| import torch | |
| from diffusers import DiffusionPipeline, ControlNetModel | |
| from diffusers.utils import load_image | |
| import gradio as gr | |
| controlnet = ControlNetModel.from_pretrained( | |
| "jasperai/Flux.1-dev-Controlnet-Upscaler", | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-dev", | |
| controlnet=controlnet, | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| pipe.to("cpu") | |
| def enhance_image(input_image): | |
| control_image = load_image(input_image) | |
| w, h = control_image.size | |
| control_image = control_image.resize((w * 4, h * 4)) | |
| image = pipe( | |
| prompt="", | |
| control_image=control_image, | |
| controlnet_conditioning_scale=0.6, | |
| num_inference_steps=28, | |
| guidance_scale=3.5, | |
| height=control_image.size[1], | |
| width=control_image.size[0] | |
| ).images[0] | |
| return image | |
| interface = gr.Interface( | |
| fn=enhance_image, | |
| inputs=gr.inputs.Image(type="filepath"), | |
| outputs=gr.outputs.Image(type="pil"), | |
| title="Image Enhancer using FLUX.1-dev", | |
| description="Upload an image to enhance using the FLUX.1-dev model." | |
| ) | |
| interface.launch() |