# 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')