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| # Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2 | |
| from torchvision.utils import save_image | |
| from PIL import Image | |
| from pytorch_lightning import seed_everything | |
| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import random | |
| import requests | |
| from io import BytesIO | |
| from annotator.util import resize_image, HWC3 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| use_blip = False | |
| use_gradio = False | |
| # Diffusion init using diffusers. | |
| import groundingdino.datasets.transforms as T | |
| from groundingdino.models import build_model | |
| from groundingdino.util import box_ops | |
| from groundingdino.util.slconfig import SLConfig | |
| from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap | |
| from groundingdino.util.inference import annotate, load_image, predict | |
| from segment_anything import build_sam, SamPredictor | |
| from segment_anything.utils.amg import remove_small_regions | |
| # diffusers==0.14.0 required. | |
| from diffusers import ControlNetModel, UniPCMultistepScheduler | |
| from utils.stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline | |
| import torch | |
| base_model_path = "stabilityai/stable-diffusion-2-inpainting" | |
| controlnet_path = "shgao/edit-anything-v0-1-1" | |
| controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) | |
| pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( | |
| base_model_path, controlnet=controlnet, torch_dtype=torch.float16 | |
| ) | |
| # speed up diffusion process with faster scheduler and memory optimization | |
| pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| # remove following line if xformers is not installed | |
| pipe.enable_xformers_memory_efficient_attention() | |
| # pipe.enable_model_cpu_offload() # disable for now because of unknow bug in accelerate | |
| pipe.to(device) | |
| # Segment-Anything init. | |
| # pip install git+https://github.com/facebookresearch/segment-anything.git | |
| from segment_anything import sam_model_registry, SamAutomaticMaskGenerator | |
| sam_checkpoint = "./models/sam_vit_h_4b8939.pth" | |
| groundingdino_checkpoint = "./models/groundingdino_swint_ogc.pth" | |
| groundingdino_config_file = "./GroundingDINO_SwinT_OGC.py" | |
| model_type = "default" | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) | |
| sam.to(device=device) | |
| mask_generator = SamAutomaticMaskGenerator(sam) | |
| def load_groundingdino_model(model_config_path, model_checkpoint_path): | |
| args = SLConfig.fromfile(model_config_path) | |
| args.device = device | |
| model = build_model(args) | |
| checkpoint = torch.load(model_checkpoint_path, map_location="cpu") | |
| load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) | |
| print(load_res) | |
| _ = model.eval() | |
| return model | |
| grounding_model = load_groundingdino_model(groundingdino_config_file, groundingdino_checkpoint).to(device) | |
| sam_predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device=device)) | |
| # BLIP2 init. | |
| if use_blip: | |
| # need the latest transformers | |
| # pip install git+https://github.com/huggingface/transformers.git | |
| from transformers import AutoProcessor, Blip2ForConditionalGeneration | |
| processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| blip_model = Blip2ForConditionalGeneration.from_pretrained( | |
| "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) | |
| blip_model.to(device) | |
| blip_model.to(device) | |
| def get_blip2_text(image): | |
| inputs = processor(image, return_tensors="pt").to(device, torch.float16) | |
| generated_ids = blip_model.generate(**inputs, max_new_tokens=50) | |
| generated_text = processor.batch_decode( | |
| generated_ids, skip_special_tokens=True)[0].strip() | |
| return generated_text | |
| def show_anns(anns): | |
| if len(anns) == 0: | |
| return | |
| sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) | |
| full_img = None | |
| # for ann in sorted_anns: | |
| for i in range(len(sorted_anns)): | |
| ann = anns[i] | |
| m = ann['segmentation'] | |
| if full_img is None: | |
| full_img = np.zeros((m.shape[0], m.shape[1], 3)) | |
