will/magi
3
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 SolitaryThinker
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d6e020402e |
[feat] magi-human: switch all 8 examples to umbrella HF repo + register umbrella paths
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.
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|
SolitaryThinker
|
0d99e47e16 |
[feat] magi-human: TI2V (text+image-to-AV) inference flow
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.
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|
SolitaryThinker
|
e114fba53f |
[docs] examples: add basic_* stubs for remaining magi-human variants
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.
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