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| # Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2 | |
| from diffusers.utils import load_image | |
| from diffusers import 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 | |
| import base64 | |
| from io import BytesIO | |
| from utils.stable_diffusion_controlnet import StableDiffusionControlNetPipeline2, ControlNetModel2 | |
| def create_demo(): | |
| MAX_COLORS = 12 | |
| canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>" | |
| load_js = """ | |
| async () => { | |
| const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js" | |
| fetch(url) | |
| .then(res => res.text()) | |
| .then(text => { | |
| const script = document.createElement('script'); | |
| script.type = "module" | |
| script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' })); | |
| document.head.appendChild(script); | |
| }); | |
| } | |
| """ | |
| get_js_colors = """ | |
| async (canvasData) => { | |
| const canvasEl = document.getElementById("canvas-root"); | |
| return [canvasEl._data] | |
| } | |
| """ | |
| set_canvas_size = """ | |
| async (aspect) => { | |
| if(aspect ==='square'){ | |
| _updateCanvas(512,512) | |
| } | |
| if(aspect ==='horizontal'){ | |
| _updateCanvas(768,512) | |
| } | |
| if(aspect ==='vertical'){ | |
| _updateCanvas(512,768) | |
| } | |
| } | |
| """ | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # aspect = gr.Radio(["square", "horizontal", "vertical"], value="square", label="Aspect Ratio", visible=False if is_shared_ui else 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'), | |
| ('LAION Pretrained(v0-4)', 'shgao/edit-anything-v0-4-sd21'), | |
| ]) | |
| def obtain_generation_model(controlnet_path): | |
| controlnet = ControlNetModel2.from_pretrained( | |
| controlnet_path, torch_dtype=torch.float16) | |
| pipe = StableDiffusionControlNetPipeline2.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(v0-4)'] | |
| pipe = obtain_generation_model(default_controlnet_path) | |
| def get_sam_control(image): | |
| im2arr = np.array(image) | |
| colors_map, res = None, None | |
| ptr = 0 | |
| for color in colors: | |
| r, g, b = color | |
| if any(c != 255 for c in (r, g, b)): | |
| binary_matrix = np.all(im2arr == (r, g, b), axis=-1) | |
| if colors_map is None: | |
| colors_map = np.zeros((im2arr.shape[0], im2arr.shape[1]), dtype=np.uint16) | |
| res = np.zeros((im2arr.shape[0], im2arr.shape[1], 3)) | |
| colors_map[binary_matrix != 0] = ptr + 1 | |
| ptr += 1 | |
| white = np.all(im2arr == (255, 255, 255), axis=-1) | |
| scale_map = (white != 1).astype(np.float32) | |
| res[:, :, 0] = colors_map % 256 | |
| res[:, :, 1] = colors_map // 256 | |
| res.astype(np.float32) | |
| return image, res, scale_map | |
| def process_sketch(canvas_data): | |
| nonlocal colors | |
| base64_img = canvas_data['image'] | |
| image_data = base64.b64decode(base64_img.split(',')[1]) | |
| image = Image.open(BytesIO(image_data)).convert("RGB") | |
| colors = [tuple(map(int, rgb[4:-1].split(','))) for rgb in canvas_data['colors']] | |
| print(colors) | |
| # binary_matrixes['sketch'] = res | |
| return image, "sketch loaded." | |
| def process(condition_model, input_image, control_scale, prompt, a_prompt, n_prompt, | |
| num_samples, image_resolution, ddim_steps, guess_mode, use_scale_map, 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(): | |
| print("All text:", prompt) | |
| input_image = HWC3(input_image) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| # the default SAM model is trained with 1024 size. | |
| fullseg, detected_map, scale_map = get_sam_control(input_image) | |
| 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() | |
| scale_map = torch.from_numpy(scale_map).float().cuda() if use_scale_map else None | |
| 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, | |
| controlnet_conditioning_scale=float(control_scale), | |
| controlnet_conditioning_scale_map=scale_map, | |
| image=control.type(torch.float16), | |
| ).images | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return [fullseg] + results, prompt, "waiting for sketch..." | |
| # disable gradio when not using GUI. | |
| block = gr.Blocks() | |
| with block as demo: | |
| colors = [] | |
| with gr.Row(): | |
| gr.Markdown( | |
| "## Generate Anything") | |
| with gr.Row(): | |
| with gr.Column(): | |
| canvas_data = gr.JSON(value={}, visible=False) | |
| canvas = gr.HTML(canvas_html) | |
| aspect = gr.Radio(["square", "horizontal", "vertical"], value="square", label="Aspect Ratio", | |
| visible=False) | |
| button_run = gr.Button("I've finished my sketch", elem_id="main_button", interactive=True) | |
| result_text1 = gr.Text(label='sketch status:') | |
| with gr.Column(visible=True) as post_sketch: | |
| input_image = gr.Image(type="numpy", visible=False) | |
| 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) | |
| control_scale = gr.Slider( | |
| label="Mask Align strength", info="Large value -> strict alignment with SAM mask", minimum=0, | |
| maximum=1, value=1, step=0.1) | |
| num_samples = gr.Slider( | |
| label="Images", minimum=1, maximum=12, value=1, step=1) | |
| # enable_auto_prompt = 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) | |
| use_scale_map = gr.Checkbox(label='Use scale map', value=False) | |
| 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') | |
| aspect.change(None, inputs=[aspect], outputs=None, _js=set_canvas_size) | |
| button_run.click(process_sketch, inputs=[canvas_data], | |
| outputs=[input_image, result_text1], _js=get_js_colors, queue=False) | |
| ips = [condition_model, input_image, control_scale, prompt, a_prompt, n_prompt, | |
| num_samples, image_resolution, ddim_steps, guess_mode, use_scale_map, strength, scale, seed, eta] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery, result_text, result_text1]) | |
| demo.load(None, None, None, _js=load_js) | |
| return demo | |
| if __name__ == '__main__': | |
| demo = create_demo() | |
| demo.queue().launch(server_name='0.0.0.0', share=True) | |