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| import gradio as gr | |
| import os | |
| import sys | |
| import argparse | |
| import random | |
| import time | |
| from omegaconf import OmegaConf | |
| import torch | |
| import torchvision | |
| from pytorch_lightning import seed_everything | |
| from huggingface_hub import hf_hub_download | |
| from einops import repeat | |
| import torchvision.transforms as transforms | |
| from utils.utils import instantiate_from_config | |
| sys.path.insert(0, "scripts/evaluation") | |
| from funcs import ( | |
| batch_ddim_sampling, | |
| load_model_checkpoint, | |
| get_latent_z, | |
| save_videos | |
| ) | |
| def download_model(): | |
| REPO_ID = 'Doubiiu/DynamiCrafter_512' | |
| filename_list = ['model.ckpt'] | |
| if not os.path.exists('./checkpoints/dynamicrafter_512_v1/'): | |
| os.makedirs('./checkpoints/dynamicrafter_512_v1/') | |
| for filename in filename_list: | |
| local_file = os.path.join('./checkpoints/dynamicrafter_512_v1/', filename) | |
| if not os.path.exists(local_file): | |
| hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_512_v1/', force_download=True) | |
| def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123): | |
| resolution = (320, 512) | |
| download_model() | |
| ckpt_path='checkpoints/dynamicrafter_512_v1/model.ckpt' | |
| config_file='configs/inference_512_v1.0.yaml' | |
| config = OmegaConf.load(config_file) | |
| model_config = config.pop("model", OmegaConf.create()) | |
| model_config['params']['unet_config']['params']['use_checkpoint']=False | |
| model = instantiate_from_config(model_config) | |
| assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" | |
| model = load_model_checkpoint(model, ckpt_path) | |
| model.eval() | |
| model = model.cuda() | |
| save_fps = 8 | |
| seed_everything(seed) | |
| transform = transforms.Compose([ | |
| transforms.Resize(min(resolution)), | |
| transforms.CenterCrop(resolution), | |
| ]) | |
| torch.cuda.empty_cache() | |
| print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) | |
| start = time.time() | |
| if steps > 60: | |
| steps = 60 | |
| batch_size=1 | |
| channels = model.model.diffusion_model.out_channels | |
| frames = model.temporal_length | |
| h, w = resolution[0] // 8, resolution[1] // 8 | |
| noise_shape = [batch_size, channels, frames, h, w] | |
| # text cond | |
| text_emb = model.get_learned_conditioning([prompt]) | |
| # img cond | |
| img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) | |
| img_tensor = (img_tensor / 255. - 0.5) * 2 | |
| image_tensor_resized = transform(img_tensor) #3,256,256 | |
| videos = image_tensor_resized.unsqueeze(0) # bchw | |
| z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw | |
| img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) | |
| cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc | |
| img_emb = model.image_proj_model(cond_images) | |
| imtext_cond = torch.cat([text_emb, img_emb], dim=1) | |
| fs = torch.tensor([fs], dtype=torch.long, device=model.device) | |
| cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} | |
| ## inference | |
| batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) | |
| ## b,samples,c,t,h,w | |
| video_path = './output.mp4' | |
| save_videos(batch_samples, './', filenames=['output'], fps=save_fps) | |
| model = model.cpu() | |
| return video_path | |
| i2v_examples = [ | |
| ['prompts/512/bloom01.png', 'time-lapse of a blooming flower with leaves and a stem', 50, 7.5, 1.0, 24, 123], | |
| ['prompts/512/campfire.png', 'a bonfire is lit in the middle of a field', 50, 7.5, 1.0, 24, 123], | |
| ['prompts/512/isometric.png', 'rotating view, small house', 50, 7.5, 1.0, 24, 123], | |
| ['prompts/512/girl08.png', 'a woman looking out in the rain', 50, 7.5, 1.0, 24, 1234], | |
| ['prompts/512/ship02.png', 'a sailboat sailing in rough seas with a dramatic sunset', 50, 7.5, 1.0, 24, 123], | |
| ['prompts/512/zreal_penguin.png', 'a group of penguins walking on a beach', 50, 7.5, 1.0, 20, 123], | |
| ] | |
| css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}""" | |
| with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface: | |
| gr.Markdown("<div align='center'> <h1> DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors </span> </h1> \ | |
| <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\ | |
| <a href='https://doubiiu.github.io/'>Jinbo Xing</a>, \ | |
| <a href='https://menghanxia.github.io/'>Menghan Xia</a>, <a href='https://yzhang2016.github.io/'>Yong Zhang</a>, \ | |
| <a href=''>Haoxin Chen</a>, <a href=''> Wangbo Yu</a>,\ | |
| <a href='https://github.com/hyliu'>Hanyuan Liu</a>, <a href='https://xinntao.github.io/'>Xintao Wang</a>,\ | |
| <a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html'>Tien-Tsin Wong</a>,\ | |
| <a href='https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=zh-CN'>Ying Shan</a>\ | |
| </h2> \ | |
| <a style='font-size:18px;color: #000000' href='https://arxiv.org/abs/2310.12190'> [ArXiv] </a>\ | |
| <a style='font-size:18px;color: #000000' href='https://doubiiu.github.io/projects/DynamiCrafter/'> [Project Page] </a> \ | |
| <a style='font-size:18px;color: #000000' href='https://github.com/Doubiiu/DynamiCrafter'> [Github] </a> </div>") | |
| with gr.Tab(label='ImageAnimation_320x512'): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| i2v_input_image = gr.Image(label="Input Image",elem_id="input_img") | |
| with gr.Row(): | |
| i2v_input_text = gr.Text(label='Prompts') | |
| with gr.Row(): | |
| i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123) | |
| i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") | |
| i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale") | |
| with gr.Row(): | |
| i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50) | |
| i2v_motion = gr.Slider(minimum=15, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=24) | |
| i2v_end_btn = gr.Button("Generate") | |
| # with gr.Tab(label='Result'): | |
| with gr.Row(): | |
| i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True) | |
| gr.Examples(examples=i2v_examples, | |
| inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed], | |
| outputs=[i2v_output_video], | |
| fn = infer, | |
| ) | |
| i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed], | |
| outputs=[i2v_output_video], | |
| fn = infer | |
| ) | |
| dynamicrafter_iface.queue(max_size=12).launch(show_api=True) |