import os print("Installing correct gradio version...") os.system("pip uninstall -y gradio") os.system("pip install gradio==4.37.2") print("Installing Finished!") import gradio as gr import numpy as np import cv2 import uuid import torch import torchvision import json import spaces from PIL import Image from omegaconf import OmegaConf from einops import rearrange, repeat from torchvision import transforms from transformers import CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDIMScheduler from pipelines.pipeline_imagecoductor import ImageConductorPipeline from modules.unet import UNet3DConditionFlowModel from utils.gradio_utils import ensure_dirname, split_filename, visualize_drag, image2pil, image2arr from utils.utils import create_image_controlnet, create_flow_controlnet, interpolate_trajectory, load_weights, load_model, bivariate_Gaussian from utils.lora_utils import add_LoRA_to_controlnet from utils.visualizer import Visualizer, vis_flow_to_video #### Description #### title = r"""

CustomNet: Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models

""" head = r"""

Image Conductor: Precision Control for Interactive Video Synthesis

Project Page

""" descriptions = r""" Official Gradio Demo for Image Conductor: Precision Control for Interactive Video Synthesis.
🧙Image Conductor enables precise, fine-grained control for generating motion-controllable videos from images, advancing the practical application of interactive video synthesis.
""" instructions = r""" - ⭐️ step1: Upload or select one image from Example. - ⭐️ step2: Click 'Add Drag' to draw some drags. - ⭐️ step3: Input text prompt that complements the image (Necessary). - ⭐️ step4: Select 'Drag Mode' to specify the control of camera transition or object movement. - ⭐️ step5: Click 'Run' button to generate video assets. - ⭐️ others: Click 'Delete last drag' to delete the whole lastest path. Click 'Delete last step' to delete the lastest clicked control point. """ citation = r""" If Image Conductor is helpful, please help to ⭐ the Github Repo. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/liyaowei-stu%2FImageConductor)](https://github.com/liyaowei-stu/ImageConductor) --- 📝 **Citation**
If our work is useful for your research, please consider citing: ```bibtex @misc{li2024imageconductor, title={Image Conductor: Precision Control for Interactive Video Synthesis}, author={Li, Yaowei and Wang, Xintao and Zhang, Zhaoyang and Wang, Zhouxia and Yuan, Ziyang and Xie, Liangbin and Zou, Yuexian and Shan, Ying}, year={2024}, eprint={2406.15339}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` 📧 **Contact**
If you have any questions, please feel free to reach me out at ywl@stu.pku.edu.cn. # """ os.makedirs("models/personalized") os.makedirs("models/sd1-5") if not os.path.exists("models/flow_controlnet.ckpt"): os.system(f'wget -q https://huggingface.co/TencentARC/ImageConductor/resolve/main/flow_controlnet.ckpt?download=true -P models/') os.system(f'mv models/flow_controlnet.ckpt?download=true models/flow_controlnet.ckpt') print("flow_controlnet Download!", ) if not os.path.exists("models/image_controlnet.ckpt"): os.system(f'wget -q https://huggingface.co/TencentARC/ImageConductor/resolve/main/image_controlnet.ckpt?download=true -P models/') os.system(f'mv models/image_controlnet.ckpt?download=true models/image_controlnet.ckpt') print("image_controlnet Download!", ) if not os.path.exists("models/unet.ckpt"): os.system(f'wget -q https://huggingface.co/TencentARC/ImageConductor/resolve/main/unet.ckpt?download=true -P models/') os.system(f'mv models/unet.ckpt?download=true models/unet.ckpt') print("unet Download!", ) if not os.path.exists("models/sd1-5/config.json"): os.system(f'wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/unet/config.json?download=true -P models/sd1-5/') os.system(f'mv