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.