Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from diffusers import ( | |
| ControlNetModel, | |
| StableDiffusionControlNetPipeline, | |
| UniPCMultistepScheduler, | |
| ) | |
| from PIL import Image | |
| from transformers import AutoImageProcessor, UperNetForSemanticSegmentation | |
| 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 ade_palette(): | |
| """ADE20K palette that maps each class to RGB values.""" | |
| return [ | |
| [120, 120, 120], | |
| [180, 120, 120], | |
| [6, 230, 230], | |
| [80, 50, 50], | |
| [4, 200, 3], | |
| [120, 120, 80], | |
| [140, 140, 140], | |
| [204, 5, 255], | |
| [230, 230, 230], | |
| [4, 250, 7], | |
| [224, 5, 255], | |
| [235, 255, 7], | |
| [150, 5, 61], | |
| [120, 120, 70], | |
| [8, 255, 51], | |
| [255, 6, 82], | |
| [143, 255, 140], | |
| [204, 255, 4], | |
| [255, 51, 7], | |
| [204, 70, 3], | |
| [0, 102, 200], | |
| [61, 230, 250], | |
| [255, 6, 51], | |
| [11, 102, 255], | |
| [255, 7, 71], | |
| [255, 9, 224], | |
| [9, 7, 230], | |
| [220, 220, 220], | |
| [255, 9, 92], | |
| [112, 9, 255], | |
| [8, 255, 214], | |
| [7, 255, 224], | |
| [255, 184, 6], | |
| [10, 255, 71], | |
| [255, 41, 10], | |
| [7, 255, 255], | |
| [224, 255, 8], | |
| [102, 8, 255], | |
| [255, 61, 6], | |
| [255, 194, 7], | |
| [255, 122, 8], | |
| [0, 255, 20], | |
| [255, 8, 41], | |
| [255, 5, 153], | |
| [6, 51, 255], | |
| [235, 12, 255], | |
| [160, 150, 20], | |
| [0, 163, 255], | |
| [140, 140, 140], | |
| [250, 10, 15], | |
| [20, 255, 0], | |
| [31, 255, 0], | |
| [255, 31, 0], | |
| [255, 224, 0], | |
| [153, 255, 0], | |
| [0, 0, 255], | |
| [255, 71, 0], | |
| [0, 235, 255], | |
| [0, 173, 255], | |
| [31, 0, 255], | |
| [11, 200, 200], | |
| [255, 82, 0], | |
| [0, 255, 245], | |
| [0, 61, 255], | |
| [0, 255, 112], | |
| [0, 255, 133], | |
| [255, 0, 0], | |
| [255, 163, 0], | |
| [255, 102, 0], | |
| [194, 255, 0], | |
| [0, 143, 255], | |
| [51, 255, 0], | |
| [0, 82, 255], | |
| [0, 255, 41], | |
| [0, 255, 173], | |
| [10, 0, 255], | |
| [173, 255, 0], | |
| [0, 255, 153], | |
| [255, 92, 0], | |
| [255, 0, 255], | |
| [255, 0, 245], | |
| [255, 0, 102], | |
| [255, 173, 0], | |
| [255, 0, 20], | |
| [255, 184, 184], | |
| [0, 31, 255], | |
| [0, 255, 61], | |
| [0, 71, 255], | |
| [255, 0, 204], | |
| [0, 255, 194], | |
| [0, 255, 82], | |
| [0, 10, 255], | |
| [0, 112, 255], | |
| [51, 0, 255], | |
| [0, 194, 255], | |
| [0, 122, 255], | |
| [0, 255, 163], | |
| [255, 153, 0], | |
| [0, 255, 10], | |
| [255, 112, 0], | |
| [143, 255, 0], | |
| [82, 0, 255], | |
| [163, 255, 0], | |
| [255, 235, 0], | |
| [8, 184, 170], | |
| [133, 0, 255], | |
| [0, 255, 92], | |
| [184, 0, 255], | |
| [255, 0, 31], | |
| [0, 184, 255], | |
| [0, 214, 255], | |
| [255, 0, 112], | |
| [92, 255, 0], | |
| [0, 224, 255], | |
| [112, 224, 255], | |
| [70, 184, 160], | |
| [163, 0, 255], | |
| [153, 0, 255], | |
| [71, 255, 0], | |
| [255, 0, 163], | |
| [255, 204, 0], | |
| [255, 0, 143], | |
| [0, 255, 235], | |
| [133, 255, 0], | |
| [255, 0, 235], | |
| [245, 0, 255], | |
| [255, 0, 122], | |
| [255, 245, 0], | |
| [10, 190, 212], | |
| [214, 255, 0], | |
| [0, 204, 255], | |
| [20, 0, 255], | |
