. Each docstring lists the pipeline-side
work that is missing in FastVideo (VAE encoder load, reference
image stage, latent_video[..., :1] overwrite at every denoise
step, new I2V config + preset). main() raises NotImplementedError
with a pointer to magi-human.md.
* basic_magi_human_sr540p.py
basic_magi_human_sr540p_ti2v.py -- not-yet-ported super-resolution
to 540p. Docstring describes the upstream two-stage flow (base ->
SR latent prep with trilinear up + ZeroSNR noise -> SR DiT -> Wan
VAE) and lists the FV components needed (MagiHumanSR540pConfig,
SR latent prep stage, SR denoise stage with cfg-trick guidance
tensor, conversion script invocation for the 540p_sr subfolder,
new preset + registry entry).
* basic_magi_human_sr1080p.py
basic_magi_human_sr1080p_ti2v.py -- not-yet-ported SR-1080p.
Same SR-540p scaffolding plus a block-sparse local-window
attention path (32 of 40 SR DiT layers). The docstring points at
upstream's FFAHandler q_ranges/k_ranges blocks in dit_module.py
and notes that MagiAttention currently always runs full SDPA.
All stubs follow the existing example file convention (SPDX header,
focused docstring, single main()). The not-yet-ported stubs exit with
NotImplementedError so they fail loudly rather than silently misbehaving;
each error message points at the docstring for the missing-component
checklist.
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()