NVIDIA / NVIDIA/cosmos-framework

Need released-code recipe to reproduce Cosmos3 PAIBench-C transfer results

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Python
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Description

Hi, thanks for releasing Cosmos3.

I am trying to reproduce the Cosmos3 PAIBench-C transfer-generation results reported in the technical report/model documentation, especially the single-control segmentation setting.

The report describes the transfer-generation setup as using 50 denoising steps, text guidance 3, control guidance 1.5, shift 10, and full-range CFG. In the released cosmos-framework transfer inference path, I can find the standard guidance / shift / step settings, but I cannot find how to set the separate control-guidance weight described by the two-weight CFG setup.

I opened a related PAIBench issue for the evaluator/reference segmentation artifact side:
https://github.com/SHI-Labs/physical-ai-bench/issues/7

This issue is about the Cosmos3 inference/model side. Could you clarify:

  • the exact released-code command/config used for the PAIBench-C Table 16 transfer-generation evaluation
  • whether the two-weight CFG / separate control-guidance mechanism is implemented in the public repo
  • if implemented, which CLI/config fields correspond to text guidance and control guidance
  • the expected frame count and resolution for PAIBench-C reproduction
  • whether the reported Cosmos3 Nano/Super PAIBench-C numbers are reproducible from the current public checkpoints and code
  • any additional inference settings needed for the segmentation-control run

The goal is to distinguish a released-code Cosmos3 PAIBench-C reproduction from an approximate run using the public transfer inference defaults.

Thanks.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the released cosmos-framework transfer inference path and the technical report/model documentation, then compare them with the related PAIBench evaluator issue #7. Done means documenting the exact PAIBench-C Table 16 command/config, separate guidance fields if available, expected frame count and resolution, required segmentation-control settings, and checkpoint reproducibility.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Documentation
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
38/100

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