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Bruece
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Model Details
Model Provider
Bruece
Model Tree
black-forest-labs/FLUX.1-dev
Input Modalities
Output Modalities
Supported Functionality
Below is the code to load and merge the LoRA adapter with the base FLUX.1-dev model.
python
import torchfrom diffusers import FluxPipelinefrom peft import PeftModelmodel_id = "black-forest-labs/FLUX.1-dev"lora_ckpt_path = "Bruece/FLUX.1-dev-CMO"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)prompt = "a photo of a black kite and a green bear"image = pipe(prompt,height=512,width=512,num_inference_steps=40,guidance_scale=4.5).images[0]image.save("flux_cmo_lora.png")
If you find this ECCV 2026 model useful for your research, please cite:
bibtex
@article{wi2026correlation,title={Correlation-Weighted Multi-Reward Optimization for Compositional Generation},author={Wi, Jungmyung and Kim, Hyunsoo and Kim, Donghyun},journal={arXiv preprint arXiv:2603.18528},year={2026}}