import io import requests import numpy as np import torch import os from PIL import Image from typing import List, Optional from functools import reduce from argparse import ArgumentParser import gradio as gr from transformers import DetrFeatureExtractor, DetrForSegmentation, DetrConfig from transformers.models.detr.feature_extraction_detr import rgb_to_id from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler parser = ArgumentParser() parser.add_argument('--disable-cuda', action='store_true') parser.add_argument('--attention-slicing', action='store_true') args = parser.parse_args() auth_token = os.environ.get("READ_TOKEN") try_cuda = not args.disable_cuda torch.inference_mode() torch.no_grad() # Device helper def get_device(try_cuda=True): return torch.device('cuda' if try_cuda and torch.cuda.is_available() else 'cpu') device = get_device(try_cuda=try_cuda) # Load segmentation models def load_segmentation_models(model_name: str = 'facebook/detr-resnet-50-panoptic'): feature_extractor = DetrFeatureExtractor.from_pretrained(model_name) model = DetrForSegmentation.from_pretrained(model_name) cfg = DetrConfig.from_pretrained(model_name) return feature_extractor, model, cfg # Load diffusion pipeline def load_diffusion_pipeline(model_name: str = 'stabilityai/stable-diffusion-2-inpainting'): return StableDiffusionInpaintPipeline.from_pretrained( model_name, revision='fp16', torch_dtype=torch.float16 if try_cuda and torch.cuda.is_available() else torch.float32, use_auth_token=auth_token ) def min_pool(x: torch.Tensor, kernel_size: int): pad_size = (kernel_size - 1) // 2 return -torch.nn.functional.max_pool2d(-x, kernel_size, (1, 1), padding=pad_size) def max_pool(x: torch.Tensor, kernel_size: int): pad_size = (kernel_size - 1) // 2 return torch.nn.functional.max_pool2d(x, kernel_size, (1, 1), padding=pad_size) # Apply min-max pooling to clean up mask def clean_mask(mask, max_kernel: int = 23, min_kernel: int = 5): mask = torch.Tensor(mask[None, None]).float().to(device) mask = min_pool(mask, min_kernel) mask = max_pool(mask, max_kernel) mask = mask.bool().squeeze().cpu().numpy() return mask feature_extractor, segmentation_model, segmentation_cfg = load_segmentation_models() pipe = load_diffusion_pipeline() pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) segmentation_model = segmentation_model.to(device) pipe = pipe.to(device) if args.attention_slicing: pipe.enable_attention_slicing() # Callback function that runs segmentation and updates CheckboxGroup def fn_segmentation(image, max_kernel, min_kernel): inputs = feature_extractor(images=image, return_tensors="pt").to(device) outputs = segmentation_model(**inputs) processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0) result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0] panoptic_seg = Image.open(io.BytesIO(result["png_string"])).resize((image.width, image.height)) panoptic_seg = np.array(panoptic_seg, dtype=np.uint8) panoptic_seg_id = rgb_to_id(panoptic_seg) raw_masks = [] for s in result['segments_info']: m = panoptic_seg_id == s['id'] raw_masks.append(m.astype(np.uint8) * 255) checkbox_choices = [f"{s['id']}:{segmentation_cfg.id2label[s['category_id']]}" for s in result['segments_info']] checkbox_group = gr.CheckboxGroup.update( choices=checkbox_choices ) return raw_masks, checkbox_group, gr.Image.update(value=np.zeros((image.height, image.width))), gr.Image.update(value=image) # Callback function that updates the displayed mask based on selected checkboxes def fn_update_mask( image: Image, masks: List[np.array], masks_enabled: List[int], max_kernel: int, min_kernel: int, invert_mask: bool ): masks_enabled = [int(m.split(':')[0]) for m in masks_enabled] combined_mask = reduce(lambda x, y: x | y, [masks[i] for i in masks_enabled], np.zeros_like(masks[0], dtype=bool)) if invert_mask: combined_mask = ~combined_mask combined_mask = clean_mask(combined_mask, max_kernel, min_kernel) masked_image = np.array(image).copy() masked_image[combined_mask] = 0.0 return combined_mask.astype(np.uint8) * 255, Image.fromarray(masked_image) # Callback function that runs diffusion given the current image, mask and prompt. def fn_diffusion( prompt: str, masked_image: Image, mask: Image, num_diffusion_steps: int, guidance_scale: float, negative_prompt: Optional[str] = None, ): if len(negative_prompt) == 0: negative_prompt = None # Resize image to a more stable diffusion friendly format. # TODO: remove magic number STABLE_DIFFUSION_SMALL_EDGE = 512 w, h = masked_image.size is_width_larger = w > h resize_ratio = STABLE_DIFFUSION_SMALL_EDGE / (h if is_width_larger else w) new_width = int(w * resize_ratio) if is_width_larger else STABLE_DIFFUSION_SMALL_EDGE new_height = STABLE_DIFFUSION_SMALL_EDGE if is_width_larger else int(h * resize_ratio) new_width += 8 - (new_width % 8) if is_width_larger else 0 new_height += 0 if is_width_larger else 8 - (new_height % 8) mask = Image.fromarray(mask).convert("RGB").resize((new_width, new_height)) masked_image = masked_image.convert("RGB").resize((new_width, new_height)) # Run diffusion inpainted_image = pipe( height=new_height, width=new_width, prompt=prompt, image=masked_image, mask_image=mask, num_inference_steps=num_diffusion_steps, guidance_scale=guidance_scale, negative_prompt=negative_prompt ).images[0] # Resize back to the original size inpainted_image = inpainted_image.resize((w, h)) return inpainted_image demo = gr.Blocks(css=open('app.css').read()) with demo: # Input image control input_image = gr.Image(type='pil', label="Input Image") # Combined mask controls bt_masks = gr.Button("Compute Masks") with gr.Row(): masked_image = gr.Image(type='pil', label="Masked Image") mask_storage = gr.State() # Mask editing controls with gr.Row(): max_slider = gr.Slider(minimum=1, maximum=99, value=23, step=2, label="Mask Overflow") min_slider = gr.Slider(minimum=1, maximum=99, value=5, step=2, label="Mask Denoising") with gr.Row(style="align-contents:left;"): invert_mask = gr.Checkbox(label="Invert Mask") with gr.Row(): mask_checkboxes = gr.CheckboxGroup(interactive=True, label="Mask Selection") # Diffusion controls and output with gr.Row(): with gr.Column(): prompt = gr.Textbox("An angry dog floating in outer deep space. Twinkling stars in the background. High definition.", label="Prompt") negative_prompt = gr.Textbox(label="Negative Prompt") with gr.Column(): steps_slider = gr.Slider(minimum=1, maximum=100, value=50, label="Inference Steps") guidance_slider = gr.Slider(minimum=0.0, maximum=50.0, value=7.5, step=0.1, label="Guidance Scale") bt_diffusion = gr.Button("Run Diffusion") mask_image = gr.Image(type='numpy', label="Diffusion Mask") inpainted_image = gr.Image(type='pil', label="Inpainted Image") # TODO: saw a better way of handling many inputs online.. # forgot where though update_mask_inputs = [input_image, mask_storage, mask_checkboxes, max_slider, min_slider, invert_mask] update_mask_outputs = [mask_image, masked_image] # Clear checkbox group on input image change input_image.change(lambda: gr.CheckboxGroup.update(choices=[], value=[]), outputs=mask_checkboxes) input_image.change(lambda: gr.Checkbox.update(value=False), outputs=invert_mask) # Segmentation button callback bt_masks.click(fn_segmentation, inputs=[input_image, max_slider, min_slider], outputs=[mask_storage, mask_checkboxes, mask_image, masked_image]) # Update mask callbacks max_slider.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False) min_slider.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False) mask_checkboxes.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False) invert_mask.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False) # Diffusion button callback bt_diffusion.click(fn_diffusion, inputs=[ prompt, masked_image, mask_image, steps_slider, guidance_slider, negative_prompt ], outputs=inpainted_image) gr.HTML(open('app_license.html').read()) demo.launch()