update

app.py

@@ -7,7 +7,7 @@ import spaces
 # os.system("pip install -v -v -v  'git+https://github.com/facebookresearch/pytorch3d.git@stable'")
 # os.system("cd pytorch3d && pip install -e . && cd ..")
 # os.system("pip install 'git+https://github.com/facebookresearch/pytorch3d.git'")
-
+os.system("mkdir -p checkpoints/ && wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/")
 
 import gradio as gr
 import random

app_new.py

@@ -0,0 +1,145 @@
+import os
+import torch
+import sys
+import gradio as gr
+import random
+from configs.infer_config import get_parser
+from huggingface_hub import hf_hub_download
+sys.path.append('./extern/dust3r')
+from dust3r.inference import inference, load_model
+from omegaconf import OmegaConf
+from pytorch_lightning import seed_everything
+from utils.diffusion_utils import instantiate_from_config,load_model_checkpoint,image_guided_synthesis
+
+
+i2v_examples = [
+    ['test/images/boy.png', 0, 1.0, '0 40', '0 0', '0 0',  50, 123],
+    ['test/images/car.jpeg', 0, 1.0, '0 -35', '0 0', '0 -0.1',  50, 123],
+    ['test/images/fruit.jpg', 0, 1.0, '0 -3 -15 -20 -17 -5 0', '0 -2 -5 -10 -8 -5 0 2 5 3 0', '0 0',  50, 123],
+    ['test/images/room.png', 5, 1.0, '0 3 10 20 17 10 0', '0 -2 -8 -6 0 2 5 3 0', '0 -0.02 -0.09 -0.16 -0.09 0',  50, 123],
+    ['test/images/castle.png', 0, 1.0, '0 30', '0 -1 -5 -4 0 1 5 4 0', '0 -0.2',  50, 123],
+]
+
+max_seed = 2 ** 31
+
+def download_model():
+    REPO_ID = 'Drexubery/ViewCrafter_25'
+    filename_list = ['model.ckpt']
+    for filename in filename_list:
+        local_file = os.path.join('./checkpoints/', filename)
+        if not os.path.exists(local_file):
+            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/', force_download=True)
+    
+download_model()
+
+
+css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px} #random_button {max-width: 100px !important}"""
+parser = get_parser() # infer_config.py
+opts = parser.parse_args() # default device: 'cuda:0'
+opts.save_dir = './'
+os.makedirs(opts.save_dir,exist_ok=True)
+test_tensor = torch.Tensor([0]).cuda()
+opts.device = str(test_tensor.device)
+
+dust3r = load_model(opts.model_path, opts.device)
+config = OmegaConf.load(opts.config)
+model_config = config.pop("model", OmegaConf.create())
+model_config['params']['unet_config']['params']['use_checkpoint'] = False
+model = instantiate_from_config(model_config)
+model = model.to(opts.device)
+model.cond_stage_model.device = opts.device
+model.perframe_ae = opts.perframe_ae
+assert os.path.exists(opts.ckpt_path), "Error: checkpoint Not Found!"
+model = load_model_checkpoint(model, opts.ckpt_path)
+model.eval()
+diffusion = model
+transform = transforms.Compose([
+    transforms.Resize(576),
+    transforms.CenterCrop((576,1024)),
+    ])
+
+def infer(opts,i2v_input_image, i2v_elevation, i2v_center_scale, i2v_d_phi, i2v_d_theta, i2v_d_r, i2v_steps, i2v_seed):
+    elevation = float(i2v_elevation)
+    center_scale = float(i2v_center_scale)
+    ddim_steps = i2v_steps
+    gradio_traj = [float(i) for i in i2v_d_phi.split()],[float(i) for i in i2v_d_theta.split()],[float(i) for i in i2v_d_r.split()]
+    seed_everything(i2v_seed)
+
+    torch.cuda.empty_cache()
+    img_tensor = torch.from_numpy(i2v_input_image).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
+    img_tensor = (img_tensor / 255. - 0.5) * 2
+    image_tensor_resized = transform(img_tensor) #1,3,h,w
+    images = get_input_dict(image_tensor_resized,idx = 0,dtype = torch.float32)
+    images = [images, copy.deepcopy(images)]
+    images[1]['idx'] = 1
+    se_images = images
+    se_img_ori = (image_tensor_resized.squeeze(0).permute(1,2,0) + 1.)/2.
+
+    run_dust3r(input_images=self.images)
+    nvs_single_view(gradio=True)
+
+    traj_dir = os.path.join(self.opts.save_dir, "viz_traj.mp4")
+    gen_dir = os.path.join(self.opts.save_dir, "diffusion0.mp4")
