from typing import Tuple, Union, List import numpy as np from PIL import Image import torch from diffusers.pipelines.controlnet import StableDiffusionControlNetInpaintPipeline from diffusers import ControlNetModel, UniPCMultistepScheduler, AutoPipelineForText2Image from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, AutoModelForDepthEstimation from .colors import ade_palette from .utils import map_colors_rgb from diffusers import StableDiffusionXLPipeline import gc import os from huggingface_hub import hf_hub_download rootfolder = "./ComfyUI/custom_nodes/StableDesign-for-ComfyUI" repo_id = "MykolaL/StableDesign" file_name = "diffusion_pytorch_model.safetensors" models = ["controlnet_depth", "own_controlnet"] # Função para verificar e baixar o arquivo, se necessário def check_and_download(model_name, rootfolder): model_path = os.path.join(rootfolder, model_name) file_path = os.path.join(model_path, file_name) if not os.path.exists(file_path): print(f"📥 Arquivo {file_name} não encontrado em {model_path}. Baixando do Hugging Face Space...") downloaded_file = hf_hub_download( repo_id=repo_id, filename=file_name, # Nome do arquivo subfolder=model_name, # Nome da subpasta dentro do Space local_dir=rootfolder, # Diretório onde salvar o arquivo repo_type="space" # Indica que está baixando de um Space ) print(f"✅ Download concluído: {downloaded_file}") else: print(f"✔️ Arquivo já existe: {file_path}") for model_name in models: # Garante que a pasta do modelo exista dentro do diretório raiz model_path = os.path.join(rootfolder, model_name) os.makedirs(model_path, exist_ok=True) # Verificar e baixar o arquivo para cada modelo check_and_download(model_name, rootfolder) device = "cuda" dtype = torch.float16 def filter_items( colors_list: Union[List, np.ndarray], items_list: Union[List, np.ndarray], items_to_remove: Union[List, np.ndarray] ) -> Tuple[Union[List, np.ndarray], Union[List, np.ndarray]]: """ Filters items and their corresponding colors from given lists, excluding specified items. Args: colors_list: A list or numpy array of colors corresponding to items. items_list: A list or numpy array of items. items_to_remove: A list or numpy array of items to be removed. Returns: A tuple of two lists or numpy arrays: filtered colors and filtered items. """ filtered_colors = [] filtered_items = [] for color, item in zip(colors_list, items_list): if item not in items_to_remove: filtered_colors.append(color) filtered_items.append(item) return filtered_colors, filtered_items def get_segmentation_pipeline( ) -> Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]: """Method to load the segmentation pipeline Returns: Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]: segmentation pipeline """ image_processor = AutoImageProcessor.from_pretrained( "openmmlab/upernet-convnext-small" ) image_segmentor = UperNetForSemanticSegmentation.from_pretrained( "openmmlab/upernet-convnext-small" ) return image_processor, image_segmentor def segment_image( image: Image, image_processor: AutoImageProcessor, image_segmentor: UperNetForSemanticSegmentation ) -> Image: """ Segments an image using a semantic segmentation model. Args: image (Image): The input image to be segmented. image_processor (AutoImageProcessor): The processor to prepare the image for segmentation. image_segmentor (UperNetForSemanticSegmentation): The semantic segmentation model used to identify different segments in the image. Returns: Image: The segmented image with each segment colored differently based on its identified class. """ # image_processor, image_segmentor = get_segmentation_pipeline() 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) seg_image = Image.fromarray(color_seg).convert('RGB') return seg_image def get_depth_pipeline(): feature_extractor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=dtype) depth_estimator = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=dtype) return feature_extractor, depth_estimator def get_depth_image( image: Image, feature_extractor: AutoImageProcessor, depth_estimator: AutoModelForDepthEstimation ) -> Image: image_to_depth = feature_extractor(images=image, return_tensors="pt").to(device) with torch.no_grad(): depth_map = depth_estimator(**image_to_depth).predicted_depth width, height = image.size depth_map = torch.nn.functional.interpolate( depth_map.unsqueeze(1).float(), size=(height, width), mode="bicubic", align_corners=False, ) depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) depth_map = (depth_map - depth_min) / (depth_max - depth_min) image = torch.cat([depth_map] * 3, dim=1) image = image.permute(0, 2, 3, 1).cpu().numpy()[0] image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) return