Add PAI-Bench-C reproduction guide for Cosmos3#224
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Paibench-c results on demo 4 samples run by @trungtpham |
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@lfengad please help review this PR? Thanks |
Adds a self-contained end-to-end script and notebook for reproducing Cosmos3 PAI-Bench-C results using the public physical-ai-bench library. Key fixes: - Remove hardcoded --dp-shard-size/--dp-replicate-size/--cp-size/--cfgp-size from generation torchrun; let --parallelism-preset=latency auto-shard so Cosmos3-Super (32B) fits across multiple GPUs without OOM. - Include checkpoint slug in output path (demo-Cosmos3-Nano/, Cosmos3-Super/, etc.) so Nano and Super runs no longer overwrite each other. - Add SKIP_GEN=1 flag to skip generation and evaluate existing videos. - Strengthen paibench venv health check: detect broken Python symlinks from NFS stale handles and auto-rebuild the venv. - Add explicit UV_PROJECT_ENVIRONMENT to all paibench uv/pip calls so uv never auto-discovers a stale .venv from the working directory. - Compute GT seg/depth once (first modality) and reuse cache for the rest.
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QA evaluation report can be found at https://docs.google.com/document/d/1B6YkXoz9jnh4CBZqCuptLegHhqkorpa8BTQfKVkoP7w/edit?tab=t.ridcxjs8xsjg |
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Adds an end-to-end recipe for reproducing PAI-Bench Conditional Generation (PAI-Bench-C) results with Cosmos3 using the native Cosmos Framework PyTorch entrypoint.
New files under evaluation/cosmos3/generator/paibench_c/:
PAIBench-C Reproducibility
