Delete app.py

app.py

@@ -1,341 +0,0 @@
-import os
-import imageio
-import numpy as np
-import torch
-import rembg
-from PIL import Image
-from torchvision.transforms import v2
-from pytorch_lightning import seed_everything
-from omegaconf import OmegaConf
-from einops import rearrange, repeat
-from tqdm import tqdm
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-
-from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    get_zero123plus_input_cameras,
-    get_circular_camera_poses,
-)
-from src.utils.mesh_util import save_obj
-from src.utils.infer_util import remove_background, resize_foreground, images_to_video
-
-import tempfile
-from functools import partial
-
-from huggingface_hub import hf_hub_download
-
-import gradio as gr
-import spaces
-
-
-def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
-    """
-    Get the rendering camera parameters.
-    """
-    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
-    if is_flexicubes:
-        cameras = torch.linalg.inv(c2ws)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
-    else:
-        extrinsics = c2ws.flatten(-2)
-        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
-        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
-    return cameras
-
-
-def images_to_video(images, output_path, fps=30):
-    # images: (N, C, H, W)
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    frames = []
-    for i in range(images.shape[0]):
-        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
-        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
-            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
-        assert frame.min() >= 0 and frame.max() <= 255, \
-            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
-        frames.append(frame)
-    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
-
-
-###############################################################################
-# Configuration.
-###############################################################################
-
-config_path = 'configs/instant-mesh-large.yaml'
-config = OmegaConf.load(config_path)
-config_name = os.path.basename(config_path).replace('.yaml', '')
-model_config = config.model_config
-infer_config = config.infer_config
-
-IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
-
-device = torch.device('cuda')
-
-# load diffusion model
-print('Loading diffusion model ...')
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="zero123plus",
-    torch_dtype=torch.float16,
-)
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
-
-# load custom white-background UNet
-unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
-state_dict = torch.load(unet_ckpt_path, map_location='cpu')
-pipeline.unet.load_state_dict(state_dict, strict=True)
-
-pipeline = pipeline.to(device)
-
-# load reconstruction model
-print('Loading reconstruction model ...')
-model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
-model = instantiate_from_config(model_config)
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
-model.load_state_dict(state_dict, strict=True)
-
-model = model.to(device)
-if IS_FLEXICUBES:
-    model.init_flexicubes_geometry(device)
-model = model.eval()
-
-print('Loading Finished!')
-
-
-def check_input_image(input_image):
-    if input_image is None:
-        raise gr.Error("No image uploaded!")
-
-
-def preprocess(input_image, do_remove_background):
-
-    rembg_session = rembg.new_session() if do_remove_background else None
-
-    if do_remove_background:
-        input_image = remove_background(input_image, rembg_session)
-        input_image = resize_foreground(input_image, 0.85)
-
-    return input_image
-
-
-def generate_mvs(input_image, sample_steps, sample_seed):
-
-    seed_everything(sample_seed)
-    
-    # sampling
-    z123_image = pipeline(
-        input_image, 
-        num_inference_steps=sample_steps
-    ).images[0]
-
-    show_image = np.asarray(z123_image, dtype=np.uint8)
-    show_image = torch.from_numpy(show_image)     # (960, 640, 3)
-    show_image = rearrange(show_image, '(n h) (m w) c -> (m h) (n w) c', n=3, m=2)
-    show_image = Image.fromarray(show_image.numpy())
-
-    return z123_image, show_image
-
-def make_mesh(mesh_fpath, planes):
-
-    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
-    mesh_dirname = os.path.dirname(mesh_fpath)
-    mesh_vis_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
-        
-    with torch.no_grad():
-
-        # get mesh
-        mesh_out = model.extract_mesh(
-            planes,
-            use_texture_map=False,
-            **infer_config,
-        )
-
-        vertices, faces, vertex_colors = mesh_out
-        vertices = vertices[:, [0, 2, 1]]
-        vertices[:, -1] *= -1
-
-        save_obj(vertices, faces, vertex_colors, mesh_fpath)
-        
-        print(f"Mesh saved to {mesh_fpath}")
-
-    return mesh_fpath
-
-@spaces.GPU
-def make3d(input_image, sample_steps, sample_seed):
-
-    images, show_images = generate_mvs(input_image, sample_steps, sample_seed)
-
-    images = np.asarray(images, dtype=np.float32) / 255.0
-    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
-    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)
-
-    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=2.5).to(device)