| map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16) | |
| map[m != 0] = i + 1 | |
| color_mask = np.random.random((1, 3)).tolist()[0] | |
| full_img[m != 0] = color_mask | |
| full_img = full_img * 255 | |
| # anno encoding from https://github.com/LUSSeg/ImageNet-S | |
| res = np.zeros((map.shape[0], map.shape[1], 3)) | |
| res[:, :, 0] = map % 256 | |
| res[:, :, 1] = map // 256 | |
| res.astype(np.float32) | |
| return full_img, res | |
| def get_sam_control(image): | |
| masks = mask_generator.generate(image) | |
| full_img, res = show_anns(masks) | |
| return full_img, res | |
| def prompt2mask(original_image, caption, box_threshold=0.25, text_threshold=0.25, num_boxes=2): | |
| def image_transform_grounding(init_image): | |
| transform = T.Compose([ | |
| T.RandomResize([800], max_size=1333), | |
| T.ToTensor(), | |
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| image, _ = transform(init_image, None) # 3, h, w | |
| return init_image, image | |
| image_np = np.array(original_image, dtype=np.uint8) | |
| caption = caption.lower() | |
| caption = caption.strip() | |
| if not caption.endswith("."): | |
| caption = caption + "." | |
| _, image_tensor = image_transform_grounding(original_image) | |
| boxes, logits, phrases = predict(grounding_model, | |
| image_tensor, caption, box_threshold, text_threshold, device='cpu') | |
| print(logits) | |
| print('number of boxes: ', boxes.size(0)) | |
| # exit(0) | |
| # from PIL import Image, ImageDraw, ImageFont | |
| H, W = original_image.size[1], original_image.size[0] | |
| boxes = boxes * torch.Tensor([W, H, W, H]) | |
| boxes[:, :2] = boxes[:, :2] - boxes[:, 2:] / 2 | |
| boxes[:, 2:] = boxes[:, 2:] + boxes[:, :2] | |
| # draw = ImageDraw.Draw(original_image) | |
| # for box in boxes: | |
| # # from 0..1 to 0..W, 0..H | |
| # # box = box * torch.Tensor([W, H, W, H]) | |
| # # # from xywh to xyxy | |
| # # box[:2] -= box[2:] / 2 | |
| # # box[2:] += box[:2] | |
| # # random color | |
| # color = tuple(np.random.randint(0, 255, size=3).tolist()) | |
| # # draw | |
| # x0, y0, x1, y1 = box | |
| # x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) | |
| # | |
| # draw.rectangle([x0, y0, x1, y1], outline=color, width=6) | |
| # original_image.save('debug.jpg') | |
| # exit(0) | |
| final_m = torch.zeros((image_np.shape[0], image_np.shape[1])) | |
| if boxes.size(0) > 0: | |
| sam_predictor.set_image(image_np) | |
| transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image_np.shape[:2]) | |
| masks, _, _ = sam_predictor.predict_torch( | |
| point_coords=None, | |
| point_labels=None, | |
| boxes=transformed_boxes.to(device), | |
| multimask_output=False, | |
| ) | |
| # remove small disconnected regions and holes | |
| fine_masks = [] | |
| for mask in masks.to('cpu').numpy(): # masks: [num_masks, 1, h, w] | |
| fine_masks.append(remove_small_regions(mask[0], 400, mode="holes")[0]) | |
| masks = np.stack(fine_masks, axis=0)[:, np.newaxis] | |
| masks = torch.from_numpy(masks) | |
| num_obj = min(len(logits), num_boxes) | |
| for obj_ind in range(num_obj): | |
| # box = boxes[obj_ind] | |
| m = masks[obj_ind][0] | |
| final_m += m | |
| final_m = (final_m > 0).to('cpu').numpy() | |
| # print(final_m.max(), final_m.min()) | |
| return np.dstack((final_m, final_m, final_m)) * 255 | |
| def process(input_image, mask_prompt, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, | |
| ddim_steps, guess_mode, strength, scale, seed, eta): | |
| with torch.no_grad(): | |
| mask_image = np.array(prompt2mask(input_image, mask_prompt), dtype=np.uint8) | |
| input_image = np.array(input_image, dtype=np.uint8)[:, :, :3] | |
| if use_blip: | |
| print("Generating text:") | |
| blip2_prompt = get_blip2_text(input_image) | |
| print("Generated text:", blip2_prompt) | |
| if len(prompt) > 0: | |
| prompt = blip2_prompt + ',' + prompt | |
| else: | |
| prompt = blip2_prompt | |
| print("All text:", prompt) | |
| input_image = HWC3(input_image) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| print("Generating SAM seg:") | |
| # the default SAM model is trained with 1024 size. | |
| full_segmask, detected_map = get_sam_control( | |
