Ports the daVinci-MagiHuman SR-1080p inference flow to FastVideo. Builds
on the SR-540p two-stage pipeline plus block-sparse local-window
video->video attention on 32 of 40 SR DiT layers. Mirrors upstream
SR2_1080 config override at inference/common/config.py:225-244 and
calc_local_qk_range at inference/pipeline/data_proxy.py:31-79.
Files added:
* tests/local_tests/magi_human/test_magi_human_sr1080p_pipeline_parity.py:
parametric parity test for both T2V and TI2V modes. Both pass
diff_max=0.0000 / diff_mean=0.0000 -- BIT-EXACT.
Files modified (key changes):
* fastvideo/models/dits/magi_human.py:
- AttentionSubConfig.use_local_attn / frame_receptive_field
- MagiAttention.configure_local_attention(): per-layer toggle
- MagiAttention._sdpa(): thin SDPA wrapper for [L,H,D] tensors
- MagiAttention._local_window_attention(): 3-block accumulator
mirroring upstream FFA semantics with vanilla SDPA segments
(per-frame video window + all video->audio+text + audio+text->all).
- MagiAttention.forward() dispatches to _local_window_attention
when use_local_attn flag is set.
- MagiTransformerLayer wires use_local_attn from arch.local_attn_layers.
- MagiHumanDiT.configure_local_attention() top-level toggle.
* pipeline_configs.py: MagiHumanSR1080pConfig + I2V variant with
sr_local_attn_layers populated to upstream's 32 indices.
* presets.py: MAGI_HUMAN_SR_1080P + MAGI_HUMAN_SR_1080P_TI2V presets.
* registry.py: SR-1080p config entries with detectors.
* magi_human_pipeline.py: SR-1080p pipeline classes activating
local_attn_layers on the SR DiT at construction.
* scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py:
--sr-subfolder 1080p_sr support.
* tests/local_tests/helpers/magi_human_upstream.py: arch override
support for SR-1080p parity test.
* test_magi_human_pipeline_smoke.py: preset set expanded.
* basic_magi_human_sr1080p{,_ti2v}.py: stubs -> runnable.
Verification:
* pytest tests/local_tests/magi_human/ -v -s: 14 passed, 0 failed.
All bit-exact including SR-1080p T2V and TI2V parity. The
block-sparse SDPA-segmented implementation matches upstream FFA's
q_ranges/k_ranges accumulator semantics exactly for the 3-block
layout (overlap accumulation handled by explicit '+' at
magi_human.py:421-425).
* basic_magi_human.py (T2V regression check): mp4 byte-identical
md5 dcf5f2bf6534c7c0d91e7353e42b23db.
* Base/Distill/TI2V/SR-540p flows untouched.
Notes:
* Converted SR-1080p artifact at /raid/.../magi_human_sr_1080p
(~58 GB), symlinked at converted_weights/magi_human_sr_1080p
(gitignored).
* Upstream's flex_flash_attn_func via SandAI-org/MagiAttention is
NOT a dependency; FV's pure-SDPA segmented implementation is
mathematically equivalent for this 3-block layout.
SolitaryThinker
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