# Migration Notes: Flux -> SANA LRM

## Reused Components
- Training loop orchestration from `flux/trainer/scripts/train.py`.
- Accelerator stack (`base`, `debug`, `deepspeed`).
- Optimizer and LR scheduler modules.
- Pairwise loss structure and evaluation flow.
- Dataset filtering logic including pseudo-preference support.

## SANA-Specific Adaptations
- Replace Flux model wrapper with SANA latent-transformer wrapper.
- Replace Flux tokenizer/text path with variant-aware SANA path.
- Keep pairwise reward objective unchanged; adapt only feature extraction.
- Add model profile support for four requested checkpoints.

## Naming Changes
- `step_flux` -> `step_sana`
- `step_flux_base` -> `step_sana_base`
- `step_clip_flux` -> `step_clip_sana`
- `FluxPreferenceModel` -> `SanaPreferenceModel`

## Config Baseline Choice
- Dataset source follows flux working setting:
  - `pickapic-anonymous/pickapic_v1`
- Pseudo preference CSV path follows flux launcher fallback behavior.
- Logging/output path remains flux-style to simplify operational parity.

## Runtime Notes
- Quick profile should force minimal run:
  - test split for train/valid/test
  - image size downscaled
  - `max_steps=1`
- Main profile should use production defaults with DeepSpeed sharded mode.

## Known Risks During Migration
- Mismatch between tokenizer outputs and model forward signature.
- Caption embedding/channel shape mismatch in SANA transformer conditioning.
- Missing subfolders for some checkpoints (tokenizer_2 or text_encoder_2).
- Diffusers/transformers version compatibility for SANA classes.

## Mitigation Strategy
- Use profile metadata for each checkpoint (explicit, not implicit only).
- Fail fast with clear validation errors at model init time.
- Keep quick-run smoke checks as gate before longer runs.
- Preserve fallback behavior from flux launcher for data and cache paths.