Simplify model card

README.md

@@ -50,37 +50,3 @@ model, model_config, training_config, step = load_checkpoint(
 ```
 
 Select `FM_Condition_gradient/latest.pt` for the gradient-loss model.
-
-## HiPredict API
-
-The HiPredict API adapter reconstructs RED4 (`ir2red`), on patches or full strips. Large images are predicted with overlapping tiles.
-
-```python
-from hipredict.diffusion import build_hipredict_predictor
-
-predictor, config, step = build_hipredict_predictor(checkpoint, device="cuda")
-samples = predictor.load("files/data_record_evals.csv", mode="patches")
-predictions = predictor.predict(samples)
-```
-
-For `red2ir`, use the diffusion inference command with the downloaded checkpoint path:
-
-```bash
-python -m hipredict.diffusion.inference.inference     --ckpt_path /path/to/latest.pt     --input_path observation_RED4.npy     --red3_path observation_RED3.npy     --direction red2ir     --output_dir outputs/inference/
-```
-
-## Brightness correction
-
-The default correction uses the RED3 boundary. To use the learned residual brightness head for dataset inference or evaluation, download `brightness/model.pt`, select `--dc_correct head`, and pass its local path as `--head_ckpt`.
-
-## Model configuration
-
-Both FM checkpoints use a 128-channel base width, three residual blocks per level, Sobel edge conditioning, and noise-conditioned flow matching. Their normalization settings are stored in the checkpoint:
-
-- Center: `0.103687465`
-- Scale: `0.04842728`
-- Normalized training values clipped to `[-5, 5]`
-
-HiPredict reads these settings when loading the checkpoint. Evaluation uses original, unclipped physical targets. The checkpoint format is `hipredict.diffusion`, version `1`.
-
-File SHA-256 digests are recorded in `checksums.json`.