# 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 subprocess from collections import OrderedDict import cv2 import einops import gradio as gr import numpy as np import torch import random import os import requests from io import BytesIO from annotator.util import resize_image, HWC3 def create_demo(): device = "cuda" if torch.cuda.is_available() else "cpu" use_blip = True use_gradio = True # Diffusion init using diffusers. # diffusers==0.14.0 required. from diffusers import ControlNetModel, UniPCMultistepScheduler from utils.stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline from diffusers.utils import load_image base_model_path = "stabilityai/stable-diffusion-2-inpainting" config_dict = OrderedDict([('SAM Pretrained(v0-1): Good Natural Sense', 'shgao/edit-anything-v0-1-1'), ('LAION Pretrained(v0-3): Good Face', 'shgao/edit-anything-v0-3'), # ('LAION Pretrained(v0-3-1)', '../../edit/edit-anything-ckpt-v0-3'), ]) def obtain_generation_model(controlnet_path): 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) return pipe global default_controlnet_path global pipe default_controlnet_path = config_dict['LAION Pretrained(v0-3): Good Face'] pipe = obtain_generation_model(default_controlnet_path) # Segment-Anything init. # pip install git+https://github.com/facebookresearch/segment-anything.git try: from segment_anything import sam_model_registry, SamAutomaticMaskGenerator except ImportError: print('segment_anything not installed') result = subprocess.run(['pip', 'install', 'git+https://github.com/facebookresearch/segment-anything.git'], check=True) print(f'Install segment_anything {result}') from segment_anything import sam_model_registry, SamAutomaticMaskGenerator if not os.path.exists('./models/sam_vit_h_4b8939.pth'): result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth', '-P', 'models'], check=True) print(f'Download sam_vit_h_4b8939.pth {result}') 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) full_img = Image.fromarray(np.uint8(full_img)) 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(condition_model, source_image, enable_all_generate, mask_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta): input_image = source_image["image"] if mask_image is None: if enable_all_generate: print("source_image", source_image["mask"].shape, input_image.shape,) print(source_image["mask"].max()) mask_image = np.ones((input_image.shape[0], input_image.shape[1], 3))*255 else: mask_image = source_image["mask"] global default_controlnet_path print("To Use:", config_dict[condition_model], "Current:", default_controlnet_path) if default_controlnet_path!=config_dict[condition_model]: print("Change condition model to:", config_dict[condition_model]) global pipe pipe = obtain_generation_model(config_dict[condition_model]) default_controlnet_path = config_dict[condition_model] with torch.no_grad(): if use_blip and (enable_auto_prompt or len(prompt) == 0): 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, prompt 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: # This part is not updated, it's just a example to use it without GUI. image_path = "../data/samples/sa_223750.jpg" mask_path = "../data/samples/sa_223750inpaint.png" input_image = Image.open(image_path) mask_image = Image.open(mask_path) enable_auto_prompt = True input_image = np.array(input_image, dtype=np.uint8) mask_image = np.array(mask_image, dtype=np.uint8) prompt = "esplendent sunset sky, red brick wall" 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(condition_model, input_image, mask_image, enable_auto_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 fully tested yet. Please open an issue if you find bugs.") block = gr.Blocks() with block as demo: with gr.Row(): gr.Markdown( "## Edit Anything") with gr.Row(): with gr.Column(): source_image = gr.Image(source='upload',label="Image (Upload an image and cover the region you want to edit with sketch)", type="numpy", tool="sketch") enable_all_generate = gr.Checkbox(label='Auto generation on all region.', value=True) prompt = gr.Textbox(label="Prompt (Text in the expected things of edited region)") enable_auto_prompt = gr.Checkbox(label='Auto generate text prompt from input image with BLIP2: Warning: Enable this may makes your prompt not working.', value=True) run_button = gr.Button(label="Run") condition_model = gr.Dropdown(choices=list(config_dict.keys()), value=list(config_dict.keys())[1], label='Model', multiselect=False) num_samples = gr.Slider( label="Images", minimum=1, maximum=12, value=2, step=1) with gr.Accordion("Advanced options", open=False): mask_image = gr.Image(source='upload', label="(Optional) Upload a predefined mask of edit region if you do not want to write your prompt.", type="numpy", value=None) 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') result_text = gr.Text(label='BLIP2+Human Prompt Text') ips = [condition_model, source_image, enable_all_generate, mask_image, enable_auto_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, result_text]) return demo if __name__ == '__main__': demo = create_demo() demo.queue().launch(server_name='0.0.0.0')