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| import space | |
| import torch | |
| import numpy as np | |
| from transformers import AutoImageProcessor, AutoModelForDepthEstimation | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler | |
| import natten | |
| import gradio | |
| from PIL import Image | |
| """ | |
| IMPORT MODEL | |
| """ | |
| #model generate depth image | |
| depth_image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16) | |
| depth_model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16) | |
| depth_model = depth_model.cuda() | |
| #model generate segment image | |
| from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation | |
| processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large") | |
| model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large") | |
| model = model.cuda() | |
| #model generate image | |
| #load depth controlnet, segmentation controlnet | |
| controlnets = [ | |
| ControlNetModel.from_pretrained("Lam-Hung/controlnet_depth_interior", torch_dtype=torch.float16, use_safetensors=True), | |
| ControlNetModel.from_pretrained("Lam-Hung/controlnet_segment_interior", torch_dtype=torch.float16, use_safetensors=True) | |
| ] | |
| #load stable diffusion 1.5 and controlnets | |
| pipeline = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet= controlnets, torch_dtype=torch.float16, use_safetensors=True | |
| ) | |
| # take UniPCMultistepScheduler for faster inference | |
| pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config) | |
| pipeline.load_lora_weights('Lam-Hung/controlnet_lora_interior', weight_name= "pytorch_lora_weights.safetensors", adapter_name="interior") | |
| pipeline.to("cuda") | |
| """ | |
| IMPORT FUNCTION | |
| """ | |
| def ade_palette() -> list[list[int]]: | |
| """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 get_depth_image(image: Image) -> Image: | |
| """ | |
| create depth image | |
| """ | |
| image_to_depth = depth_image_processor(images=image, return_tensors="pt").to("cuda") | |
| with torch.no_grad(): | |
| depth_map = depth_model(**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 get_segmentation_of_room(image: Image): | |
| #-> tuple[np.ndarray, Image]: | |
| """ | |
| create instance segmentation image | |
| """ | |
| # Semantic Segmentation | |
| with torch.inference_mode(): | |
| semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") | |
| semantic_inputs = {key: value.to("cuda") for key, value in semantic_inputs.items()} | |
| semantic_outputs = model(**semantic_inputs) | |
| # pass through image_processor for postprocessing | |
| predicted_semantic_map = \ | |
| processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0] | |
| predicted_semantic_map = predicted_semantic_map.cpu() | |
| color_seg = np.zeros((predicted_semantic_map.shape[0], predicted_semantic_map.shape[1], 3), dtype=np.uint8) | |
| palette = np.array(ade_palette()) | |
| for label, color in enumerate(palette): | |
| color_seg[predicted_semantic_map == label, :] = color | |
| color_seg = color_seg.astype(np.uint8) | |
| seg_image = Image.fromarray(color_seg).convert('RGB') | |
| return seg_image | |
| def interior_inference(image, | |
| prompt, | |
| negative_prompt="window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner", | |
| num_inference_steps=25, | |
| depth_weight=0.9, | |
| segment_weight=0.9, | |
| lora_weight=0.7, | |
| seed= 123): | |
| depth_image = get_depth_image(image) | |
| segmentation_image = get_segmentation_of_room(image) | |
| prompt = prompt + " interior design, 4K, high resolution, photorealistic" | |
| image_interior = pipeline( | |
| prompt, | |
| negative_prompt = negative_prompt, | |
| image = [depth_image, segmentation_image], | |
| num_inference_steps = num_inference_steps, | |
| generator = torch.manual_seed(seed), | |
| #lora_scale if enable_lora | |
| cross_attention_kwargs={"scale": lora_weight}, | |
| controlnet_conditioning_scale=[depth_weight, segment_weight], | |
| ).images[0] | |
| return image_interior | |
| interface = gr.Interface( | |
| fn = interior_inference, | |
| inputs = [ | |
| gr.Image(type = "pil", label = "Empty room image", show_label = True), | |
| gr.Textbox(label = "Prompt", lines = 3, placeholder = "Enter your prompt here"), | |
| ], | |
| outputs=[ | |
| gr.Image(type = "pil", label = "Interior design", show_label = True), | |
| ], | |
| additional_inputs=[ | |
| gr.Textbox(label = "Negative prompt", lines = 3, placeholder = "Enter your negative prompt here"), | |
| gr.Slider(label = "Number of inference steps", minimum = 1, maximum = 100, value = 25, step = 1), | |
| gr.Slider(label = "Depth weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1), | |
| gr.Slider(label = "Segment weight", minimum = 0, maximum = 1, value = 0.9, step = 0.1), | |
| gr.Slider(label = "Lora weight", minimum = 0, maximum = 1, value = 0.7, step = 0.1), | |
| gr.Number(label = "Seed", value = 123), | |
| ], | |
| title="INTERIOR DESIGN", | |
| description="**We will design your empty room become the beautiful room", | |
| ) | |
| if "__name__" =="__main__": | |
| interface.launch() |