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| import gradio as gr | |
| import torch | |
| from controlnet_aux import MLSDdetector | |
| from diffusers import ( | |
| ControlNetModel, | |
| StableDiffusionControlNetPipeline, | |
| UniPCMultistepScheduler, | |
| ) | |
| from PIL import Image | |
| stable_model_list = [ | |
| "runwayml/stable-diffusion-v1-5", | |
| ] | |
| stable_prompt_list = ["a photo of a man.", "a photo of a girl."] | |
| stable_negative_prompt_list = ["bad, ugly", "deformed"] | |
| data_list = [ | |
| "data/test.png", | |
| ] | |
| def controlnet_mlsd(image_path: str): | |
| mlsd = MLSDdetector.from_pretrained("lllyasviel/ControlNet") | |
| image = Image.open(image_path) | |
| image = mlsd(image) | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/sd-controlnet-mlsd", | |
| torch_dtype=torch.float16, | |
| ) | |
| return controlnet, image | |
| def stable_diffusion_controlnet_mlsd( | |
| image_path: str, | |
| model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| ): | |
| controlnet, image = controlnet_mlsd(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_mlsd_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| controlnet_mlsd_image_file = gr.Image( | |
| type="filepath", label="Image" | |
| ) | |
| controlnet_mlsd_model_id = gr.Dropdown( | |
| choices=stable_model_list, | |
| value=stable_model_list[0], | |
| label="Stable Model Id", | |
| ) | |
| controlnet_mlsd_prompt = gr.Textbox( | |
| lines=1, value=stable_prompt_list[0], label="Prompt" | |
| ) | |
| controlnet_mlsd_negative_prompt = gr.Textbox( | |
| lines=1, | |
| value=stable_negative_prompt_list[0], | |
| label="Negative Prompt", | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| controlnet_mlsd_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| controlnet_mlsd_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| controlnet_mlsd_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Output") | |
| gr.Examples( | |
| fn=stable_diffusion_controlnet_mlsd, | |
| examples=[ | |
| [ | |
| data_list[0], | |
| stable_model_list[0], | |
| stable_prompt_list[0], | |
| stable_negative_prompt_list[0], | |
| 7.5, | |
| 50, | |
| ] | |
| ], | |
| inputs=[ | |
| controlnet_mlsd_image_file, | |
| controlnet_mlsd_model_id, | |
| controlnet_mlsd_prompt, | |
| controlnet_mlsd_negative_prompt, | |
| controlnet_mlsd_guidance_scale, | |
| controlnet_mlsd_num_inference_step, | |
| ], | |
| outputs=[output_image], | |
| label="ControlNet-MLSD Example", | |
| cache_examples=False, | |
| ) | |
| controlnet_mlsd_predict.click( | |
| fn=stable_diffusion_controlnet_mlsd, | |
| inputs=[ | |
| controlnet_mlsd_image_file, | |
| controlnet_mlsd_model_id, | |
| controlnet_mlsd_prompt, | |
| controlnet_mlsd_negative_prompt, | |
| controlnet_mlsd_guidance_scale, | |
| controlnet_mlsd_num_inference_step, | |
| ], | |
| outputs=output_image, | |
| ) | |