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How to verify reward models load before a long training run with Diffusion OPSD

Run the README setup checks and gradient smoke tests so a failed reward stack does not waste GPU hours.

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

Who it is forA researcher about to start a costly run who needs to confirm HPSv3 or DeQA work on their machine.

The problemHeavy reward models use separate dependency stacks, and a broken install can fail only after training has begun.

The outcomePass or fail results for the HPSv3 and DeQA setup checks and differentiable reward path, so the team knows the stack is usable.

What you need first

  • A recent Linux machine with CUDA GPUs (eight-GPU profiles are reported in the README)
  • Python 3.10 or 3.11 and conda
  • A Hugging Face account that has accepted the SD3.5-M license, for the gated model
  • Enough disk space for model weights, reward checkpoints, and the Pick-a-Pic prompt manifest

Steps

  1. Create a fresh Python 3.10 or 3.11 environment and install a CUDA-matched PyTorch build first, then run pip install -e ..
  2. Run pip install --no-deps 'hpsv3==1.0.0' and then the listed pip install line with qwen-vl-utils, omegaconf, safetensors, and the other packages from the README.
  3. Run python scripts/check_reward_setup.py --backbone sd35 --reward hpsv3.
  4. Run python scripts/check_reward_setup.py --backbone sd35 --reward deqa.
  5. Run python scripts/smoke_reward_gradient.py --reward hpsv3 and python scripts/smoke_reward_gradient.py --reward deqa.

Prompt for your agent

In the repo worldbench/DiffusionOPSD, run the reward setup checks for <reward name> with the SD3.5 backbone and the gradient smoke test. Report which commands passed or failed and the error text for any failure. Verify that the smoke test ran the differentiable reward path before reporting success.

Caveat. The README warns against normal dependency resolution for HPSv3 and says a separate heavy-reward environment is needed, so mixing it into the main environment can break the Transformers version.

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