# Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2 from diffusers.utils import load_image from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler 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 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. base_model_path = "stabilityai/stable-diffusion-2-1" config_dict = OrderedDict([('SAM Pretrained(v0-1)', 'shgao/edit-anything-v0-1-1'), ('LAION Pretrained(v0-3)', 'shgao/edit-anything-v0-3'), ]) def obtain_generation_model(controlnet_path): controlnet = ControlNetModel.from_pretrained( controlnet_path, torch_dtype=torch.float16) pipe = StableDiffusionControlNetPipeline.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 default_controlnet_path = config_dict['LAION Pretrained(v0-3)'] 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) 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, input_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta): global default_controlnet_path global pipe 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]) 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() if seed == -1: seed = random.randint(0, 65535) seed_everything(seed) print("control.shape", control.shape) generator = torch.manual_seed(seed) x_samples = pipe( 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, height=H, width=W, image=control.type(torch.float16), ).images results = [x_samples[i] for i in range(num_samples)] return [full_segmask] + results, prompt # 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. condition_model = 'shgao/edit-anything-v0-1-1' 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 = 4 image_resolution = 512 detect_resolution = 512 ddim_steps = 100 guess_mode = False strength = 1.0 scale = 9.0 seed = 10086 eta = 0.0 outputs, full_text = process(condition_model, 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] 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: block = gr.Blocks() with block as demo: with gr.Row(): gr.Markdown( "## Generate Anything") with gr.Row(): with gr.Column(): input_image = gr.Image(source='upload', type="numpy") prompt = gr.Textbox(label="Prompt (Optional)") run_button = gr.Button(label="Run") condition_model = gr.Dropdown(choices=list(config_dict.keys()), value=list(config_dict.keys())[0], label='Model', multiselect=False) num_samples = gr.Slider( label="Images", minimum=1, maximum=12, value=1, step=1) enable_auto_prompt = gr.Checkbox(label='Auto generated BLIP2 prompt', value=True) with gr.Accordion("Advanced options", open=False): 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, input_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')