{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "worldbench-diffusionopsd--check-reward-stack-before-training",
 "repo": "worldbench/DiffusionOPSD",
 "repo_url": "https://github.com/worldbench/DiffusionOPSD",
 "repo_slug": "worldbench-diffusionopsd",
 "title": "Verify reward models load before a long training run",
 "page_title": "How to verify reward models load before a long training run with Diffusion OPSD",
 "summary": "Run the README setup checks and gradient smoke tests so a failed reward stack does not waste GPU hours.",
 "who": "A researcher about to start a costly run who needs to confirm HPSv3 or DeQA work on their machine.",
 "problem": "Heavy reward models use separate dependency stacks, and a broken install can fail only after training has begun.",
 "outcome": "Pass or fail results for the HPSv3 and DeQA setup checks and differentiable reward path, so the team knows the stack is usable.",
 "steps": [
  "Create a fresh Python 3.10 or 3.11 environment and install a CUDA-matched PyTorch build first, then run `pip install -e .`.",
  "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.",
  "Run `python scripts/check_reward_setup.py --backbone sd35 --reward hpsv3`.",
  "Run `python scripts/check_reward_setup.py --backbone sd35 --reward deqa`.",
  "Run `python scripts/smoke_reward_gradient.py --reward hpsv3` and `python scripts/smoke_reward_gradient.py --reward deqa`."
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "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.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/worldbench-diffusionopsd/check-reward-stack-before-training/",
 "md": "https://dedupedai.com/use-cases/worldbench-diffusionopsd/check-reward-stack-before-training/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/worldbench-diffusionopsd--check-reward-stack-before-training.json",
 "api_version": "v1"
}