| [255, 255, 0], | |
| [0, 153, 255], | |
| [0, 41, 255], | |
| [0, 255, 204], | |
| [41, 0, 255], | |
| [41, 255, 0], | |
| [173, 0, 255], | |
| [0, 245, 255], | |
| [71, 0, 255], | |
| [122, 0, 255], | |
| [0, 255, 184], | |
| [0, 92, 255], | |
| [184, 255, 0], | |
| [0, 133, 255], | |
| [255, 214, 0], | |
| [25, 194, 194], | |
| [102, 255, 0], | |
| [92, 0, 255], | |
| ] | |
| def controlnet_mlsd(image_path: str): | |
| image_processor = AutoImageProcessor.from_pretrained( | |
| "openmmlab/upernet-convnext-small" | |
| ) | |
| image_segmentor = UperNetForSemanticSegmentation.from_pretrained( | |
| "openmmlab/upernet-convnext-small" | |
| ) | |
| image = Image.open(image_path).convert("RGB") | |
| pixel_values = image_processor(image, return_tensors="pt").pixel_values | |
| with torch.no_grad(): | |
| outputs = image_segmentor(pixel_values) | |
| seg = image_processor.post_process_semantic_segmentation( | |
| outputs, target_sizes=[image.size[::-1]] | |
| )[0] | |
| color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) | |
| palette = np.array(ade_palette()) | |
| for label, color in enumerate(palette): | |
| color_seg[seg == label, :] = color | |
| color_seg = color_seg.astype(np.uint8) | |
| image = Image.fromarray(color_seg) | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16 | |
| ) | |
| return controlnet, image | |
| def stable_diffusion_controlnet_seg( | |
| 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_seg_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| controlnet_seg_image_file = gr.Image( | |
| type="filepath", label="Image" | |
| ) | |
| controlnet_seg_model_id = gr.Dropdown( | |
| choices=stable_model_list, | |
| value=stable_model_list[0], | |
| label="Stable Model Id", | |
| ) | |
| controlnet_seg_prompt = gr.Textbox( | |
| lines=1, value=stable_prompt_list[0], label="Prompt" | |
| ) | |
| controlnet_seg_negative_prompt = gr.Textbox( | |
| lines=1, | |
| value=stable_negative_prompt_list[0], | |
| label="Negative Prompt", | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| controlnet_seg_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| controlnet_seg_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| controlnet_seg_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Output") | |
| gr.Examples( | |
| fn=stable_diffusion_controlnet_seg, | |
| examples=[ | |
| [ | |
| data_list[0], | |
| stable_model_list[0], | |
| stable_prompt_list[0], | |
| stable_negative_prompt_list[0], | |
| 7.5, | |
| 50, | |
| ], | |
| ], | |
| inputs=[ | |
| controlnet_seg_image_file, | |
| controlnet_seg_model_id, | |
| controlnet_seg_prompt, | |
| controlnet_seg_negative_prompt, | |
| controlnet_seg_guidance_scale, | |
| controlnet_seg_num_inference_step, | |
| ], | |
| outputs=[output_image], | |
| cache_examples=False, | |
| label="ControlNet Segmentation Example", | |
| ) | |
| controlnet_seg_predict.click( | |
| fn=stable_diffusion_controlnet_seg, | |
| inputs=[ | |
| controlnet_seg_image_file, | |
| controlnet_seg_model_id, | |
| controlnet_seg_prompt, | |
| controlnet_seg_negative_prompt, | |
| controlnet_seg_guidance_scale, | |
| controlnet_seg_num_inference_step, | |
| ], | |
| outputs=[output_image], | |
| ) | |