SolitaryThinker
0d99e47e16
Ports the daVinci-MagiHuman TI2V branch to FastVideo for both base
and distill variants. The TI2V case takes a reference image, encodes
it through the Wan VAE, and overwrites the first frame's video latent
with the encoded image latent at every denoise step (mirrors upstream
inference/pipeline/video_generate.py:300-360 evaluate + 424-425
per-step overwrite).
Files added:
* fastvideo/pipelines/basic/magi_human/stages/reference_image.py:
new MagiHumanReferenceImageStage. Loads PIL image (or path),
resizecrops to (height, width) matching upstream resizecrop,
runs VideoProcessor.preprocess at vae_scale_factor=16, encodes
via the Wan VAE (uses .mean for deterministic latent), applies
shift_factor / scaling_factor normalization, stashes on
batch.image_latent.
* tests/local_tests/magi_human/test_magi_human_ti2v_pipeline_parity.py:
bit-exact parity test against upstream MagiEvaluator's TI2V denoise
(with reference image conditioning). Passes
ti2v video diff_max=0.0000 diff_mean=0.0000.
Files modified:
* pipeline_configs.py: MagiHumanBaseI2VConfig keeps the VAE encoder
loaded (load_encoder=True) so the reference image path can encode.
* presets.py: MAGI_HUMAN_BASE_TI2V and MAGI_HUMAN_DISTILL_TI2V presets
with workload_type=i2v.
* registry.py: TI2V config entries for both base and distill variants.
* magi_human_pipeline.py: MagiHumanI2VPipeline subclass that inserts
the MagiHumanReferenceImageStage between prompt encoding and latent
preparation. Reuses the lazy-load path for shared components.
* stages/latent_preparation.py: pre-loop overwrite of
latent_video[:, :, :1] with batch.image_latent[:, :, :1] when
image conditioning is present (matches upstream
evaluate_with_latent line 425 first-iteration overwrite).
* stages/denoising.py: per-step _overwrite_first_frame helper that
applies the same overwrite at the start of every denoise step
(matches upstream evaluate_with_latent line 424 in-loop overwrite).
static_packed rebuild moved inside the loop after the overwrite
so packed video tokens reflect the conditioned latent.
* examples/inference/basic/basic_magi_human_ti2v.py: rewritten from
NotImplementedError stub to runnable. Uses local
converted_weights/magi_human_base + the existing example
saxophonist reference image; produces a coherent mp4 with the
image-conditioned subject.
* examples/inference/basic/basic_magi_human_distill_ti2v.py: same
pattern against converted_weights/magi_human_distill (runnable
once distill weights are converted).
Verification:
* pytest tests/local_tests/magi_human/ -v -s: 10 passed, 0 failed.
All bit-exact (diff_max=0.0, diff_mean=0.0) including new
test_magi_human_distill_dit_parity (Phase 1) and
test_magi_human_ti2v_pipeline_parity (Phase 2) tests.
* basic_magi_human.py (T2V regression check): mp4 byte-identical
md5 dcf5f2bf6534c7c0d91e7353e42b23db.
* basic_magi_human_ti2v.py: produces coherent saxophonist scene
matching the reference image conditioning. Frames at
/tmp/opencode/ti2v_frame_*.png.
T2V flow unchanged: both T2V example and base parity test produce
identical output to pre-change. TI2V is purely additive on top.
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()