Contrib: S3Diff one-step 4x super-resolution#149
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jimburtoft wants to merge 1 commit intoaws-neuron:mainfrom
Open
Contrib: S3Diff one-step 4x super-resolution#149jimburtoft wants to merge 1 commit intoaws-neuron:mainfrom
jimburtoft wants to merge 1 commit intoaws-neuron:mainfrom
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S3Diff (ECCV 2024) performs degradation-guided 4x super-resolution in a single denoising step using SD-Turbo with dynamic LoRA modulation. A DEResNet encoder estimates degradation and produces per-layer modulation matrices injected between LoRA A/B weights. Uses torch_neuronx.trace() (no TP needed, model is ~2 GB). Validated on trn2.3xlarge, SDK 2.29: 0.544s/image, ~21x CPU speedup. LoRA components use --model-type=unet-inference to avoid NaN from --auto-cast=matmult on small einsum operations.
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Summary
Adds S3Diff (ECCV 2024) one-step 4x super-resolution to contrib. S3Diff uses SD-Turbo's UNet with degradation-guided dynamic LoRA modulation: a DEResNet encoder estimates input degradation and produces per-layer
[rank, rank]modulation matrices injected between LoRA A/B weights via einsum.torch_neuronx.trace()(model is ~2 GB, no tensor parallelism needed)Benchmark Results (128x128 -> 512x512)
Implementation Notes
--model-type=unet-inferenceinstead of--auto-cast=matmultbecause the small einsum modulation operations produce NaN under BF16 auto-casting.--auto-cast=matmultnormally.Files
src/modeling_s3diff.py— Full pipeline: DEResNet, LoRA forward, wrappers, S3DiffNeuronPipeline classsrc/generate_s3diff.py— CLI generation script with weight download supporttest/integration/test_model.py— 3 integration tests (smoke, SR size, timing)README.md— Full documentationTests
All 3 integration tests pass on trn2.3xlarge:
test_smoke_pipeline_loads— PASSEDtest_sr_produces_correct_size— PASSED (512x512 output, pixel std=19.9)test_warm_generation_time— PASSED (0.544s < 2s threshold)Weights
stabilityai/sd-turbozhangap/S3Diff