app_new.py

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)