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.
Extends the existing lazy-load pattern for text_encoder, tokenizer,
and audio_vae to the video VAE: each MagiHuman variant's converted
repo no longer needs to bundle a copy of the Wan 2.2 TI2V-5B VAE.
The four cross-variant shared components are now all fetched from
their canonical upstream HF repos at first build:
* text_encoder, tokenizer -> google/t5gemma-9b-9b-ul2 (gated)
* audio_vae -> stabilityai/stable-audio-open-1.0 (gated)
* vae -> Wan-AI/Wan2.2-TI2V-5B-Diffusers
Per-variant converted repo shrinks to transformer/ + scheduler/ +
model_index.json (~5 GB for base bf16, ~30 GB for distill bf16). All
variants share the same ~25 GB cache of upstream weights, so a user
running 4 variants ends up with ~75 GB total instead of ~400 GB.
Implementation:
* fastvideo/utils.py:verify_model_config_and_directory now treats
the contents of model_index.json as authoritative for which
component subfolders must exist locally. Pipelines that emit a
minimal model_index.json (omitting vae / text_encoder / etc.)
pass verification; pipelines that DO declare a component must
still ship its subfolder. transformer/ remains mandatory.
* fastvideo/pipelines/basic/magi_human/magi_human_pipeline.py adds
vae to the deferred list in load_modules and a new
_load_video_vae helper that prefers a bundled vae/ subfolder
(legacy converted repos) and falls back to snapshot_download +
the standard FV VAELoader. Both paths produce the same FV
AutoencoderKLWan, so production behavior is unchanged.
* convert_magi_human_to_diffusers.py docstring updated; --bundle-vae
flag is unchanged (still optional) but the README example now
omits it so new converted repos default to the minimal layout.
Verification:
* basic_magi_human.py with bundled vae/: byte-identical mp4 (md5
dcf5f2bf...) to pre-refactor output.
* basic_magi_human.py with vae/ moved out and removed from
model_index.json: same byte-identical mp4 via the lazy-load path.
* 4/4 magi-human parity tests pass with diff_max=0.0 / diff_mean=0.0
(DiT, pipeline, smoke + typed surface preflight).
- Drop misleading "T2V" framing — base MagiHuman is a joint audio-visual
generator. Rename MagiHumanT2VConfig→MagiHumanBaseConfig,
MagiHumanDistillT2VConfig→MagiHumanDistillConfig, presets
magi_human_base_t2v→magi_human_base, magi_human_distill_t2v→
magi_human_distill. Update comments / docstrings / example output
filename. Keep workload_type="t2v" string (framework enum has no
T2AV variant yet — same placeholder Stable Audio uses for T2A).
- Pipeline parity test: tighten atol/rtol from 0.5/0.5 to 0.35/0.05.
atol absorbs the observed worst-element drift (~0.31 on signal abs_mean
~2.4); the tight rtol still flags gross structural bugs.
- Wan VAE parity: import FastVideo's AutoencoderKLWan from
fastvideo.models.vaes.wanvae instead of diffusers. The test now
actually validates the class MagiHumanBaseConfig.vae_config resolves
to in production.
- Pipeline parity scheduler init: split per-side so upstream mirrors
MagiEvaluator's single-shift pattern (fresh FlowUniPCMultistepScheduler
+ shift only in set_timesteps) and FastVideo mirrors
MagiHumanDenoisingStage's double-shift pattern (constructor + set_timesteps
both with shift). Surfaces a real production divergence between
FastVideo's magi denoise loop and the official one for N>1 steps.
Ports GAIR-NLP/daVinci-MagiHuman's 15B-param joint-AV DiT into FastVideo:
text + video + audio joint denoise in one flat token stream, 32-step
FlowUniPC w/ CFG=2, Wan 2.2 TI2V-5B video VAE, Stable Audio Open 1.0
audio VAE (first-class port at fastvideo/models/vaes/oobleck.py, no
runtime diffusers import), T5-Gemma 9B UL2 text encoder, auto-muxed
mp4 with h264 video + aac stereo audio.
Parity tests all pass on converted weights: DiT (diff_median=2.6e-3),
text encoder (exact), video VAE (8e-4), audio VAE (exact), pipeline
latent (5e-2).
See .agents/skills/add-model/REVIEW.md for porting procedure + open
review items.
SolitaryThinker