metadata
base_model: black-forest-labs/FLUX.1-dev
library_name: peft
license: other
pipeline_tag: text-to-image
tags:
- text-to-image
- flux
- lora
- peft
- flow-grpo
- solace
- reinforcement-learning
FLUX.1-dev-SOLACE
LoRA adapter from SOLACE (Self-cOnfidence reward for aLigning text-to-imAge models via ConfidencE optimization), CVPR 2026.
SOLACE applied to FLUX.1-dev, using the model's own denoising confidence as an intrinsic reward (no external reward model at training time).
- Base model:
black-forest-labs/FLUX.1-dev - Method: SOLACE intrinsic self-confidence reward (built on Flow-GRPO)
- Code: https://github.com/wookiekim/SOLACE
- Adapter type: PEFT LoRA (rank 64) on the Flux transformer
Usage
import torch
from diffusers import FluxPipeline
from peft import PeftModel
model_id = "black-forest-labs/FLUX.1-dev"
lora_ckpt_path = "wookiekim/FLUX.1-dev-SOLACE"
device = "cuda"
pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_ckpt_path)
pipe.transformer = pipe.transformer.merge_and_unload()
pipe = pipe.to(device)
image = pipe(
"a photo of a cat wearing a small red hat",
height=512, width=512,
num_inference_steps=28, guidance_scale=3.5,
).images[0]
image.save("solace_flux.png")
Citation
@inproceedings{kim2026solace,
title={Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards},
author={Kim, Wookyoung and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2026}
}
Acknowledgments
This work builds upon Flow-GRPO by Jie Liu et al.