Update scripts/gradio/i2v_test_application.py

scripts/gradio/i2v_test_application.py

@@ -1,229 +1,229 @@
-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
-from cldm.model import load_state_dict
-import cv2
-
-
-def extract_frames(video_path):
-    # 動画ファイルを読み込む
-    cap = cv2.VideoCapture(video_path)
-    
-    frame_list = []
-    frame_num = 0
-    
-    while True:
-        # フレームを読み込む
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        # フレームをリストに追加
-        frame_list.append(frame)
-        frame_num += 1
-    
-    # 動画ファイルを閉じる
-    cap.release()
-    
-    return frame_list
-
-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)
-        
-        #ToonCrafterModel
-        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  
-
-        #ControlModel
-        cn_ckpt_path = "control_models/sketch_encoder.ckpt"
-        cn_config_file = 'configs/cldm_v21.yaml'
-        cn_config = OmegaConf.load(cn_config_file)
-        cn_model_config = cn_config.pop("control_stage_config", OmegaConf.create())
-
- 
-        model_list = []
-        for gpu_id in range(gpu_num):
-            model = instantiate_from_config(model_config)
-            cn_model = instantiate_from_config(cn_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()
-
-            cn_model.load_state_dict(load_state_dict(cn_ckpt_path, location='cuda'))  
-            cn_model.eval()
-
-            model.control_model = cn_model
-            
-            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, frame_guides=None,control_scale=0.6):
-        control_frames = extract_frames(frame_guides)
-        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.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]
-
-        # text cond
-        with torch.no_grad(), torch.cuda.amp.autocast():
-            text_emb = model.get_learned_conditioning([prompt])
-
-            #control cond
-            if frame_guides is not None:
-                cn_videos = []
-                for frame in control_frames:
-                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-                    frame = cv2.bitwise_not(frame)
-                    cn_tensor = torch.from_numpy(frame).unsqueeze(2).permute(2, 0, 1).float().to(model.device)
-                    
-                    #cn_tensor = (cn_tensor / 255. - 0.5) * 2
-                    cn_tensor = ( cn_tensor/255.0 )
-                    cn_tensor_resized = transform(cn_tensor) #3,h,w
-
-                    cn_video = cn_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
-                    cn_videos.append(cn_video)
-                
-                cn_videos = torch.cat(cn_videos, dim=2)
-                model_list = []
-                for model in self.model_list:
-                    model.control_scale = control_scale
-                    model_list.append(model)
-                self.model_list = model_list
-                
-            else:
-                cn_videos = None
-
-
-
-            # 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,h,w
-            videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
-            
-            # z = get_latent_z(model, videos) #bc,1,hw
-            videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
-
-            img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().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], "control_cond": cn_videos}
-            
-            ## inference
-            batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs)
-
-            ## remove the last frame
-            if image2 is None:
-                batch_samples = batch_samples[:,:,:,:-1,...]
-            ## 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[:40]
-            if len(prompt_str) == 0:
-                prompt_str = 'empty_prompt'
-
-        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()
-        result_dir = os.path.join("/group/40034/gzhiwang/ToonCrafter_with_SketchGuidance", f"{prompt_str}.mp4")
-        print("result saved to:", result_dir)
-        return result_dir
-
-        # import torchvision
-        # batch_tensors = batch_samples
-        # n_samples = batch_tensors.shape[1]
-        # for idx, vid_tensor in enumerate(batch_tensors):
-        #     video = vid_tensor.detach().cpu()
-        #     video = torch.clamp(video.float(), -1., 1.)
-        #     video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
-        #     frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w]
-        #     grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
-        #     grid = (grid + 1.0) / 2.0
-        #     grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
-        #     # savepath = os.path.join(savedir, f"{filenames[idx]}.mp4")
-        #     # torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'})
-        #     return grid
-
-    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 = []
-        ### use only the first and last hidden states
-        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__':
-    i2v = Image2Video()
-    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
+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
+from cldm.model import load_state_dict
+import cv2
+
+
+def extract_frames(video_path):
+    # 動画ファイルを読み込む
+    cap = cv2.VideoCapture(video_path)
+    
+    frame_list = []
+    frame_num = 0
+    
+    while True:
+        # フレームを読み込む
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # フレームをリストに追加
+        frame_list.append(frame)
+        frame_num += 1
+    
+    # 動画ファイルを閉じる
+    cap.release()
+    
+    return frame_list
+
+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)
+        
+        #ToonCrafterModel
+        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  
+
+        #ControlModel
+        cn_ckpt_path = "control_models/sketch_encoder.ckpt"
+        cn_config_file = 'configs/cldm_v21.yaml'
+        cn_config = OmegaConf.load(cn_config_file)
+        cn_model_config = cn_config.pop("control_stage_config", OmegaConf.create())
+
+ 
+        model_list = []
+        for gpu_id in range(gpu_num):
+            model = instantiate_from_config(model_config)
+            cn_model = instantiate_from_config(cn_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()
+
+            cn_model.load_state_dict(load_state_dict(cn_ckpt_path, location='cpu'))  
+            cn_model.eval()
+
+            model.control_model = cn_model
+            
+            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, frame_guides=None,control_scale=0.6):
+        control_frames = extract_frames(frame_guides)
+        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.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]
+
+        # text cond
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            text_emb = model.get_learned_conditioning([prompt])
+
+            #control cond
+            if frame_guides is not None:
+                cn_videos = []
+                for frame in control_frames:
+                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                    frame = cv2.bitwise_not(frame)
+                    cn_tensor = torch.from_numpy(frame).unsqueeze(2).permute(2, 0, 1).float().to(model.device)
+                    
+                    #cn_tensor = (cn_tensor / 255. - 0.5) * 2
+                    cn_tensor = ( cn_tensor/255.0 )
+                    cn_tensor_resized = transform(cn_tensor) #3,h,w
+
+                    cn_video = cn_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
+                    cn_videos.append(cn_video)
+                
+                cn_videos = torch.cat(cn_videos, dim=2)
+                model_list = []
+                for model in self.model_list:
+                    model.control_scale = control_scale
+                    model_list.append(model)
+                self.model_list = model_list
+                
+            else:
+                cn_videos = None
+
+
+
+            # 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,h,w
+            videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw
+            
+            # z = get_latent_z(model, videos) #bc,1,hw
+            videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
+
+            img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().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], "control_cond": cn_videos}
+            
+            ## inference
+            batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs)
+
+            ## remove the last frame
+            if image2 is None:
+                batch_samples = batch_samples[:,:,:,:-1,...]
+            ## 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[:40]
+            if len(prompt_str) == 0:
+                prompt_str = 'empty_prompt'
+
+        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()
+        result_dir = os.path.join("/group/40034/gzhiwang/ToonCrafter_with_SketchGuidance", f"{prompt_str}.mp4")
+        print("result saved to:", result_dir)
+        return result_dir
+
+        # import torchvision
+        # batch_tensors = batch_samples
+        # n_samples = batch_tensors.shape[1]
+        # for idx, vid_tensor in enumerate(batch_tensors):
+        #     video = vid_tensor.detach().cpu()
+        #     video = torch.clamp(video.float(), -1., 1.)
+        #     video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
+        #     frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w]
+        #     grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
+        #     grid = (grid + 1.0) / 2.0
+        #     grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+        #     # savepath = os.path.join(savedir, f"{filenames[idx]}.mp4")
+        #     # torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'})
+        #     return grid
+
+    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 = []
+        ### use only the first and last hidden states
+        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__':
+    i2v = Image2Video()
+    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
     print('done', video_path)