Update app.py

app.py

@@ -1,155 +1,156 @@
-import gradio as gr
-import os
-import sys
-import argparse
-import random
-import time
-from omegaconf import OmegaConf
-import torch
-import torchvision
-from pytorch_lightning import seed_everything
-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,
-    load_model_checkpoint,
-    get_latent_z,
-    save_videos
-)
-
-def download_model():
-    REPO_ID = 'Doubiiu/DynamiCrafter'
-    filename_list = ['model.ckpt']
-    if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'):
-        os.makedirs('./checkpoints/dynamicrafter_256_v1/')
-    for filename in filename_list:
-        local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename)
-        if not os.path.exists(local_file):
-            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', force_download=True)
-    
-
-def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
-    download_model()
-    ckpt_path='checkpoints/dynamicrafter_256_v1/model.ckpt'
-    config_file='configs/inference_256_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 = instantiate_from_config(model_config)
-    assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
-    model = load_model_checkpoint(model, ckpt_path)
-    model.eval()
-    model = model.cuda()
-    save_fps = 8
-
-    seed_everything(seed)
-    transform = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(256),
-        ])
-    torch.cuda.empty_cache()
-    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
-    channels = model.model.diffusion_model.out_channels
-    frames = model.temporal_length
-    h, w = 256 // 8, 256 // 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) #3,256,256
-    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
-
-    video_path = './output.mp4'
-    save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
-    model = model.cpu()
-    return video_path
-
-
-
-
-
-
-i2v_examples = [
-    ['prompts/art.png', 'man fishing in a boat at sunset', 50, 7.5, 1.0, 3, 234],
-    ['prompts/boy.png', 'boy walking on the street', 50, 7.5, 1.0, 3, 125],
-    ['prompts/dance1.jpeg', 'two people dancing', 50, 7.5, 1.0, 3, 116],
-    ['prompts/fire_and_beach.jpg', 'a campfire on the beach and the ocean waves in the background', 50, 7.5, 1.0, 3, 111],
-    ['prompts/girl3.jpeg', 'girl talking and blinking', 50, 7.5, 1.0, 3, 111],
-    ['prompts/guitar0.jpeg', 'bear playing guitar happily, snowing', 50, 7.5, 1.0, 3, 111],
-]
-css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
-
-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>")
-    
-    with gr.Tab(label='ImageAnimation'):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        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():
-                        i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
-                        i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
-                        i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
-                    with gr.Row():
-                        i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
-                        i2v_motion = gr.Slider(minimum=1, maximum=4, step=1, elem_id="i2v_motion", label="Motion magnitude", value=3)
-                    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)
-
-            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
-        )
-
+import gradio as gr
+import os
+import sys
+import argparse
+import random
+import time
+from omegaconf import OmegaConf
+import torch
+import torchvision
+from pytorch_lightning import seed_everything
+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,
+    load_model_checkpoint,
+    get_latent_z,
+    save_videos
+)
+
+def download_model():
+    REPO_ID = 'Doubiiu/DynamiCrafter'
+    filename_list = ['model.ckpt']
+    if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'):
+        os.makedirs('./checkpoints/dynamicrafter_256_v1/')
+    for filename in filename_list:
+        local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename)
+        if not os.path.exists(local_file):
+            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', force_download=True)
+    
+
+def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
+    download_model()
+    ckpt_path='checkpoints/dynamicrafter_256_v1/model.ckpt'
+    config_file='configs/inference_256_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 = instantiate_from_config(model_config)
+    assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
+    model = load_model_checkpoint(model, ckpt_path)
+    model.eval()
+    model = model.cuda()
+    save_fps = 8
+
+    seed_everything(seed)
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(256),
+        ])
+    torch.cuda.empty_cache()
+    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
+    channels = model.model.diffusion_model.out_channels
+    frames = model.temporal_length
+    h, w = 256 // 8, 256 // 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) #3,256,256
+    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
+
+    video_path = './output.mp4'
+    save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
+    model = model.cpu()
+    return video_path
+
+
+
+
+
+
+i2v_examples = [
+    ['prompts/art.png', 'man fishing in a boat at sunset', 50, 7.5, 1.0, 3, 234],
+    ['prompts/boy.png', 'boy walking on the street', 50, 7.5, 1.0, 3, 125],
+    ['prompts/dance1.jpeg', 'two people dancing', 50, 7.5, 1.0, 3, 116],
+    ['prompts/fire_and_beach.jpg', 'a campfire on the beach and the ocean waves in the background', 50, 7.5, 1.0, 3, 111],
+    ['prompts/girl3.jpeg', 'girl talking and blinking', 50, 7.5, 1.0, 3, 111],
+    ['prompts/guitar0.jpeg', 'bear playing guitar happily, snowing', 50, 7.5, 1.0, 3, 122],
+    ['prompts/surf.png', 'a man is surfing', 50, 7.5, 1.0, 3, 123],
+]
+css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
+
+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>")
+    
+    with gr.Tab(label='ImageAnimation'):
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        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():
+                        i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
+                        i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
+                        i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
+                    with gr.Row():
+                        i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
+                        i2v_motion = gr.Slider(minimum=1, maximum=4, step=1, elem_id="i2v_motion", label="Motion magnitude", value=3)
+                    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)
+
+            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
+        )
+
 dynamicrafter_iface.queue(max_size=12).launch(show_api=True)