udpate

app.py

@@ -3,9 +3,6 @@ import torch
 import sys
 import spaces
 
-# os.system('pip install iopath')
-# os.system("pip install -v -v -v 'git+https://github.com/facebookresearch/pytorch3d.git@stable'")
-# os.system("cd pytorch3d && pip install -e . && cd ..")
 
 import gradio as gr
 import random
@@ -37,11 +34,8 @@ opts.save_dir = './'
 os.makedirs(opts.save_dir,exist_ok=True)
 test_tensor = torch.Tensor([0]).cuda()
 opts.device = str(test_tensor.device)
-# os.system('pip install iopath') FORCE_CUDA=1 
-# os.system('curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz')
-# os.system('tar xzf 1.10.0.tar.gz')
-# os.system('export CUB_HOME=$PWD/cub-1.10.0')
-# spaces.GPU(os.system("pip install 'git+https://github.com/facebookresearch/pytorch3d.git'"))
+
+# install pytorch3d
 pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
 version_str="".join([
  f"py3{sys.version_info.minor}_cu",
@@ -51,7 +45,6 @@ version_str="".join([
 print(version_str)
 os.system(f"{sys.executable} -m pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html")
 os.system("mkdir -p checkpoints/ && wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/")
-
 print(f'>>> System info: {version_str}')
 
 
@@ -110,7 +103,7 @@ def viewcrafter_demo(opts):
  inputs=[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 = image2video.run_gradio,
- cache_examples=False,
+ cache_examples='lazy',
  )
 
  # 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)

app_bad.py

@@ -1,114 +0,0 @@
-import os
-import torch
-import sys
-
-# os.system('pip install iopath')
-# 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'")
-
-
-import gradio as gr
-import random
-from viewcrafter import ViewCrafter
-from configs.infer_config import get_parser
-from huggingface_hub import hf_hub_download
-
-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)
- 
- REPO_ID = 'naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt'
-
-download_model()
-
-def viewcrafter_demo(opts):
- css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px} #random_button {max-width: 100px !important}"""
- image2video = ViewCrafter(opts, gradio = True)
- 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=[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 = image2video.run_gradio,
- 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=[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 = image2video.run_gradio
- )
-
- return viewcrafter_iface
-
-
-if __name__ == "__main__":
- 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)
- viewcrafter_iface = viewcrafter_demo(opts)
- viewcrafter_iface.queue(max_size=10)
- viewcrafter_iface.launch()
- # viewcrafter_iface.launch(server_name='127.0.0.1', server_port=80, max_threads=1,debug=False)
-

app_new.py

@@ -1,146 +0,0 @@
-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
-import torchvision.transforms as transforms
-import copy
-
-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)
-
-

pytorch3d

@@ -1 +0,0 @@
-Subproject commit 05cbea115acbbcbea77999c03d55155b23479991