wookiekim
FLUX.1-dev-SOLACE
Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: otherUsage
python
import torchfrom diffusers import FluxPipelinefrom peft import PeftModelmodel_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
bibtex
@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.
Model provider
wookiekim
Model tree
Base
black-forest-labs/FLUX.1-dev
Adapter
this model
Modalities
Input
Text
Output
Image
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information