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| from transformers import AutoImageProcessor, UperNetForSemanticSegmentation | |
| import torch | |
| from diffusers import (StableDiffusionControlNetPipeline, | |
| ControlNetModel, UniPCMultistepScheduler) | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| 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( | |
| "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=torch.float16 | |
| ) | |
| return controlnet, image | |
| def stable_diffusion_controlnet_img2img( | |
| stable_model_path:str, | |
| image_path:str, | |
| prompt:str, | |
| negative_prompt:str, | |
| num_samples:int, | |
| guidance_scale:int, | |
| num_inference_step:int, | |
| ): | |
| controlnet, image = controlnet_mlsd(image_path=image_path) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| pretrained_model_name_or_path=stable_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_images_per_prompt = num_samples, | |
| num_inference_steps = num_inference_step, | |
| guidance_scale = guidance_scale, | |
| ).images | |
| return output | |