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@@ -34,3 +34,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+xingqiu_images/layer_024.png filter=lfs diff=lfs merge=lfs -text
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+xingqiu_images/layer_064.png filter=lfs diff=lfs merge=lfs -text
+xingqiu_images/layer_074.png filter=lfs diff=lfs merge=lfs -text
+xingqiu_images/layer_084.png filter=lfs diff=lfs merge=lfs -text
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run_videos.py

@@ -0,0 +1,170 @@
+import os
+import time
+from omegaconf import OmegaConf
+import torch
+from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z
+from utils.utils import instantiate_from_config
+from huggingface_hub import hf_hub_download
+from einops import repeat
+import torchvision.transforms as transforms
+from pytorch_lightning import seed_everything
+from einops import rearrange
+import argparse
+import glob
+from PIL import Image
+import numpy as np
+from moviepy.editor import VideoFileClip, concatenate_videoclips
+
+class Image2Video():
+    def __init__(self, result_dir='./tmp/', gpu_num=1, resolution='256_256') -> None:
+        self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw
+        self.download_model()
+        
+        self.result_dir = result_dir
+        if not os.path.exists(self.result_dir):
+            os.mkdir(self.result_dir)
+        ckpt_path='checkpoints/tooncrafter_'+resolution.split('_')[1]+'_interp_v1/model.ckpt'
+        config_file='configs/inference_'+resolution.split('_')[1]+'_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)
+            print(ckpt_path)
+            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=7.5, eta=1.0, fs=3, seed=123, image2=None):
+        img_name = ""
+        if type(image) == type(""):
+            img_name = os.path.basename(image).split('.')[0]
+            image = np.asarray(Image.open(image))
+        if type(image2) == type(""):
+            image2 = np.asarray(Image.open(image2))
+        
+        seed_everything(seed)
+        transform = transforms.Compose([
+            transforms.Resize(min(self.resolution)),
+            transforms.CenterCrop(self.resolution),
+            ])
+        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.half().cuda()
+        batch_size=1
+        channels = model.model.diffusion_model.out_channels
+        frames = model.temporal_length
+        h, w = self.resolution[0] // 8, self.resolution[1] // 8
+        noise_shape = [batch_size, channels, frames, h, w]
+
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            text_emb = model.get_learned_conditioning([prompt])
+            img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().half().to(model.device)
+            img_tensor = (img_tensor / 255. - 0.5) * 2
+            image_tensor_resized = transform(img_tensor) #3,h,w
+            videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
+            videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
+            
+            if image2 is not None:
+                img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().half().to(model.device)
+                img_tensor2 = (img_tensor2 / 255. - 0.5) * 2
+                image_tensor_resized2 = transform(img_tensor2) #3,h,w
+                videos2 = image_tensor_resized2.unsqueeze(0).unsqueeze(2) # bchw
+                videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
+                videos = torch.cat([videos, videos2], dim=2)
+            
+            z, hs = self.get_latent_z_with_hidden_states(model, videos)
+            img_tensor_repeat = torch.zeros_like(z)
+            img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:]
+            img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:]
+
+            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]}
+            
+            batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs)
+            if image2 is None:
+                batch_samples = batch_samples[:,:,:,:-1,...]
+            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[:40]
+            if len(prompt_str) == 0:
+                prompt_str = 'empty_prompt'
+
+        # 使用 img_path 的名称来命名视频
+        #img_name = os.path.basename(image).split('.')[0]
+        video_filename = f"{img_name}"
+        save_videos(batch_samples, self.result_dir, filenames=[video_filename], fps=self.save_fps)
+        print(f"Saved in {video_filename}. Time used: {(time.time() - start):.2f} seconds")
+        model = model.cpu()
+        video_filename += ".mp4"
+        return os.path.join(self.result_dir, video_filename)
+    
+    def download_model(self):
+        REPO_ID = 'Doubiiu/ToonCrafter'
+        filename_list = ['model.ckpt']
+        if not os.path.exists('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/'):
+            os.makedirs('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/')
+        for filename in filename_list:
+            local_file = os.path.join('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', filename)
+            if not os.path.exists(local_file):
+                hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', local_dir_use_symlinks=False)
+    
+    def get_latent_z_with_hidden_states(self, model, videos):
+        b, c, t, h, w = videos.shape
+        x = rearrange(videos, 'b c t h w -> (b t) c h w')
+        encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True)
+
+        hidden_states_first_last = []
+        for hid in hidden_states:
+            hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t)
+            hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2)
+            hidden_states_first_last.append(hid_new)
+
+        z = model.get_first_stage_encoding(encoder_posterior).detach()
+        z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
+        return z, hidden_states_first_last
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Image to Video Conversion')
+    parser.add_argument('--image_dir', type=str, required=True, help='Path to the directory containing input images')
+    parser.add_argument('--prompt', type=str, required=True, help='Prompt for the video')
+    parser.add_argument('--steps', type=int, default=50, help='Number of steps')
+    parser.add_argument('--cfg_scale', type=float, default=7.5, help='CFG scale')
+    parser.add_argument('--eta', type=float, default=1.0, help='Eta value')
+    parser.add_argument('--fs', type=int, default=3, help='FS value')
+    parser.add_argument('--seed', type=int, default=123, help='Seed value')
+    args = parser.parse_args()
+
+    i2v = Image2Video("results" ,resolution = "320_512")
+    image_paths = sorted(glob.glob(os.path.join(args.image_dir, '*.png')))
+    
+    video_paths = []
+    for i in range(len(image_paths) - 1):
+        img_path = image_paths[i]
+        img2_path = image_paths[i + 1]
+        video_path = i2v.get_image(img_path, args.prompt, args.steps, args.cfg_scale, args.eta, args.fs, args.seed, img2_path)
+        video_paths.append(video_path)
+        print('done', video_path)
+
+    # 使用第一个图像的名称来命名最终视频
+    first_image_name = os.path.basename(image_paths[0]).split('.')[0]
+    final_video_path = os.path.join(i2v.result_dir, f"{first_image_name}_final.mp4")
+
+    # 顺次连接所有生成的视频
+    clips = [VideoFileClip(vp) for vp in video_paths]
+    final_clip = concatenate_videoclips(clips, method="compose")
+    final_clip.write_videofile(final_video_path, codec="libx264", fps=i2v.save_fps)
+
+    print(f"Final video saved at {final_video_path}")