license: apache-2.0
base_model: stabilityai/stable-diffusion-3.5-medium
library_name: peft
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-3
- lora
- diffusionnft
- reinforcement-learning
NFT-OR scratch LoRA (SD3.5-Medium)
From-scratch DiffusionNFT LoRA checkpoints for HaC-RL / NFT-OR.
Base model: stabilityai/stable-diffusion-3.5-medium.
LoRA: rank 32, alpha 64, attention projections only.
What is uploaded
Inference adapters only (adapter_config.json + adapter_model.safetensors). Optimizer / scaler states are not included.
| Path | Run | Epochs | Adapter |
|---|---|---|---|
scratch-nft/checkpoint-180/ |
scratch DiffusionNFT, job 219145 |
200 (last save is 180) | EMA stored in lora/ (legacy save) |
scratch-nft-or/checkpoint-180/ |
scratch NFT-OR or_mix, job 219585 |
180 | EMA (lora_ema/) |
scratch-nft-or/checkpoint-200/ |
same run | 200 | EMA (lora_ema/) |
The NFT 200-epoch job only checkpointed at epoch starts (save_freq=20), so its last file is checkpoint-180, not 200.
Training recipe (both): Pick-a-Pic, PickScore+CLIP+HPSv2, 512², 40 sample steps, β=0.1, 4×A800, seed 42.
DrawBench (1024², 40 steps, EMA)
| Checkpoint | ImageReward | CLIP | Aesthetic | PickScore | HPSv2 | avg |
|---|---|---|---|---|---|---|
| scratch-nft ckpt-180 | 1.194 | 0.280 | 5.888 | 0.895 | 0.316 | 8.573 |
| scratch-nft-or ckpt-180 | 1.292 | 0.285 | 5.938 | 0.897 | 0.321 | 8.733 |
| scratch-nft-or ckpt-200 | 1.306 | 0.286 | 5.951 | 0.896 | 0.319 | 8.757 |
The NFT-OR run used collinear implicit_positive OR (α=0.05), which saturates after epoch 0. Do not treat the score gap as causal evidence that OR is active.
Load
from diffusers import StableDiffusion3Pipeline
import torch
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-medium",
torch_dtype=torch.bfloat16,
)
pipe.load_lora_weights("HaC-RL/NFT-OR", subfolder="scratch-nft-or/checkpoint-200")
For the matched DrawBench row, use subfolder="scratch-nft/checkpoint-180" or scratch-nft-or/checkpoint-180.