app.py

import spaces

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, save_glb
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


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.
###############################################################################

import shutil

def find_cuda():
    # Check if CUDA_HOME or CUDA_PATH environment variables are set
    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')

    if cuda_home and os.path.exists(cuda_home):
        return cuda_home

    # Search for the nvcc executable in the system's PATH
    nvcc_path = shutil.which('nvcc')

    if nvcc_path:
        # Remove the 'bin/nvcc' part to get the CUDA installation path
        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
        return cuda_path

    return None

cuda_path = find_cuda()

if cuda_path:
    print(f"CUDA installation found at: {cuda_path}")
else:
    print("CUDA installation not found")

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)

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


@spaces.GPU
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 -> (n m) h w c', n=3, m=2)
    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
    show_image = Image.fromarray(show_image.numpy())

    return z123_image, show_image


@spaces.GPU
def make3d(images):

    global model
    if IS_FLEXICUBES:
        model.init_flexicubes_geometry(device, use_renderer=False)
    model = model.eval()

    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=4.0).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")
    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")

    with torch.no_grad():
        # get triplane
        planes = model.forward_planes(images, input_cameras)

        # get mesh
        mesh_out = model.extract_mesh(
            planes,
            use_texture_map=False,
            **infer_config,
        )

        vertices, faces, vertex_colors = mesh_out
        vertices = vertices[:, [1, 2, 0]]
        
        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
        save_obj(vertices, faces, vertex_colors, mesh_fpath)
        
        print(f"Mesh saved to {mesh_fpath}")

    return mesh_fpath, mesh_glb_fpath


_HEADER_ = """

# Generate 3D Assets for Roblox 🪄
Here, you can **generate 3D Assets using AI for your Roblox game for free**.

Some examples of generated assets (video by InstantMesh Team):
<video width="640" height="360" controls>
        <source src="https://huggingface.co/spaces/ThomasSimonini/Roblox-3D-Assets-Generator/resolve/main/assets/vid/examples.mp4" type="video/mp4">
</video>
We **wrote a tutorial to help you generating assets and importing it in your Roblox Games** ➡️ [here](https://substack.com/home/post/p-148756528?r=dq5fg&utm_campaign=post&utm_medium=web)

If you like the tool, please click on the like button ❤️.

➡️ With this tool, you can generate **high poly mesh for your Roblox game**.

- This tool uses <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>InstantMesh</a>, an open-source AI model for **fast** feedforward 3D mesh generation from a single image.

### The Process

1. Choose an image of the object you want to create in 3D. Or generate one with [Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion).

2. Upload the image

💡 Tips:

- If there's a background in your image -> ✅ Check "Remove background"

- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).

3. Click on generate

4. The 3D mesh is generated, and you can download the file. 
- If you want to **import it now to Roblow download the GLB file** using the arrow ⬇️
- If you want to **transform it to low poly mesh, download the OBJ** file using the arrow ⬇️

5. Open Roblox Studio

6. In your Roblox Project, click on Import 3D and select the downloaded file.

7. You can now drag and drop your generated 3D file in your scene 🎉.

8. (Optional) You can also transform this High poly mesh to a low poly using the second tool below this one on this page, but the mesh material will disappear and you'll need to create your own materials in Roblox.
"""

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 Value", 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.Column():

            with gr.Row():

                with gr.Column():
                    mv_show_images = gr.Image(
                        label="Generated Multi-views",
                        type="pil",
                        width=379,
                        interactive=False
                    )

            with gr.Row():
                with gr.Tab("OBJ"):
                    output_model_obj = gr.Model3D(
                        label="Output Model (OBJ Format)",
                        interactive=False,
                    )
                    gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
                with gr.Tab("GLB"):
                    output_model_glb = gr.Model3D(
                        label="Output Model (GLB Format)",
                        interactive=False,
                    )
                    gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")

            with gr.Row():
                gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')

    mv_images = gr.State()

    submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=preprocess,
        inputs=[input_image, do_remove_background],
        outputs=[processed_image],
    ).success(
        fn=generate_mvs,
        inputs=[processed_image, sample_steps, sample_seed],
        outputs=[mv_images, mv_show_images]
        
    ).success(
        fn=make3d,
        inputs=[mv_images],
        outputs=[output_model_obj, output_model_glb]
    )
demo.queue()
demo.launch()