models/sd1-5/config.json?download=true models/sd1-5/config.json') print("config Download!", ) if not os.path.exists("models/sd1-5/unet.ckpt"): os.system(f'cp -r models/unet.ckpt models/sd1-5/unet.ckpt') # os.system(f'wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/unet/diffusion_pytorch_model.bin?download=true -P models/sd1-5/') if not os.path.exists("models/personalized/helloobjects_V12c.safetensors"): os.system(f'wget -q https://huggingface.co/TencentARC/ImageConductor/resolve/main/helloobjects_V12c.safetensors?download=true -P models/personalized') os.system(f'mv models/personalized/helloobjects_V12c.safetensors?download=true models/personalized/helloobjects_V12c.safetensors') print("helloobjects_V12c Download!", ) if not os.path.exists("models/personalized/TUSUN.safetensors"): os.system(f'wget -q https://huggingface.co/TencentARC/ImageConductor/resolve/main/TUSUN.safetensors?download=true -P models/personalized') os.system(f'mv models/personalized/TUSUN.safetensors?download=true models/personalized/TUSUN.safetensors') print("TUSUN Download!", ) # - - - - - examples - - - - - # image_examples = [ ["__asset__/images/object/turtle-1.jpg", "a sea turtle gracefully swimming over a coral reef in the clear blue ocean.", "object", 11318446767408804497, "", "turtle" ], ["__asset__/images/object/rose-1.jpg", "a red rose engulfed in flames.", "object", 6854275249656120509, "", "rose", ], ["__asset__/images/object/jellyfish-1.jpg", "intricate detailing,photorealism,hyperrealistic, glowing jellyfish mushroom, flying, starry sky, bokeh, golden ratio composition.", "object", 17966188172968903484, "HelloObject", "jellyfish" ], ["__asset__/images/camera/lush-1.jpg", "detailed craftsmanship, photorealism, hyperrealistic, roaring waterfall, misty spray, lush greenery, vibrant rainbow, golden ratio composition.", "camera", 7970487946960948963, "HelloObject", "lush", ], ["__asset__/images/camera/tusun-1.jpg", "tusuncub with its mouth open, blurry, open mouth, fangs, photo background, looking at viewer, tongue, full body, solo, cute and lovely, Beautiful and realistic eye details, perfect anatomy, Nonsense, pure background, Centered-Shot, realistic photo, photograph, 4k, hyper detailed, DSLR, 24 Megapixels, 8mm Lens, Full Frame, film grain, Global Illumination, studio Lighting, Award Winning Photography, diffuse reflection, ray tracing.", "camera", 996953226890228361, "TUSUN", "tusun", ], ["__asset__/images/camera/painting-1.jpg", "A oil painting.", "camera", 16867854766769816385, "", "painting" ], ] POINTS = { 'turtle': "__asset__/trajs/object/turtle-1.json", 'rose': "__asset__/trajs/object/rose-1.json", 'jellyfish': "__asset__/trajs/object/jellyfish-1.json", 'lush': "__asset__/trajs/camera/lush-1.json", 'tusun': "__asset__/trajs/camera/tusun-1.json", 'painting': "__asset__/trajs/camera/painting-1.json", } IMAGE_PATH = { 'turtle': "__asset__/images/object/turtle-1.jpg", 'rose': "__asset__/images/object/rose-1.jpg", 'jellyfish': "__asset__/images/object/jellyfish-1.jpg", 'lush': "__asset__/images/camera/lush-1.jpg", 'tusun': "__asset__/images/camera/tusun-1.jpg", 'painting': "__asset__/images/camera/painting-1.jpg", } DREAM_BOOTH = { 'HelloObject': 'models/personalized/helloobjects_V12c.safetensors', } LORA = { 'TUSUN': 'models/personalized/TUSUN.safetensors', } LORA_ALPHA = { 'TUSUN': 0.6, } NPROMPT = { "HelloObject": 'FastNegativeV2,(bad-artist:1),(worst quality, low quality:1.4),(bad_prompt_version2:0.8),bad-hands-5,lowres,bad anatomy,bad hands,((text)),(watermark),error,missing fingers,extra digit,fewer digits,cropped,worst quality,low quality,normal quality,((username)),blurry,(extra limbs),bad-artist-anime,badhandv4,EasyNegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3,BadDream,(three hands:1.6),(three legs:1.2),(more than two hands:1.4),(more than two legs,:1.2)' } output_dir = "outputs" ensure_dirname(output_dir) def points_to_flows(track_points, model_length, height, width): input_drag = np.zeros((model_length - 1, height, width, 2)) for splited_track in track_points: if len(splited_track) == 1: # stationary point displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1]) splited_track = tuple([splited_track[0], displacement_point]) # interpolate the track splited_track = interpolate_trajectory(splited_track, model_length) splited_track = splited_track[:model_length] if len(splited_track) < model_length: splited_track = splited_track + [splited_track[-1]] * (model_length -len(splited_track)) for i in range(model_length - 1): start_point = splited_track[i] end_point = splited_track[i+1] input_drag[i][int(start_point[1])][int(start_point[0])][0] = end_point[0] - start_point[0] input_drag[i][int(start_point[1])][int(start_point[0])][1] = end_point[1] - start_point[1] return input_drag class ImageConductor: def __init__(self, device, unet_path, image_controlnet_path, flow_controlnet_path, height, width, model_length, lora_rank=64): self.device = device tokenizer = CLIPTokenizer.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="tokenizer") text_encoder = CLIPTextModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="text_encoder").to(device) vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae").to(device) inference_config = OmegaConf.load("configs/inference/inference.yaml") unet = UNet3DConditionFlowModel.from_pretrained_2d("models/sd1-5/", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)) self.vae = vae ### >>> Initialize UNet module >>> ### load_model(unet, unet_path) ### >>> Initialize image controlnet module >>> ### image_controlnet = create_image_controlnet("configs/inference/image_condition.yaml", unet) load_model(image_controlnet, image_controlnet_path) ### >>> Initialize flow controlnet module >>> ### flow_controlnet = create_flow_controlnet("configs/inference/flow_condition.yaml", unet) add_LoRA_to_controlnet(lora_rank, flow_controlnet) load_model(flow_controlnet, flow_controlnet_path) unet.eval().to(device) image_controlnet.eval().to(device) flow_controlnet.eval().to(device) self.pipeline = ImageConductorPipeline( unet=unet, vae=vae, tokenizer=tokenizer, text_encoder=text_encoder, scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)), image_controlnet=image_controlnet, flow_controlnet=flow_controlnet, ).to(device) self.height = height self.width = width # _, model_step, _ = split_filename(model_path) # self.ouput_prefix = f'{model_step}_{width}X{height}' self.model_length = model_length blur_kernel = bivariate_Gaussian(kernel_size=99, sig_x=10, sig_y=10, theta=0, grid=None, isotropic=True) self.blur_kernel = blur_kernel @spaces.GPU(enable_queue=True, duration=100) @torch.no_grad() def run(self, first_frame_path, tracking_points, prompt, drag_mode, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized, examples_type): if examples_type != "": ### for adapting high version gradio first_frame_path = IMAGE_PATH[examples_type] tracking_points = json.load(open(POINTS[examples_type])) print("example first_frame_path", first_frame_path) print("example tracking_points", tracking_points) original_width, original_height=384, 256 if isinstance(tracking_points, list): input_all_points = tracking_points else: input_all_points = tracking_points.value print("input_all_points", input_all_points) resized_all_points = [tuple([tuple([float(e1[0]*self.width/original_width), float(e1[1]*self.height/original_height)]) for e1 in e]) for e in input_all_points] dir, base, ext = split_filename(first_frame_path) id = base.split('_')[-1] visualized_drag, _ = visualize_drag(first_frame_path, resized_all_points, drag_mode, self.width, self.height, self.model_length) ## image condition image_transforms = transforms.Compose([ transforms.RandomResizedCrop( (self.height, self.width), (1.0, 1.0), ratio=(self.width/self.height, self.width/self.height) ), transforms.ToTensor(), ]) image_paths = [first_frame_path] controlnet_images = [(image_transforms(Image.open(path).convert("RGB"))) for path in image_paths] controlnet_images = torch.stack(controlnet_images).unsqueeze(0).to(device) controlnet_images = rearrange(controlnet_images, "b f c h w -> b c f h w") num_controlnet_images = controlnet_images.shape[2] controlnet_images = rearrange(controlnet_images, "b c f h w -> (b f) c h w") self.vae.to(device) controlnet_images = self.vae.encode(controlnet_images * 2. - 1.).latent_dist.sample() * 0.18215 controlnet_images = rearrange(controlnet_images, "(b f) c h w -> b c f h w", f=num_controlnet_images) # flow condition controlnet_flows = points_to_flows(resized_all_points, self.model_length, self.height, self.width) for i in range(0, self.model_length-1): controlnet_flows[i] = cv2.filter2D(controlnet_flows[i], -1, self.blur_kernel) controlnet_flows = np.concatenate([np.zeros_like(controlnet_flows[0])[np.newaxis, ...], controlnet_flows], axis=0) # pad the first frame with zero flow os.makedirs(os.path.join(output_dir, "control_flows"), exist_ok=True) trajs_video = vis_flow_to_video(controlnet_flows, num_frames=self.model_length) # T-1 x H x W x 3 torchvision.io.write_video(f'{output_dir}/control_flows/sample-{id}-train_flow.mp4', trajs_video, fps=8, video_codec='h264', options={'crf': '10'}) controlnet_flows = torch.from_numpy(controlnet_flows)[None][:, :self.model_length, ...] controlnet_flows = rearrange(controlnet_flows, "b f h w c-> b c f h w").float().to(device) dreambooth_model_path = DREAM_BOOTH.get(personalized, '') lora_model_path = LORA.get(personalized, '') lora_alpha = LORA_ALPHA.get(personalized, 0.6) self.pipeline = load_weights( self.pipeline, dreambooth_model_path = dreambooth_model_path, lora_model_path = lora_model_path, lora_alpha = lora_alpha, ).to(device) if NPROMPT.get(personalized, '') != '': negative_prompt = NPROMPT.get(personalized) if randomize_seed: random_seed = torch.seed() else: seed = int(seed) random_seed = seed torch.manual_seed(random_seed) torch.cuda.manual_seed_all(random_seed) print(f"current seed: {torch.initial_seed()}") sample = self.pipeline( prompt, negative_prompt = negative_prompt, num_inference_steps = num_inference_steps, guidance_scale = guidance_scale, width = self.width, height = self.height, video_length = self.model_length, controlnet_images = controlnet_images, # 1 4 1 32 48 controlnet_image_index = [0], controlnet_flows = controlnet_flows,# [1, 2, 16, 256, 384] control_mode = drag_mode, eval_mode = True, ).videos outputs_path = os.path.join(output_dir, f'output_{i}_{id}.mp4') vis_video = (rearrange(sample[0], 'c t h w -> t h w c') * 255.).clip(0, 255) torchvision.io.write_video(outputs_path, vis_video, fps=8, video_codec='h264', options={'crf': '10'}) return {output_image: visualized_drag, output_video: outputs_path} def reset_states(first_frame_path, tracking_points): first_frame_path = gr.State() tracking_points = gr.State([]) return {input_image:None, first_frame_path_var: first_frame_path, tracking_points_var: tracking_points} def preprocess_image(image, tracking_points): image_pil = image2pil(image.name) raw_w, raw_h = image_pil.size resize_ratio = max(384/raw_w, 256/raw_h) image_pil = image_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR) image_pil = transforms.CenterCrop((256, 384))(image_pil.convert('RGB')) id = str(uuid.uuid4())[:4] first_frame_path = os.path.join(output_dir, f"first_frame_{id}.jpg") image_pil.save(first_frame_path, quality=95) tracking_points = gr.State([]) return {input_image: first_frame_path, first_frame_path_var: first_frame_path, tracking_points_var: tracking_points} def add_tracking_points(tracking_points, first_frame_path, drag_mode, evt: gr.SelectData): # SelectData is a subclass of EventData if drag_mode=='object': color = (255, 0, 0, 255) elif drag_mode=='camera': color = (0, 0, 255, 255) print(f"You selected {evt.value} at {evt.index} from {evt.target}") tracking_points.value[-1].append(evt.index) print(tracking_points.value) transparent_background = Image.open(first_frame_path).convert('RGBA') w, h = transparent_background.size transparent_layer = np.zeros((h, w, 4)) for track in tracking_points.value: if len(track) > 1: for i in range(len(track)-1): start_point = track[i] end_point = track[i+1] vx = end_point[0] - start_point[0] vy = end_point[1] - start_point[1] arrow_length = np.sqrt(vx**2 + vy**2) if i == len(track)-2: cv2.arrowedLine(transparent_layer, tuple(start_point), tuple(end_point), color, 2, tipLength=8 / arrow_length) else: cv2.line(transparent_layer, tuple(start_point), tuple(end_point), color, 2,) else: cv2.circle(transparent_layer, tuple(track[0]), 5, color, -1) transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8)) trajectory_map = Image.alpha_composite(transparent_background, transparent_layer) return {tracking_points_var: tracking_points, input_image: trajectory_map} def add_drag(tracking_points): if not isinstance(tracking_points ,list): print("before", tracking_points.value) tracking_points.value.append([]) print(tracking_points.value) else: tracking_points.append([]) return {tracking_points_var: tracking_points} def delete_last_drag(tracking_points, first_frame_path, drag_mode): if drag_mode=='object': color = (255, 0, 0, 255) elif drag_mode=='camera': color = (0, 0, 255, 255) tracking_points.value.pop() transparent_background = Image.open(first_frame_path).convert('RGBA') w, h = transparent_background.size transparent_layer = np.zeros((h, w, 4)) for track in tracking_points.value: if len(track) > 1: for i in range(len(track)-1): start_point = track[i] end_point = track[i+1] vx = end_point[0] - start_point[0] vy = end_point[1] - start_point[1] arrow_length = np.sqrt(vx**2 + vy**2) if i == len(track)-2: cv2.arrowedLine(transparent_layer, tuple(start_point), tuple(end_point), color, 2, tipLength=8 / arrow_length) else: cv2.line(transparent_layer, tuple(start_point), tuple(end_point), color, 2,) else: cv2.circle(transparent_layer, tuple(track[0]), 5, color, -1) transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8)) trajectory_map = Image.alpha_composite(transparent_background, transparent_layer) return {tracking_points_var: tracking_points, input_image: trajectory_map} def delete_last_step(tracking_points, first_frame_path, drag_mode): if drag_mode=='object': color = (255, 0, 0, 255) elif drag_mode=='camera': color = (0, 0, 255, 255) tracking_points.value[-1].pop() transparent_background = Image.open(first_frame_path).convert('RGBA') w, h = transparent_background.size transparent_layer = np.zeros((h, w, 4)) for track in tracking_points.value: if len(track) > 1: for i in range(len(track)-1): start_point = track[i] end_point = track[i+1] vx = end_point[0] - start_point[0] vy = end_point[1] - start_point[1] arrow_length = np.sqrt(vx**2 + vy**2) if i == len(track)-2: cv2.arrowedLine(transparent_layer, tuple(start_point), tuple(end_point), color, 2, tipLength=8 / arrow_length) else: cv2.line(transparent_layer, tuple(start_point), tuple(end_point), color, 2,) else: cv2.circle(transparent_layer, tuple(track[0]), 5,color, -1) transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8)) trajectory_map = Image.alpha_composite(transparent_background, transparent_layer) return {tracking_points_var: tracking_points, input_image: trajectory_map} block = gr.Blocks( theme=gr.themes.Soft( radius_size=gr.themes.sizes.radius_none, text_size=gr.themes.sizes.text_md ) ).queue() with block: with gr.Row(): with gr.Column(): gr.HTML(head) gr.Markdown(descriptions) with