import gradio as gr import numpy as np import torch from diffusers import ControlNetModel, StableDiffusionControlNetPipeline from PIL import Image from transformers import AutoImageProcessor, UperNetForSemanticSegmentation from diffusion_webui.utils.model_list import stable_model_list from diffusion_webui.utils.scheduler_list import ( SCHEDULER_LIST, get_scheduler_list, ) 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], ] class StableDiffusionControlNetSegGenerator: def __init__(self): self.pipe = None def load_model( self, stable_model_path, scheduler, ): if self.pipe is None: controlnet = ControlNetModel.from_pretrained( "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16 ) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( pretrained_model_name_or_path=stable_model_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16, ) self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler) self.pipe.to("cuda") self.pipe.enable_xformers_memory_efficient_attention() return self.pipe def controlnet_seg(self, 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) return image def generate_image( self, image_path: str, model_path: str, prompt: str, negative_prompt: str, num_images_per_prompt: int, guidance_scale: int, num_inference_step: int, scheduler: str, seed_generator: int, ): image = self.controlnet_seg(image_path=image_path) pipe = self.load_model( stable_model_path=model_path, scheduler=scheduler, ) if seed_generator == 0: random_seed = torch.randint(0, 1000000, (1,)) generator = torch.manual_seed(random_seed) else: generator = torch.manual_seed(seed_generator) output = pipe( prompt=prompt, image=image, negative_prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, num_inference_steps=num_inference_step, guidance_scale=guidance_scale, generator=generator, ).images return output def app(): with gr.Blocks(): with gr.Row(): with gr.Column(): controlnet_seg_image_file = gr.Image( type="filepath", label="Image" ) controlnet_seg_prompt = gr.Textbox( lines=1, show_label=False, placeholder="Prompt", ) controlnet_seg_negative_prompt = gr.Textbox( lines=1, show_label=False, placeholder="Negative Prompt", ) with gr.Row(): with gr.Column(): controlnet_seg_model_id = gr.Dropdown( choices=stable_model_list, value=stable_model_list[0], label="Stable Model Id", ) 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", ) with gr.Row(): with gr.Column(): controlnet_seg_scheduler = gr.Dropdown( choices=SCHEDULER_LIST, value=SCHEDULER_LIST[0], label="Scheduler", ) controlnet_seg_num_images_per_prompt = ( gr.Slider( minimum=1, maximum=10, step=1, value=1, label="Number Of Images", ) ) controlnet_seg_seed_generator = gr.Slider( minimum=0, maximum=1000000, step=1, value=0, label="Seed Generator", ) controlnet_seg_predict = gr.Button(value="Generator") with gr.Column(): output_image = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery", ).style(grid=(1, 2)) controlnet_seg_predict.click( fn=StableDiffusionControlNetSegGenerator().generate_image, inputs=[ controlnet_seg_image_file, controlnet_seg_model_id, controlnet_seg_prompt, controlnet_seg_negative_prompt, controlnet_seg_num_images_per_prompt, controlnet_seg_guidance_scale, controlnet_seg_num_inference_step, controlnet_seg_scheduler, controlnet_seg_seed_generator, ], outputs=[output_image], )