Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import os
 import sys
-import argparse
 import random
 import time
 from omegaconf import OmegaConf
@@ -12,6 +11,7 @@ from huggingface_hub import hf_hub_download
 from einops import repeat
 import torchvision.transforms as transforms
 from utils.utils import instantiate_from_config
+
 sys.path.insert(0, "scripts/evaluation")
 from funcs import (
     batch_ddim_sampling,
@@ -23,13 +23,11 @@ from funcs import (
 def download_model():
     REPO_ID = 'Doubiiu/DynamiCrafter_512'
     filename_list = ['model.ckpt']
-    if not os.path.exists('./checkpoints/dynamicrafter_512_v1/'):
-        os.makedirs('./checkpoints/dynamicrafter_512_v1/')
+    os.makedirs('./checkpoints/dynamicrafter_512_v1/', exist_ok=True)
     for filename in filename_list:
         local_file = os.path.join('./checkpoints/dynamicrafter_512_v1/', filename)
         if not os.path.exists(local_file):
             hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_512_v1/', force_download=True)
-    
 
 def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     resolution = (320, 512)
@@ -38,7 +36,7 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     config_file='configs/inference_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_config['params']['unet_config']['params']['use_checkpoint'] = False   
     model = instantiate_from_config(model_config)
     assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
     model = load_model_checkpoint(model, ckpt_path)
@@ -50,14 +48,14 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     transform = transforms.Compose([
         transforms.Resize(min(resolution)),
         transforms.CenterCrop(resolution),
-        ])
+    ])
     torch.cuda.empty_cache()
-    print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
+    print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
     start = time.time()
     if steps > 60:
         steps = 60 
 
-    batch_size=1
+    batch_size = 1
     channels = model.model.diffusion_model.out_channels
     frames = model.temporal_length
     h, w = resolution[0] // 8, resolution[1] // 8
@@ -70,14 +68,14 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     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) #3,256,256
-    videos = image_tensor_resized.unsqueeze(0) # bchw
+    image_tensor_resized = transform(img_tensor) # 3, 256, 256
+    videos = image_tensor_resized.unsqueeze(0) # b, c, h, w
     
-    z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
+    z = get_latent_z(model, videos.unsqueeze(2)) # b, c, 1, h, w
     
     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
+    cond_images = model.embedder(img_tensor.unsqueeze(0)) # b, l, c
     img_emb = model.image_proj_model(cond_images)
 
     imtext_cond = torch.cat([text_emb, img_emb], dim=1)
@@ -85,16 +83,15 @@ def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
     fs = torch.tensor([fs], dtype=torch.long, device=model.device)
     cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
     
-    ## inference
+    # 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
+    # b, samples, c, t, h, w
 
     video_path = './output.mp4'
     save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
     model = model.cpu()
     return video_path
 
-
 i2v_examples = [
     ['prompts/512/bloom01.png', 'time-lapse of a blooming flower with leaves and a stem', 50, 7.5, 1.0, 24, 123],
     ['prompts/512/campfire.png', 'a bonfire is lit in the middle of a field', 50, 7.5, 1.0, 24, 123],
@@ -104,31 +101,32 @@ i2v_examples = [
     ['prompts/512/zreal_penguin.png', 'a group of penguins walking on a beach', 50, 7.5, 1.0, 20, 123],
 ]
 
-
-
-
 css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}"""
 
 with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
-    gr.Markdown("<div align='center'> <h1> DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors </span> </h1> \
-                    <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
-                    <a href='https://doubiiu.github.io/'>Jinbo Xing</a>, \
-                    <a href='https://menghanxia.github.io/'>Menghan Xia</a>, <a href='https://yzhang2016.github.io/'>Yong Zhang</a>, \
-                    <a href=''>Haoxin Chen</a>, <a href=''> Wangbo Yu</a>,\
-                    <a href='https://github.com/hyliu'>Hanyuan Liu</a>, <a href='https://xinntao.github.io/'>Xintao Wang</a>,\
-                    <a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html'>Tien-Tsin Wong</a>,\
-                    <a href='https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=zh-CN'>Ying Shan</a>\
-                </h2> \
-                    <a style='font-size:18px;color: #000000' href='https://arxiv.org/abs/2310.12190'> [ArXiv] </a>\
-                    <a style='font-size:18px;color: #000000' href='https://doubiiu.github.io/projects/DynamiCrafter/'> [Project Page] </a> \
-                    <a style='font-size:18px;color: #000000' href='https://github.com/Doubiiu/DynamiCrafter'> [Github] </a> </div>")
-    
+    gr.Markdown("""
+    <div align='center'> 
+        <h1> DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors </h1>
+        <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>
+            <a href='https://doubiiu.github.io/'>Jinbo Xing</a>, 
+            <a href='https://menghanxia.github.io/'>Menghan Xia</a>, <a href='https://yzhang2016.github.io/'>Yong Zhang</a>, 
+            <a href=''>Haoxin Chen</a>, <a href=''> Wangbo Yu</a>,
+            <a href='https://github.com/hyliu'>Hanyuan Liu</a>, <a href='https://xinntao.github.io/'>Xintao Wang</a>,
+            <a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html'>Tien-Tsin Wong</a>,
+            <a href='https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=zh-CN'>Ying Shan</a>
+        </h2>
+        <a style='font-size:18px;color: #000000' href='https://arxiv.org/abs/2310.12190'> [ArXiv] </a>
+        <a style='font-size:18px;color: #000000' href='https://doubiiu.github.io/projects/DynamiCrafter/'> [Project Page] </a>
+        <a style='font-size:18px;color: #000000' href='https://github.com/Doubiiu/DynamiCrafter'> [Github] </a>
+    </div>
+    """)
+
     with gr.Tab(label='ImageAnimation_320x512'):
         with gr.Column():
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
-                        i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
+                        i2v_input_image = gr.Image(label="Input Image", elem_id="input_img")
                     with gr.Row():
                         i2v_input_text = gr.Text(label='Prompts')
                     with gr.Row():
@@ -139,18 +137,19 @@ with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
                         i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
                         i2v_motion = gr.Slider(minimum=15, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=24)
                     i2v_end_btn = gr.Button("Generate")
-                # with gr.Tab(label='Result'):
                 with gr.Row():
-                    i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
+                    i2v_output_video = gr.Video(label="Generated Video", elem_id="output_vid", autoplay=True, show_share_button=True)
 
-            gr.Examples(examples=i2v_examples,
-                        inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
-                        outputs=[i2v_output_video],
-                        fn = infer,
+            gr.Examples(
+                examples=i2v_examples,
+                inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
+                outputs=[i2v_output_video],
+                fn=infer,
             )
-        i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
-                        outputs=[i2v_output_video],
-                        fn = infer
+        i2v_end_btn.click(
+            inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
+            outputs=[i2v_output_video],
+            fn=infer
         )
 
-dynamicrafter_iface.queue(max_size=12).launch(share=True, show_api=True)
+dynamicrafter_iface.queue(max_size=12).launch(share=True, show_api=True)