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| import os | |
| import time | |
| from omegaconf import OmegaConf | |
| import torch | |
| from scripts.evaluation.funcs import load_model_checkpoint, load_image_batch, save_videos, batch_ddim_sampling | |
| from utils.utils import instantiate_from_config | |
| from huggingface_hub import hf_hub_download | |
| class Image2Video(): | |
| def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: | |
| self.download_model() | |
| self.result_dir = result_dir | |
| if not os.path.exists(self.result_dir): | |
| os.mkdir(self.result_dir) | |
| ckpt_path='checkpoints/i2v_512_v1/model.ckpt' | |
| config_file='configs/inference_i2v_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_list = [] | |
| for gpu_id in range(gpu_num): | |
| model = instantiate_from_config(model_config) | |
| # model = model.cuda(gpu_id) | |
| assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" | |
| model = load_model_checkpoint(model, ckpt_path) | |
| model.eval() | |
| model_list.append(model) | |
| self.model_list = model_list | |
| self.save_fps = 8 | |
| def get_image(self, image, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16): | |
| torch.cuda.empty_cache() | |
| print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) | |
| start = time.time() | |
| gpu_id=0 | |
| if steps > 60: | |
| steps = 60 | |
| model = self.model_list[gpu_id] | |
| model = model.cuda() | |
| batch_size=1 | |
| channels = model.model.diffusion_model.in_channels | |
| frames = model.temporal_length | |
| h, w = 320 // 8, 512 // 8 | |
| noise_shape = [batch_size, channels, frames, h, w] | |
| #prompts = batch_size * [""] | |
| text_emb = model.get_learned_conditioning([prompt]) | |
| # cond_images = load_image_batch([image_path]) | |
| img_tensor = torch.from_numpy(image).permute(2, 0, 1).float() | |
| img_tensor = (img_tensor / 255. - 0.5) * 2 | |
| img_tensor = img_tensor.unsqueeze(0) | |
| cond_images = img_tensor.to(model.device) | |
| img_emb = model.get_image_embeds(cond_images) | |
| imtext_cond = torch.cat([text_emb, img_emb], dim=1) | |
| cond = {"c_crossattn": [imtext_cond], "fps": fps} | |
| ## 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 | |
| prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt | |
| prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str | |
| prompt_str=prompt_str[:30] | |
| save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) | |
| print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") | |
| model = model.cpu() | |
| return os.path.join(self.result_dir, f"{prompt_str}.mp4") | |
| def download_model(self): | |
| REPO_ID = 'VideoCrafter/Image2Video-512' | |
| filename_list = ['model.ckpt'] | |
| if not os.path.exists('./checkpoints/i2v_512_v1/'): | |
| os.makedirs('./checkpoints/i2v_512_v1/') | |
| for filename in filename_list: | |
| local_file = os.path.join('./checkpoints/i2v_512_v1/', filename) | |
| if not os.path.exists(local_file): | |
| hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/i2v_512_v1/', local_dir_use_symlinks=False) | |
| if __name__ == '__main__': | |
| i2v = Image2Video() | |
| video_path = i2v.get_image('prompts/i2v_prompts/horse.png','horses are walking on the grassland') | |
| print('done', video_path) |