-    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
-
-    images = images.unsqueeze(0).to(device)
-    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
-
-    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    print(mesh_fpath)
-    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
-    mesh_dirname = os.path.dirname(mesh_fpath)
-    video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
-
-    with torch.no_grad():
-        # get triplane
-        planes = model.forward_planes(images, input_cameras)
-
-        # get video
-        chunk_size = 20 if IS_FLEXICUBES else 1
-        render_size = 384
-        
-        frames = []
-        for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
-            if IS_FLEXICUBES:
-                frame = model.forward_geometry(
-                    planes,
-                    render_cameras[:, i:i+chunk_size],
-                    render_size=render_size,
-                )['img']
-            else:
-                frame = model.synthesizer(
-                    planes,
-                    cameras=render_cameras[:, i:i+chunk_size],
-                    render_size=render_size,
-                )['images_rgb']
-            frames.append(frame)
-        frames = torch.cat(frames, dim=1)
-
-        images_to_video(
-            frames[0],
-            video_fpath,
-            fps=30,
-        )
-
-        print(f"Video saved to {video_fpath}")
-
-    mesh_fpath = make_mesh(mesh_fpath, planes)
-
-    return video_fpath, mesh_fpath, show_images
-
-
-_HEADER_ = '''
-<h2><b>Official 🤗 Gradio demo for</b>
-<a href='https://github.com/TencentARC/InstantMesh' target='_blank'>
-<b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b>
-</a>.
-</h2>
-'''
-
-_LINKS_ = '''
-<h3>Code is available at <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a></h3>
-<h3>Report is available at <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a></h3>
-'''
-
-_CITE_ = r"""
-```bibtex
-@article{xu2024instantmesh,
-  title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
-  author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
-  journal={arXiv preprint arXiv:2404.07191},
-  year={2024}
-}
-```
-"""
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(_HEADER_)
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                input_image = gr.Image(
-                    label="Input Image",
-                    image_mode="RGBA",
-                    sources="upload",
-                    width=256,
-                    height=256,
-                    type="pil",
-                    elem_id="content_image",
-                )
-                processed_image = gr.Image(
-                    label="Processed Image", 
-                    image_mode="RGBA", 
-                    width=256,
-                    height=256,
-                    type="pil", 
-                    interactive=False
-                )
-            with gr.Row():
-                with gr.Group():
-                    do_remove_background = gr.Checkbox(
-                        label="Remove Background", value=True
-                    )
-                    sample_seed = gr.Number(value=42, label="Seed  (Try a different value if the result is unsatisfying)", precision=0)
-
-                    sample_steps = gr.Slider(
-                        label="Sample Steps",
-                        minimum=30,
-                        maximum=75,
-                        value=75,
-                        step=5
-                    )
-
-            with gr.Row():
-                submit = gr.Button("Generate", elem_id="generate", variant="primary")
-
-            with gr.Row(variant="panel"):
-                gr.Examples(
-                    examples=[
-                        os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
-                    ],
-                    inputs=[input_image],
-                    label="Examples",
-                    examples_per_page=15
-                )
-
-        with gr.Column():
-
-            with gr.Row():
-
-                with gr.Column():
-                    mv_show_images = gr.Image(
-                        label="Generated Multi-views",
-                        type="pil",
-                        width=379,
-                        interactive=False
-                    )
-
-                with gr.Column():
-                    output_video = gr.Video(
-                        label="video", format="mp4",
-                        width=379,
-                        autoplay=True,
-                        interactive=False
-                    )
-
-            with gr.Row():
-                output_model_obj = gr.Model3D(
-                    label="Output Model (OBJ Format)",
-                    width=768,
-                    interactive=False,
-                )
-    gr.Markdown(_LINKS_)
-    gr.Markdown(_CITE_)
-
-    submit.click(fn=check_input_image, inputs=[input_image]).success(
-        fn=preprocess,
-        inputs=[input_image, do_remove_background],
-        outputs=[processed_image],
-    ).success(
-        fn=make3d,
-        inputs=[processed_image, sample_steps, sample_seed],
-        outputs=[output_video, output_model_obj, mv_show_images]
-    )
-
-demo.launch()