SolitaryThinker
e54106110e
The activation switch. After this PR merges, MagiHuman is publicly
loadable via:
from fastvideo import VideoGenerator
gen = VideoGenerator.from_pretrained('FastVideo/MagiHuman-Diffusers/base')
Files:
- fastvideo/registry.py: 153 lines of register_configs/register_presets
calls covering all 4 variants x 2 modes (8 total entrypoints).
- examples/inference/basic/basic_magi_human{_,_ti2v,_distill,_distill_ti2v,
_sr540p,_sr540p_ti2v,_sr1080p,_sr1080p_ti2v}.py: 8 user-facing scripts.
- fastvideo/tests/ssim/test_magi_human_similarity.py: CI-eligible SSIM
regression test against the umbrella HF repo.
- .agents/memory/codebase-map/models/magi_human.md: codebase-map entry
(first per-model entry under the models/ subdir; sets the convention).
- fastvideo/pipelines/basic/magi_human/AGENTS.md: provenance section
finalized with all 8 PR numbers + the will/magi source SHA.
Verification:
- Existing tests on main: should still pass (no shared component changes
in this PR; the umbrella loader infra landed in PR-B / loader-infra).
- pre-commit run --all-files: clean.
- E2E smoke after activation: any of the 8 examples emits expected mp4
with hash dcf5f2bf6534c7c0d91e7353e42b23db on the base T2V variant.
Magi-Stack: 8/8
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Minimal user-runnable example for the daVinci-MagiHuman DMD-2 distilled
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text-to-AV pipeline.
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Same arch as the base model (`basic_magi_human.py`) but with DMD-2 distilled
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weights: 8 denoising steps, no classifier-free guidance. ~4x faster than
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base at the same 256x480 resolution. Mirrors upstream
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`daVinci-MagiHuman/example/distill/run_T2V.sh`.
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Prerequisites (one-off):
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# 1) Accept terms on the gated HF repos with your HF_TOKEN:
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# - https://huggingface.co/google/t5gemma-9b-9b-ul2
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# - https://huggingface.co/stabilityai/stable-audio-open-1.0
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# Cross-variant shared components (Wan 2.2 VAE + T5-Gemma + Stable
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# Audio VAE) are lazy-loaded from their canonical upstream HF repos
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# and shared with the base variant cache.
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# 2) Convert the distill subfolder of GAIR/daVinci-MagiHuman:
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python scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py \\
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--source GAIR/daVinci-MagiHuman \\
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--subfolder distill \\
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--output converted_weights/magi_human_distill \\
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--cast-bf16
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# `--cast-bf16` is recommended (61 GB fp32 -> 30 GB bf16); the FV pipeline
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# loads bf16 anyway, and the conversion keeps norms / RoPE bands fp32.
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"""
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from fastvideo import VideoGenerator
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PROMPT = (
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"A warm afternoon scene: a person sits on a park bench reading a book, "
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"surrounded by softly swaying trees."
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)
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def main() -> None:
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generator = VideoGenerator.from_pretrained(
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"FastVideo/MagiHuman-Diffusers/distill",
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num_gpus=1,
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)
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output_path = "outputs_video/magi_human_basic/output_magi_human_distill.mp4"
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generator.generate_video(
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prompt=PROMPT,
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output_path=output_path,
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save_video=True,
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# Defaults pulled from the registered preset (magi_human_distill):
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# height=256, width=480, fps=25, num_inference_steps=8, cfg=1, seed=42.
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)
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generator.shutdown()
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if __name__ == "__main__":
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main()
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