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How to decode renderer RGB preview faster with TAEHV with Agent Garten

Attach the TAEHV decoder to speed up RGB output while encoding stays on the Wan encoder.

  • Research
  • Intermediate
  • 2 hours
  • From the README
  • Any agent

Who it is forA researcher who reviews many renderer previews and finds RGB decoding slows each review cycle.

The problemFull Wan decoding makes it slow to check many rendered clips during a review session.

The outcomeFaster RGB decoding for previews, with encoding unchanged on the Wan encoder.

What you need first

  • A CUDA host with Python 3.11+ and PyTorch 2.9+
  • Access to the Hugging Face weights for Cosmos3-Nano and Wan2.2-TI2V-5B, plus disk space for them
  • Git and the hf command-line tool
  • For training: a JSONL manifest of your own clips with depth and normal data

Steps

  1. Complete the install and weight download steps from the Get started section.
  2. Download taew2_2_super.pth from the TAEHV project at https://github.com/madebyollin/taehv to a local path. (inferred, check the README)
  3. Add model.conditioner.codec.decoder={_target_:wm.codecs.TAEW22SuperDecoder,pretrained_path:/path/to/taew2_2_super.pth} as an extra override to the run command, replacing the path with yours.
  4. Run the validation render from the first use case, for example bash scripts/run.sh experiment=cosmos3/bidirectional train.max_iterations=0 train.validate_at_start=true trainer.callbacks.samples.every_n=1 with the override added. (inferred, check the README)

Prompt for your agent

Using the AgentGarten repo at <repo path>, add the TAEHV decoder override for the file at <taew2_2_super.pth path> and render the validation clips. Expected output: the same comparison videos under samples/, with RGB decoded through the TAEHV decoder. Verify that the decoder path in the override exists on disk before reporting success.

Caveat. The TAEHV decoder only changes RGB decoding, and the README states that encoding stays on the Wan encoder.

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