SolitaryThinker
d6e020402e
Phase 5: with all 4 weight variants now uploaded under
FastVideo/MagiHuman-Diffusers (one HF repo, four sibling subfolders
base / distill / sr_540p / sr_1080p), all magi-human examples now
default to the umbrella string. Local conversion via the
checkpoint_conversion script remains supported and is documented in
the example docstrings.
Files updated:
* basic_magi_human.py: base T2V -> base
* basic_magi_human_ti2v.py: base TI2V -> base
* basic_magi_human_distill.py: distill T2V -> distill
* basic_magi_human_distill_ti2v.py: distill TI2V-> distill
* basic_magi_human_sr540p.py: sr-540p T2V -> sr_540p
* basic_magi_human_sr540p_ti2v.py: sr-540p TI2V-> sr_540p
* basic_magi_human_sr1080p.py: sr-1080p T2V-> sr_1080p
* basic_magi_human_sr1080p_ti2v.py: sr-1080p TI2V-> sr_1080p
* fastvideo/registry.py: hf_model_paths for the base T2V config now
includes 'FastVideo/MagiHuman-Diffusers/base' and the distill T2V
config includes 'FastVideo/MagiHuman-Diffusers/distill', alongside
the existing per-variant repo names. The SR umbrella paths
(sr_540p / sr_1080p) were already registered by Phases 3/4. TI2V
variants reuse the same weight subfolders and are selected at
load time via override_pipeline_cls_name + pipeline_config (see
basic_magi_human_ti2v.py for the pattern).
Verified end-to-end:
* pytest tests/local_tests/magi_human/ -v -s: 14 passed, 0 failed.
All bit-exact (diff_max=0.0, diff_mean=0.0).
* basic_magi_human.py with local converted_weights/magi_human_base
moved out, forcing snapshot_download from the umbrella repo:
mp4 byte-identical md5 dcf5f2bf6534c7c0d91e7353e42b23db (matches
pre-upload local-path output exactly).
Notes:
* fastvideo/utils.py:maybe_download_model already supports the
'org/repo/subfolder' umbrella form (committed in e2ef3234), and
fastvideo/pipelines/basic/magi_human/magi_human_pipeline.py
lazy-loads the four shared components (Wan VAE, T5-Gemma encoder
+ tokenizer, Stable Audio VAE) from their canonical upstream HF
repos so each umbrella subfolder only ships transformer/ +
scheduler/ + (sr_transformer/) + model_index.json.
* Total HF Hub footprint after upload: ~165 GB raw, server-side
deduped. User cache footprint per variant is ~5-30 GB transformer
(+ ~30 GB sr_transformer for SR) plus a single ~25 GB upstream
cache shared across all variants.
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Minimal user-runnable example for the daVinci-MagiHuman base AV pipeline.
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Produces an mp4 with both video (Wan 2.2 TI2V-5B VAE) and audio (Stable
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Audio Open 1.0 VAE, first-class FastVideo port in
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`fastvideo/models/vaes/oobleck.py`) muxed together via PyAV.
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Prerequisites (one-off):
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# Accept terms of use 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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# All four cross-variant shared components (Wan 2.2 VAE, T5-Gemma
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# encoder + tokenizer, Stable Audio VAE) are lazy-loaded from their
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# canonical upstream HF repos on first build, so a single ~25 GB
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# cache is shared across every MagiHuman variant.
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The umbrella HF repo `FastVideo/MagiHuman-Diffusers` holds all four
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variants (base / distill / sr_540p / sr_1080p) under sibling subfolders
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and FastVideo will download just the requested subfolder. Local
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conversion via `scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py`
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is also supported.
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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/base",
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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.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_base):
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# height=256, width=448, fps=25, num_inference_steps=32, seed=42.
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# Override here only if you have a specific QA scenario.
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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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