Ports the daVinci-MagiHuman SR-540p inference flow to FastVideo for
both T2V and TI2V modes. SR-540p is a TWO-STAGE pipeline: the base
model produces a 256x480 latent, then a separate SR DiT (same arch as
base, different weights) refines it to 896x512. Mirrors upstream
MagiEvaluator.evaluate at video_generate.py:300-360.
Files added:
* fastvideo/pipelines/basic/magi_human/stages/sr_latent_preparation.py
* fastvideo/pipelines/basic/magi_human/stages/sr_denoising.py
* tests/local_tests/magi_human/test_magi_human_sr540p_pipeline_parity.py
Files modified (key changes):
* pipeline_configs.py: SR config classes with sr_* knobs sourced
from upstream EvaluationConfig
* presets.py: MAGI_HUMAN_SR_540P + MAGI_HUMAN_SR_540P_TI2V
* registry.py: SR-540p config entries
* magi_human_pipeline.py: MagiHumanSRPipeline + MagiHumanSRI2VPipeline
classes wiring the 9-stage chain (base denoise -> sr latent prep
-> sr denoise -> decode)
* fastvideo/models/loader/component_loader.py: registers
'sr_transformer' alongside transformer / transformer_2
* scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py:
--sr-source / --sr-subfolder flags for SR DiT into sr_transformer/
* examples/inference/basic/basic_magi_human_sr540p{,_ti2v}.py:
runnable
Verification:
* pytest tests/local_tests/magi_human/ -v -s: 12 passed, 0 failed.
All bit-exact (diff_max=0.0, diff_mean=0.0) including new SR-540p
T2V + TI2V parity tests.
* basic_magi_human.py (T2V regression check): mp4 byte-identical
md5 dcf5f2bf6534c7c0d91e7353e42b23db.
* basic_magi_human_sr540p.py: 896x512 mp4, coherent reading-on-
park-bench scene, much higher quality than base 480x256.
* basic_magi_human_sr540p_ti2v.py: 896x512 mp4 with reference-image-
conditioned saxophonist; reference conditioning preserved through
SR upscale.
Notes:
* Converted SR-540p artifact at converted_weights/magi_human_sr_540p
(~86 GB; both transformer/ and sr_transformer/) is gitignored.
* Base T2V/TI2V/distill flows untouched and bit-exact.
SolitaryThinker
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