+    return i2v_traj_path,i2v_output_path
+
+with gr.Blocks(analytics_enabled=False, css=css) as viewcrafter_iface:
+    gr.Markdown("<div align='center'> <h1> ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis </span> </h1> \
+                    <h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
+                    <a href='https://scholar.google.com/citations?user=UOE8-qsAAAAJ&hl=zh-CN'>Wangbo Yu</a>, \
+                    <a href='https://doubiiu.github.io/'>Jinbo Xing</a>, <a href=''>Li Yuan</a>, \
+                    <a href='https://wbhu.github.io/'>Wenbo Hu</a>, <a href='https://xiaoyu258.github.io/'>Xiaoyu Li</a>,\
+                    <a href=''>Zhipeng Huang</a>, <a href='https://scholar.google.com/citations?user=qgdesEcAAAAJ&hl=en/'>Xiangjun Gao</a>,\
+                    <a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html/'>Tien-Tsin Wong</a>,\
+                    <a href='https://scholar.google.com/citations?hl=en&user=4oXBp9UAAAAJ&view_op=list_works&sortby=pubdate/'>Ying Shan</a>\
+                    <a href=''>Yonghong Tian</a>\
+                </h2> \
+                    <a style='font-size:18px;color: #FF5DB0' href='https://github.com/Drexubery/ViewCrafter/blob/main/docs/render_help.md'> [Guideline] </a>\
+                    <a style='font-size:18px;color: #000000' href=''> [ArXiv] </a>\
+                    <a style='font-size:18px;color: #000000' href='https://drexubery.github.io/ViewCrafter/'> [Project Page] </a>\
+                    <a style='font-size:18px;color: #000000' href='https://github.com/Drexubery/ViewCrafter'> [Github] </a> </div>") 
+            
+    #######image2video######
+    with gr.Tab(label="ViewCrafter_25, 'single_view_txt' mode"):
+        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_elevation = gr.Slider(minimum=-45, maximum=45, step=1, elem_id="elevation", label="elevation", value=5)
+                    with gr.Row():
+                        i2v_center_scale = gr.Slider(minimum=0.1, maximum=2, step=0.1, elem_id="i2v_center_scale", label="center_scale", value=1)
+                    with gr.Row():
+                        i2v_d_phi = gr.Text(label='d_phi sequence, should start with 0')
+                    with gr.Row():
+                        i2v_d_theta = gr.Text(label='d_theta sequence, should start with 0')
+                    with gr.Row():
+                        i2v_d_r = gr.Text(label='d_r sequence, should start with 0')
+                    with gr.Row():
+                        i2v_steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
+                    with gr.Row():
+                        i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=max_seed, step=1, value=123)
+                    i2v_end_btn = gr.Button("Generate")
+                # with gr.Tab(label='Result'):
+                with gr.Column():
+                    with gr.Row():
+                        i2v_traj_video = gr.Video(label="Camera Trajectory",elem_id="traj_vid",autoplay=True,show_share_button=True)
+                    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=[opts,i2v_input_image, i2v_elevation, i2v_center_scale, i2v_d_phi, i2v_d_theta, i2v_d_r, i2v_steps, i2v_seed],
+                        outputs=[i2v_traj_video,i2v_output_video],
+                        fn = infer,
+                        cache_examples=False,
+            )
+
+        # image2video.run_gradio(i2v_input_image='test/images/boy.png', i2v_elevation='10', i2v_d_phi='0 40', i2v_d_theta='0 0', i2v_d_r='0 0', i2v_center_scale=1, i2v_steps=50, i2v_seed=123)
+        i2v_end_btn.click(inputs=[opts,i2v_input_image, i2v_elevation, i2v_center_scale, i2v_d_phi, i2v_d_theta, i2v_d_r, i2v_steps, i2v_seed],
+                        outputs=[i2v_traj_video,i2v_output_video],
+                        fn = infer
+        )
+
+viewcrafter_iface.queue(max_size=12).launch(show_api=True)
+
+