from diffusers import ( StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler) from controlnet_aux import HEDdetector from PIL import Image import gradio as gr import torch stable_model_list = [ "runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-2", "stabilityai/stable-diffusion-2-base", "stabilityai/stable-diffusion-2-1", "stabilityai/stable-diffusion-2-1-base" ] stable_prompt_list = [ "a photo of a man.", "a photo of a girl." ] stable_negative_prompt_list = [ "bad, ugly", "deformed" ] def controlnet_hed(image_path:str): hed = HEDdetector.from_pretrained('lllyasviel/ControlNet') image = Image.open(image_path) image = hed(image) controlnet = ControlNetModel.from_pretrained( "fusing/stable-diffusion-v1-5-controlnet-hed", torch_dtype=torch.float16 ) return controlnet, image def stable_diffusion_controlnet_hed( image_path:str, model_path:str, prompt:str, negative_prompt:str, guidance_scale:int, num_inference_step:int, ): controlnet, image = controlnet_hed(image_path=image_path) pipe = StableDiffusionControlNetPipeline.from_pretrained( pretrained_model_name_or_path=model_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.to("cuda") pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) pipe.enable_xformers_memory_efficient_attention() output = pipe( prompt = prompt, image = image, negative_prompt = negative_prompt, num_inference_steps = num_inference_step, guidance_scale = guidance_scale, ).images return output[0] def stable_diffusion_controlnet_hed_app(): with gr.Blocks(): with gr.Row(): with gr.Column(): controlnet_hed_image_file = gr.Image( type='filepath', label='Image' ) controlnet_hed_model_id = gr.Dropdown( choices=stable_model_list, value=stable_model_list[0], label='Stable Model Id' ) controlnet_hed_prompt = gr.Textbox( lines=1, value=stable_prompt_list[0], label='Prompt' ) controlnet_hed_negative_prompt = gr.Textbox( lines=1, value=stable_negative_prompt_list[0], label='Negative Prompt' ) with gr.Accordion("Advanced Options", open=False): controlnet_hed_guidance_scale = gr.Slider( minimum=0.1, maximum=15, step=0.1, value=7.5, label='Guidance Scale' ) controlnet_hed_num_inference_step = gr.Slider( minimum=1, maximum=100, step=1, value=50, label='Num Inference Step' ) controlnet_hed_predict = gr.Button(value='Generator') with gr.Column(): output_image = gr.Image(label='Output') controlnet_hed_predict.click( fn=stable_diffusion_controlnet_hed, inputs=[ controlnet_hed_image_file, controlnet_hed_model_id, controlnet_hed_prompt, controlnet_hed_negative_prompt, controlnet_hed_guidance_scale, controlnet_hed_num_inference_step, ], outputs=[output_image] )