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import os |
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import time |
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from omegaconf import OmegaConf |
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import torch |
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from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z |
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from utils.utils import instantiate_from_config |
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from huggingface_hub import hf_hub_download |
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from einops import repeat |
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import torchvision.transforms as transforms |
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from pytorch_lightning import seed_everything |
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class Image2Video(): |
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def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None: |
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self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) |
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self.download_model() |
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self.result_dir = result_dir |
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if not os.path.exists(self.result_dir): |
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os.mkdir(self.result_dir) |
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ckpt_path='checkpoints/dynamicrafter_'+resolution.split('_')[1]+'_v1/model.ckpt' |
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config_file='configs/inference_'+resolution.split('_')[1]+'_v1.0.yaml' |
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config = OmegaConf.load(config_file) |
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model_config = config.pop("model", OmegaConf.create()) |
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model_config['params']['unet_config']['params']['use_checkpoint']=False |
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model_list = [] |
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for gpu_id in range(gpu_num): |
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model = instantiate_from_config(model_config) |
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assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" |
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model = load_model_checkpoint(model, ckpt_path) |
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model.eval() |
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model_list.append(model) |
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self.model_list = model_list |
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self.save_fps = 8 |
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def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123): |
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seed_everything(seed) |
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transform = transforms.Compose([ |
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transforms.Resize(min(self.resolution)), |
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transforms.CenterCrop(self.resolution), |
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]) |
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torch.cuda.empty_cache() |
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print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) |
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start = time.time() |
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gpu_id=0 |
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if steps > 60: |
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steps = 60 |
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model = self.model_list[gpu_id] |
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model = model.cuda() |
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batch_size=1 |
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channels = model.model.diffusion_model.out_channels |
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frames = model.temporal_length |
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h, w = self.resolution[0] // 8, self.resolution[1] // 8 |
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noise_shape = [batch_size, channels, frames, h, w] |
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text_emb = model.get_learned_conditioning([prompt]) |
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img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) |
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img_tensor = (img_tensor / 255. - 0.5) * 2 |
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image_tensor_resized = transform(img_tensor) |
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videos = image_tensor_resized.unsqueeze(0) |
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z = get_latent_z(model, videos.unsqueeze(2)) |
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img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) |
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cond_images = model.embedder(img_tensor.unsqueeze(0)) |
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img_emb = model.image_proj_model(cond_images) |
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imtext_cond = torch.cat([text_emb, img_emb], dim=1) |
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fs = torch.tensor([fs], dtype=torch.long, device=model.device) |
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cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} |
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batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) |
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prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt |
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prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str |
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prompt_str=prompt_str[:40] |
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if len(prompt_str) == 0: |
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prompt_str = 'empty_prompt' |
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save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) |
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print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") |
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model = model.cpu() |
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return os.path.join(self.result_dir, f"{prompt_str}.mp4") |
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def download_model(self): |
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REPO_ID = 'Doubiiu/DynamiCrafter' |
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filename_list = ['model.ckpt'] |
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if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/'): |
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os.makedirs('./dynamicrafter_'+str(self.resolution[1])+'_v1/') |
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for filename in filename_list: |
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local_file = os.path.join('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', filename) |
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if not os.path.exists(local_file): |
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hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', local_dir_use_symlinks=False) |
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if __name__ == '__main__': |
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i2v = Image2Video() |
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video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset') |
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print('done', video_path) |