| resize_image(input_image, detect_resolution)) | |
| detected_map = HWC3(detected_map.astype(np.uint8)) | |
| detected_map = cv2.resize( | |
| detected_map, (W, H), interpolation=cv2.INTER_LINEAR) | |
| control = torch.from_numpy( | |
| detected_map.copy()).float().cuda() | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| control = einops.rearrange(control, 'b h w c -> b c h w').clone() | |
| mask_image = HWC3(mask_image.astype(np.uint8)) | |
| mask_image = cv2.resize( | |
| mask_image, (W, H), interpolation=cv2.INTER_LINEAR) | |
| mask_image = Image.fromarray(mask_image) | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| seed_everything(seed) | |
| generator = torch.manual_seed(seed) | |
| x_samples = pipe( | |
| image=img, | |
| mask_image=mask_image, | |
| prompt=[prompt + ', ' + a_prompt] * num_samples, | |
| negative_prompt=[n_prompt] * num_samples, | |
| num_images_per_prompt=num_samples, | |
| num_inference_steps=ddim_steps, | |
| generator=generator, | |
| controlnet_conditioning_image=control.type(torch.float16), | |
| height=H, | |
| width=W, | |
| ).images | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return [full_segmask, mask_image] + results | |
| def download_image(url): | |
| response = requests.get(url) | |
| return Image.open(BytesIO(response.content)).convert("RGB") | |
| # disable gradio when not using GUI. | |
| if not use_gradio: | |
| image_path = "assets/dog.png" | |
| input_image_pil = Image.open(image_path).convert('RGB') | |
| input_image = np.array(input_image_pil, dtype=np.uint8)[:, :, :3] | |
| mask_prompt = 'bench.' | |
| # mask_image = np.array(prompt2mask(input_image, mask_prompt), dtype=np.uint8) | |
| prompt = "cat" | |
| a_prompt = 'best quality, extremely detailed' | |
| n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' | |
| num_samples = 3 | |
| image_resolution = 512 | |
| detect_resolution = 512 | |
| ddim_steps = 30 | |
| guess_mode = False | |
| strength = 1.0 | |
| scale = 9.0 | |
| seed = -1 | |
| eta = 0.0 | |
| outputs = process(input_image_pil, mask_prompt, prompt, a_prompt, n_prompt, num_samples, image_resolution, | |
| detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta) | |
| image_list = [] | |
| input_image = resize_image(input_image, 512) | |
| image_list.append(torch.tensor(input_image)) | |
| for i in range(len(outputs)): | |
| each = outputs[i] | |
| if type(each) is not np.ndarray: | |
| each = np.array(each, dtype=np.uint8) | |
| each = resize_image(each, 512) | |
| print(i, each.shape) | |
| image_list.append(torch.tensor(each)) | |
| image_list = torch.stack(image_list).permute(0, 3, 1, 2) | |
| save_image(image_list, "sample.jpg", nrow=3, | |
| normalize=True, value_range=(0, 255)) | |
| else: | |
| print("The GUI is not tested yet. Please open an issue if you find bugs.") | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown( | |
| "## Edit Anything powered by ControlNet+SAM+BLIP2+Stable Diffusion") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider( | |
| label="Images", minimum=1, maximum=12, value=1, step=1) | |
| image_resolution = gr.Slider( | |
| label="Image Resolution", minimum=256, maximum=768, value=512, step=64) | |
| strength = gr.Slider( | |
| label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label='Guess Mode', value=False) | |
| detect_resolution = gr.Slider( | |
| label="SAM Resolution", minimum=128, maximum=2048, value=1024, step=1) | |
| ddim_steps = gr.Slider( | |
| label="Steps", minimum=1, maximum=100, value=20, step=1) | |
| scale = gr.Slider( | |
| label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, | |
| maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| mask_prompt = gr.Textbox( | |
| label="Mask Prompt", value='best quality, extremely detailed') | |
| a_prompt = gr.Textbox( | |
| label="Added Prompt", value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') | |
| with gr.Column(): | |
| result_gallery = gr.Gallery( | |
| label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| ips = [input_image, mask_prompt, prompt, a_prompt, n_prompt, num_samples, image_resolution, | |
| detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| block.launch(server_name='0.0.0.0') | |