import gradio as gr import os import sys 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'] os.makedirs('./checkpoints/dynamicrafter_512_v1/', exist_ok=True) 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) # b, c, h, w z = get_latent_z(model, videos.unsqueeze(2)) # b, c, 1, h, w 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)) # b, l, c 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("""

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Xintao Wang, Tien-Tsin Wong, Ying Shan

[ArXiv] [Project Page] [Github]
""") 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.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(share=True, show_api=True)