app.py

import spaces

import os
import imageio
import numpy as np
import torch
import rembg
from PIL import Image, ImageDraw, ImageFont
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

# Set device to CPU
device = torch.device('cpu')

# load diffusion model
print('Loading diffusion model ...')
pipeline = DiffusionPipeline.from_pretrained(
    "sudo-ai/zero123plus-v1.2", 
    custom_pipeline="zero123plus",
    torch_dtype=torch.float32,  # Changed from float16 to float32 for CPU
)
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


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


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


# New function to generate 2D pixel art sprites
def generate_pixel_art(prompt, remove_background=True, sample_steps=75, seed=42):
    """Generate a pixel art sprite based on the prompt"""
    seed_everything(seed)
    
    # Create a simple image with text as starting point
    text_img = Image.new('RGB', (512, 512), color=(255, 255, 255))
    draw = ImageDraw.Draw(text_img)
    # Try to load a font, use default if not available
    try:
        font = ImageFont.truetype("Arial", 20)
    except:
        font = ImageFont.load_default()
    
    # Add prompt as text
    pixel_prompt = f"Pixel art: {prompt}"
    draw.text((10, 10), pixel_prompt, fill=(0, 0, 0), font=font)
    
    # Process through the pipeline
    processed_img = preprocess(text_img, remove_background)
    
    # Generate the pixel art
    result_img, _ = generate_mvs(processed_img, sample_steps, seed)
    
    # Save to a temporary file
    sprite_path = tempfile.NamedTemporaryFile(suffix=".png", delete=False).name
    result_img.save(sprite_path)
    
    return sprite_path


_HEADER_ = '''
<h2><b>Official 🤗 Gradio Demo</b></h2><h2><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>

**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.

Code: <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.

❗️❗️❗️**Important Notes:**
- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
- 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).
'''

_CITE_ = r"""
If InstantMesh is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>Github Repo</a>. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/InstantMesh?style=social)](https://github.com/TencentARC/InstantMesh)
---
📝 **Citation**

If you find our work useful for your research or applications, please cite using this bibtex:
```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}
}
```

📋 **License**

Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/LICENSE) for details.

📧 **Contact**

If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
"""


with gr.Blocks() as demo:
    gr.Markdown("# InstantMesh and Pixel Art Generator")
    
    with gr.Tab("3D Model Generation"):
        with gr.Row(variant="panel"):
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(
                        label="Input Image",
                        image_mode="RGBA",
                        sources="upload",
                        type="pil",
                        elem_id="content_image",
                    )
                    processed_image = gr.Image(
                        label="Processed Image", 
                        image_mode="RGBA", 
                        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_3d = gr.Button("Generate 3D Model", 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,
                        )
                    with gr.Tab("GLB"):
                        output_model_glb = gr.Model3D(
                            label="Output Model (GLB Format)",
                            interactive=False,
                        )
    
    with gr.Tab("Pixel Art Generation"):
        with gr.Row(variant="panel"):
            with gr.Column():
                pixel_prompt = gr.Textbox(
                    label="Describe your pixel art sprite",
                    placeholder="green cactus snake character, side view, game sprite",
                    lines=3
                )
                with gr.Row():
                    pixel_bg_remove = gr.Checkbox(label="Remove Background", value=True)
                    pixel_seed = gr.Number(value=42, label="Seed Value", precision=0)
                    pixel_steps = gr.Slider(
                        label="Sample Steps",
                        minimum=30,
                        maximum=75,
                        value=75,
                        step=5
                    )
                submit_pixel = gr.Button("Generate Pixel Art", variant="primary")
            
            with gr.Column():
                pixel_output = gr.Image(
                    label="Generated Pixel Art Sprite",
                    type="pil",
                    interactive=False
                )
    
    # Set up event handlers
    mv_images = gr.State()
    
    # 3D Model generation flow
    submit_3d.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]
    )
    
    # Pixel Art generation flow
    submit_pixel.click(
        fn=generate_pixel_art,
        inputs=[pixel_prompt, pixel_bg_remove, pixel_steps, pixel_seed],
        outputs=[pixel_output]
    )
    
    # Enable API access
    demo.queue(concurrency_count=1)

# Launch with API access enabled
demo.launch(enable_api=True, share=False)