from PIL import Image import gradio as gr from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler import torch controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float32, use_safetensors=True) pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float32, ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # pipe.enable_xformers_memory_efficient_attention() pipe.enable_model_cpu_offload() def infer(prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed): conditioning_image = Image.fromarray(conditioning_image) conditioning_image = conditioning_image.convert('L') generator = torch.Generator(device="cpu").manual_seed(seed) output_image = pipe( prompt, conditioning_image, height=size, width=size, num_inference_steps=num_inference_steps, generator=generator, negative_prompt=negative_prompt, guidance_scale=guidance_scale, controlnet_conditioning_scale=1.0, ).images[0] return output_image with gr.Blocks() as demo: gr.Markdown( """ # ControlNet on Brightness This is a demo on ControlNet based on brightness. """) with gr.Row(): with gr.Column(): prompt = gr.Textbox( label="Prompt", ) negative_prompt = gr.Textbox( label="Negative Prompt", ) conditioning_image = gr.Image( label="Conditioning Image", ) with gr.Accordion('Advanced options', open=False): with gr.Row(): num_inference_steps = gr.Slider( 10, 40, 20, step=1, label="Steps", ) size = gr.Slider( 256, 768, 512, step=128, label="Size", ) with gr.Row(): guidance_scale = gr.Slider( label='Guidance Scale', minimum=0.1, maximum=30.0, value=7.0, step=0.1 ) seed = gr.Slider( label='Seed', minimum=-1, maximum=2147483647, step=1, randomize=True ) submit_btn = gr.Button( value="Submit", variant="primary" ) with gr.Column(min_width=300): output = gr.Image( label="Result", ) submit_btn.click( fn=infer, inputs=[ prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed ], outputs=output ) gr.Examples( examples=[ ["a painting of a village in the mountains", "monochrome", "./conditioning_images/conditioning_image_1.jpg"], ["three people walking in an alleyway with hats and pants", "monochrome", "./conditioning_images/conditioning_image_2.jpg"], ], inputs=[ prompt, negative_prompt, conditioning_image ], ) demo.launch()