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How to render comparison videos from a renderer checkpoint with Agent Garten

Check a renderer checkpoint visually by writing validation videos without training.

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

Who it is forAn ML researcher who has a renderer checkpoint and needs to judge its visual quality before deciding what to do next.

The problemJudging a checkpoint by loss numbers alone does not show whether the rendered video looks right against ground truth.

The outcomeDepth, normal, ground truth, and sample comparison videos written under samples/ for visual review.

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. git clone https://github.com/MirroS-Lab/AgentGarten.git
  2. pip install -e '.[data,media,wandb,te]'
  3. cp .env.example .env
  4. Run hf download nvidia/Cosmos3-Nano --include 'transformer/*' 'text_tokenizer/*' 'assets/negative_prompt.json' --local-dir weights/Cosmos3-Nano and hf download Wan-AI/Wan2.2-TI2V-5B Wan2.2_VAE.pth --local-dir weights/Wan2.2-TI2V-5B, then set WM_COSMOS3_NANO and WM_WAN22_VAE in .env to those paths. (inferred, check the README)
  5. bash scripts/run.sh experiment=cosmos3/bidirectional train.max_iterations=0 train.validate_at_start=true trainer.callbacks.samples.every_n=1 (inferred, check the README)

Prompt for your agent

Using the AgentGarten repo at <repo path>, set up the environment, download the weights listed in the README, and render the validation clips of the checkpoint at <checkpoint path> without training. Expected output: comparison videos (depth, normal, ground truth, sample) under samples/. Verify that the samples/ folder contains one video set per validation clip before reporting success.

Caveat. The README does not show how to load a specific checkpoint path directly, so the checkpoint may need to be selected through the training config (inferred).

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