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| # Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2 | |
| from torchvision.utils import save_image | |
| from PIL import Image | |
| from cldm.ddim_hacked import DDIMSampler | |
| from cldm.model import create_model, load_state_dict | |
| from pytorch_lightning import seed_everything | |
| from share import * | |
| import config | |
| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import random | |
| import os | |
| from annotator.util import resize_image, HWC3 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| use_blip = True | |
| use_gradio = False | |
| # Diffusion init. | |
| model = create_model('./models/cldm_v21.yaml').cpu() | |
| model.load_state_dict(load_state_dict( | |
| 'models/edit-anything-ckpt-v0-1.ckpt', location='cuda')) | |
| model.to(device=device) | |
| ddim_sampler = DDIMSampler(model) | |
| # 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" | |
| model_type = "default" | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) | |
| sam.to(device=device) | |
| mask_generator = SamAutomaticMaskGenerator(sam) | |
| # 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) | |
| 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 process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta): | |
| with torch.no_grad(): | |
| 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() | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| seed_everything(seed) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| cond = {"c_concat": [control], "c_crossattn": [ | |
| model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [ | |
| model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=True) | |
| model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ( | |
| [strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 | |
| samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, | |
| shape, cond, verbose=False, eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| x_samples = model.decode_first_stage(samples) | |
| x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') | |
| * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8) | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return [full_segmask] + results | |
| # disable gradio when not using GUI. | |
| if not use_gradio: | |
| image_path = "images/sa_309398.jpg" | |
| input_image = Image.open(image_path) | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| prompt = "" | |
| 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 = 5 | |
| image_resolution = 512 | |
| detect_resolution = 512 | |
| ddim_steps = 100 | |
| guess_mode = False | |
| strength = 1.0 | |
| scale = 9.0 | |
| seed = 10086 | |
| eta = 0.0 | |
| outputs = process(input_image, 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] | |
| 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=4, | |
| normalize=True, value_range=(0, 255)) | |
| else: | |
| 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) | |
| 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, 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') | |