image def resize_dimensions(dimensions, target_size): """ Resize PIL to target size while maintaining aspect ratio If smaller than target size leave it as is """ width, height = dimensions # Check if both dimensions are smaller than the target size if width < target_size and height < target_size: return dimensions # Determine the larger side if width > height: # Calculate the aspect ratio aspect_ratio = height / width # Resize dimensions return (target_size, int(target_size * aspect_ratio)) else: # Calculate the aspect ratio aspect_ratio = width / height # Resize dimensions return (int(target_size * aspect_ratio), target_size) def flush(): gc.collect() torch.cuda.empty_cache() class ControlNetDepthDesignModelMulti: """ Produces random noise images """ def __init__(self): """ Initialize your model(s) here """ #os.environ['HF_HUB_OFFLINE'] = "True" self.seed = 323*111 self.neg_prompt = "window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner" self.control_items = ["windowpane;window", "door;double;door"] self.additional_quality_suffix = "interior design, 4K, high resolution, photorealistic" def generate_design(self, empty_room_image: Image, prompt: str, guidance_scale: int = 10, num_steps: int = 50, strength: float =0.9, img_size: int = 640, img_dept_threshold=0.5, img_seg_threshold=0.5 ) -> Image: """ Given an image of an empty room and a prompt generate the designed room according to the prompt Inputs - empty_room_image - An RGB PIL Image of the empty room prompt - Text describing the target design elements of the room Returns - design_image - PIL Image of the same size as the empty room image If the size is not the same the submission will fail. """ print(prompt) flush() self.generator = torch.Generator(device=device).manual_seed(self.seed) pos_prompt = prompt + f', {self.additional_quality_suffix}' orig_w, orig_h = empty_room_image.size new_width, new_height = resize_dimensions(empty_room_image.size, img_size) input_image = empty_room_image.resize((new_width, new_height)) real_seg = np.array(segment_image(input_image, seg_image_processor, image_segmentor)) unique_colors = np.unique(real_seg.reshape(-1, real_seg.shape[2]), axis=0) unique_colors = [tuple(color) for color in unique_colors] segment_items = [map_colors_rgb(i) for i in unique_colors] chosen_colors, segment_items = filter_items( colors_list=unique_colors, items_list=segment_items, items_to_remove=self.control_items ) mask = np.zeros_like(real_seg) for color in chosen_colors: color_matches = (real_seg == color).all(axis=2) mask[color_matches] = 1 image_np = np.array(input_image) image = Image.fromarray(image_np).convert("RGB") mask_image = Image.fromarray((mask * 255).astype(np.uint8)).convert("RGB") segmentation_cond_image = Image.fromarray(real_seg).convert("RGB") image_depth = get_depth_image(image, depth_feature_extractor, depth_estimator) # generate image that would be used as IP-adapter flush() new_width_ip = int(new_width / 8) * 8 new_height_ip = int(new_height / 8) * 8 ip_image = guide_pipe(pos_prompt, num_inference_steps=num_steps, negative_prompt=self.neg_prompt, height=new_height_ip, width=new_width_ip, generator=[self.generator]).images[0] flush() generated_image = pipe( prompt=pos_prompt, negative_prompt=self.neg_prompt, num_inference_steps=num_steps, strength=strength, guidance_scale=guidance_scale, generator=[self.generator], image=image, mask_image=mask_image, ip_adapter_image=ip_image, control_image=[image_depth, segmentation_cond_image], controlnet_conditioning_scale=[img_dept_threshold, img_seg_threshold] ).images[0] flush() design_image = generated_image.resize( (orig_w, orig_h), Image.Resampling.LANCZOS ) return design_image controlnet_depth = ControlNetModel.from_pretrained( os.path.join(rootfolder,"controlnet_depth"), torch_dtype=dtype, use_safetensors=True ) controlnet_seg = ControlNetModel.from_pretrained( os.path.join(rootfolder,"own_controlnet"), torch_dtype=dtype, use_safetensors=True ) pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( "SG161222/Realistic_Vision_V5.1_noVAE", #"models/runwayml--stable-diffusion-inpainting", controlnet=[controlnet_depth, controlnet_seg], safety_checker=None, torch_dtype=dtype ) pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin") pipe.set_ip_adapter_scale(0.4) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to(device) guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=dtype, use_safetensors=True, variant="fp16") guide_pipe = guide_pipe.to(device) seg_image_processor, image_segmentor = get_segmentation_pipeline() depth_feature_extractor, depth_estimator = get_depth_pipeline() depth_estimator = depth_estimator.to(device)