gr.Accordion(label="🛠️ Instructions:", open=True, elem_id="accordion"): with gr.Row(equal_height=True): gr.Markdown(instructions) # device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") device = torch.device("cuda") unet_path = 'models/unet.ckpt' image_controlnet_path = 'models/image_controlnet.ckpt' flow_controlnet_path = 'models/flow_controlnet.ckpt' ImageConductor_net = ImageConductor(device=device, unet_path=unet_path, image_controlnet_path=image_controlnet_path, flow_controlnet_path=flow_controlnet_path, height=256, width=384, model_length=16 ) first_frame_path_var = gr.State(value=None) tracking_points_var = gr.State([]) with gr.Row(): with gr.Column(scale=1): image_upload_button = gr.UploadButton(label="Upload Image",file_types=["image"]) add_drag_button = gr.Button(value="Add Drag") reset_button = gr.Button(value="Reset") delete_last_drag_button = gr.Button(value="Delete last drag") delete_last_step_button = gr.Button(value="Delete last step") with gr.Column(scale=7): with gr.Row(): with gr.Column(scale=6): input_image = gr.Image(label="Input Image", interactive=True, height=300, width=384,) with gr.Column(scale=6): output_image = gr.Image(label="Motion Path", interactive=False, height=256, width=384,) with gr.Row(): with gr.Column(scale=1): prompt = gr.Textbox(value="a wonderful elf.", label="Prompt (highly-recommended)", interactive=True, visible=True) negative_prompt = gr.Text( label="Negative Prompt", max_lines=5, placeholder="Please input your negative prompt", value='worst quality, low quality, letterboxed',lines=1 ) drag_mode = gr.Radio(['camera', 'object'], label='Drag mode: ', value='object', scale=2) run_button = gr.Button(value="Run") with gr.Accordion("More input params", open=False, elem_id="accordion1"): with gr.Group(): seed = gr.Textbox( label="Seed: ", value=561793204, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=False) with gr.Group(): with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=1, maximum=12, step=0.1, value=8.5, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=25, ) with gr.Group(): personalized = gr.Dropdown(label="Personalized", choices=['HelloObject', 'TUSUN', ""], value="") examples_type = gr.Textbox(label="Examples Type (Ignore) ", value="", visible=False) with gr.Column(scale=7): output_video = gr.Video( label="Output Video", width=384, height=256) with gr.Row(): def process_example(input_image, prompt, drag_mode, seed, personalized, examples_type): return input_image, prompt, drag_mode, seed, personalized, examples_type example = gr.Examples( label="Input Example", examples=image_examples, inputs=[input_image, prompt, drag_mode, seed, personalized, examples_type], outputs=[input_image, prompt, drag_mode, seed, personalized, examples_type], fn=process_example, examples_per_page=10, cache_examples=False, ) with gr.Row(): gr.Markdown(citation) image_upload_button.upload(preprocess_image, [image_upload_button, tracking_points_var], [input_image, first_frame_path_var, tracking_points_var]) add_drag_button.click(add_drag, tracking_points_var, tracking_points_var) delete_last_drag_button.click(delete_last_drag, [tracking_points_var, first_frame_path_var, drag_mode], [tracking_points_var, input_image]) delete_last_step_button.click(delete_last_step, [tracking_points_var, first_frame_path_var, drag_mode], [tracking_points_var, input_image]) reset_button.click(reset_states, [first_frame_path_var, tracking_points_var], [input_image, first_frame_path_var, tracking_points_var]) input_image.select(add_tracking_points, [tracking_points_var, first_frame_path_var, drag_mode], [tracking_points_var, input_image]) run_button.click(ImageConductor_net.run, [first_frame_path_var, tracking_points_var, prompt, drag_mode, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized, examples_type], [output_image, output_video]) block.launch()