add_loop

prompts/512_loop/test_prompts.txt

+a beach with waves and clouds at sunset
\ No newline at end of file

scripts/gradio/i2v_test.py

-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
-
-
-class Image2Video():
-    def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256', **kwargs) -> 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/dynamicrafter_'+resolution.split('_')[1]+'_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)
-            # 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
-        
-        self.kwargs = kwargs
-
-    def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
-        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
-        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,h,w
-        videos = image_tensor_resized.unsqueeze(0) # bchw
-        
-        z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
-        
-        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)) ## 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]}
-        
-        ## inference
-        with torch.no_grad(), torch.cuda.amp.autocast():
-            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[: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()
-        return os.path.join(self.result_dir, f"{prompt_str}.mp4")
-    
-    def download_model(self):
-        REPO_ID = 'Doubiiu/DynamiCrafter_'+str(self.resolution[1]) if self.resolution[1]!=256 else 'Doubiiu/DynamiCrafter'
-        filename_list = ['model.ckpt']
-        if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/'):
-            os.makedirs('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/')
-        for filename in filename_list:
-            local_file = os.path.join('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', filename)
-            if not os.path.exists(local_file):
-                hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', local_dir_use_symlinks=False)
-    
-if __name__ == '__main__':
-    i2v = Image2Video()
-    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
-    print('done', video_path)
+mport 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
+
+
+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/dynamicrafter_'+resolution.split('_')[1]+'_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)
+            # 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=7.5, eta=1.0, fs=3, seed=123):
+        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])
+
+            # 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) # bchw
+            
+            z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+            
+            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)) ## 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]}
+            
+            ## 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[: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()
+        return os.path.join(self.result_dir, f"{prompt_str}.mp4")
+    
+    def download_model(self):
+        REPO_ID = 'Doubiiu/DynamiCrafter_'+str(self.resolution[1]) if self.resolution[1]!=256 else 'Doubiiu/DynamiCrafter'
+        filename_list = ['model.ckpt']
+        if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/'):
+            os.makedirs('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/')
+        for filename in filename_list:
+            local_file = os.path.join('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', filename)
+            if not os.path.exists(local_file):
+                hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', local_dir_use_symlinks=False)
+    
+if __name__ == '__main__':
+    i2v = Image2Video()
+    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
+    print('done', video_path)
\ No newline at end of file

scripts/gradio/i2v_test_application.py

+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
+
+
+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/dynamicrafter_'+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)
+            # 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=7.5, eta=1.0, fs=3, seed=123, image2=None):
+        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])
+
+            # 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) # bchw
+            
+            z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+            
+
+            if image2 is not None:
+                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) # bchw
+                
+                z2 = get_latent_z(model, videos2.unsqueeze(2)) #bc,1,hw
+
+            img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
+
+            img_tensor_repeat = torch.zeros_like(img_tensor_repeat)
+
+            ## old
+            img_tensor_repeat[:,:,:1,:,:] = z
+            if image2 is not None:
+                img_tensor_repeat[:,:,-1:,:,:] = z2
+            else:
+                img_tensor_repeat[:,:,-1:,:,:] = z
+
+
+            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]}
+            
+            ## inference
+            batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
+
+            ## 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()
+        return os.path.join(self.result_dir, f"{prompt_str}.mp4")
+    
+    def download_model(self):
+        REPO_ID = 'Doubiiu/DynamiCrafter_'+str(self.resolution[1])+'_Interp'
+        filename_list = ['model.ckpt']
+        if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_interp_v1/'):
+            os.makedirs('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_interp_v1/')
+        for filename in filename_list:
+            local_file = os.path.join('./checkpoints/dynamicrafter_'+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/dynamicrafter_'+str(self.resolution[1])+'_interp_v1/', local_dir_use_symlinks=False)
+    
+if __name__ == '__main__':
+    i2v = Image2Video()
+    video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset')
+    print('done', video_path)

scripts/run_application.sh

+version=$1 # interp or loop
+ckpt=checkpoints/dynamicrafter_512_interp_v1/model.ckpt
+config=configs/inference_512_v1.0.yaml
+
+prompt_dir=prompts/512_$1/
+res_dir="results"
+
+FS=5 ## This model adopts FPS=5, range recommended: 5-30 (smaller value -> larger motion)
+
+
+if [ "$1" == "interp" ]; then
+seed=12306
+name=dynamicrafter_512_$1_seed${seed}
+CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/inference.py \
+--seed ${seed} \
+--ckpt_path $ckpt \
+--config $config \
+--savedir $res_dir/$name \
+--n_samples 1 \
+--bs 1 --height 320 --width 512 \
+--unconditional_guidance_scale 7.5 \
+--ddim_steps 50 \
+--ddim_eta 1.0 \
+--prompt_dir $prompt_dir \
+--text_input \
+--video_length 16 \
+--frame_stride ${FS} \
+--timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 --perframe_ae --interp
+else
+seed=234
+name=dynamicrafter_512_$1_seed${seed}
+CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/inference.py \
+--seed ${seed} \
+--ckpt_path $ckpt \
+--config $config \
+--savedir $res_dir/$name \
+--n_samples 1 \
+--bs 1 --height 320 --width 512 \
+--unconditional_guidance_scale 7.5 \
+--ddim_steps 50 \
+--ddim_eta 1.0 \
+--prompt_dir $prompt_dir \
+--text_input \
+--video_length 16 \
+--frame_stride ${FS} \
+--timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 --perframe_ae --loop
+fi

scripts/run_mp.sh

@@ -63,8 +63,8 @@ fi
 
 ## inference using single node with multi-GPUs:
 if [ "$1" == "256" ]; then
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
---nproc_per_node=8 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \
+CUDA_VISIBLE_DEVICES=0,1 python3 -m torch.distributed.launch \
+--nproc_per_node=2 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \
 scripts/evaluation/ddp_wrapper.py \
 --module 'inference' \
 --seed ${seed} \
@@ -81,8 +81,8 @@ scripts/evaluation/ddp_wrapper.py \
 --video_length 16 \
 --frame_stride ${FS}
 else
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
---nproc_per_node=8 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \
+CUDA_VISIBLE_DEVICES=0,1 python3 -m torch.distributed.launch \
+--nproc_per_node=2 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \
 scripts/evaluation/ddp_wrapper.py \
 --module 'inference' \
 --seed ${seed} \