Reward_sana_idealized/README.md

# Reward SANA Idealized

This folder is a SANA-only reward-guided inference package.

## What is inside

- `models/reward_model.py`
  - Local SANA reward wrapper (no trainer import from other directories).
  - Loads base SANA diffusers modules and local reward checkpoint weights.
- `pipelines/sana_reward_pipeline.py`
  - SANA pipeline with per-step reward tracking.
- `pipelines/sana_gradient_ascent_pipeline.py`
  - SANA pipeline with gradient-ascent latent updates.
- `eval.py`
  - End-to-end evaluation script.
- `examples.sh`
  - Cluster entrypoint for prefetch and evaluation.

## Default checkpoint

`examples.sh` defaults to:

`/g/data/rr81/LPO/lrm/lrm_sana/logs/v8/reward_model/step_sana_sana_600m_512_variable-t_lr1e-5_step-8000_filter2_time951/checkpoint-gstep76000`

Override with:

```bash
LRM_MODEL_PATH=/path/to/checkpoint-dir-or-model.safetensors
```

## Run (10-sample smoke test)

```bash
cd /g/data/rr81/LPO/Reward_sana_idealized
OFFLINE_MODE=1 MAX_SAMPLES=10 MODE=gradient_ascent MODEL_PROFILE=sana_600m_512 ./examples.sh
```

## Notes

- Uses existing Python env: `/g/data/rr81/aev/bin/python`.
- GPU nodes should run with offline HF cache.