app.py

import spaces
import os
import time
from os import path
from huggingface_hub import hf_hub_download
import numpy as np
import torch
import rembg
from PIL import Image
from torchvision.transforms import v2
from einops import rearrange
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import shutil
import tempfile
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 random
import requests
import io

# Set up cache path
cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

if not path.exists(cache_path):
    os.makedirs(cache_path, exist_ok=True)

torch.backends.cuda.matmul.allow_tf32 = True

class timer:
    def __init__(self, method_name="timed process"):
        self.method = method_name
    def __enter__(self):
        self.start = time.time()
        print(f"{self.method} starts")
    def __exit__(self, exc_type, exc_val, exc_tb):
        end = time.time()
        print(f"{self.method} took {str(round(end - self.start, 2))}s")

def find_cuda():
    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
    nvcc_path = shutil.which('nvcc')
    if nvcc_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")


API_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

device = 'cuda'

# Load 3D generation models
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

# Load diffusion model for 3D generation
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 get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
    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 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)
    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)
    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()
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)

    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
    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
    mesh_dirname = os.path.dirname(mesh_fpath)
    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")

    with torch.no_grad():
        planes = model.forward_planes(images, input_cameras)
        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)
    
    return mesh_fpath, mesh_glb_fpath

# Remove the FluxPipeline setup and replace with the query function
def query(prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
    if not prompt:
        return None

    lora_id = "gokaygokay/Flux-Game-Assets-LoRA-v2"
    API_URL = f"https://api-inference.huggingface.co/models/{lora_id}"
    
    if randomize_seed:
        seed = random.randint(1, 4294967296)
    
    payload = {
        "inputs": prompt,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed,
        "parameters": {
            "width": width,
            "height": height
        }
    }

    response = requests.post(API_URL, headers=headers, json=payload, timeout=100)
    if response.status_code != 200:
        if response.status_code == 503:
            raise gr.Error("The model is being loaded")
        raise gr.Error(f"Error {response.status_code}")
    
    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        return image
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None

# Update the Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        <div style="text-align: center; max-width: 650px; margin: 0 auto;">
            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem;">Flux Image to 3D Model Generator</h1>
        </div>
        """
    )

    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(
                label="Your Image Description",
                placeholder="E.g., A serene landscape with mountains and a lake at sunset",
                lines=3
            )
            
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Group():
                    with gr.Row():
                        height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
                        width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
                    
                    with gr.Row():
                        steps = gr.Slider(label="Inference Steps", minimum=10, maximum=50, step=1, value=28)
                        scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
                    
                    seed = gr.Number(label="Seed", value=-1, precision=0)
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            generate_btn = gr.Button("Generate 3D Model", variant="primary")

        with gr.Column(scale=4):
            flux_output = gr.Image(label="Generated Flux Image")
            mv_show_images = gr.Image(label="Generated Multi-views")
            with gr.Row():
                with gr.Tab("OBJ"):
                    output_model_obj = gr.Model3D(label="Output Model (OBJ Format)")
                with gr.Tab("GLB"):
                    output_model_glb = gr.Model3D(label="Output Model (GLB Format)")

    mv_images = gr.State()

    def process_pipeline(prompt, height, width, steps, scales, seed, randomize_seed):
        # Generate Flux image using the API
        prompt_real = f"wbgmsst, {prompt}, white background"
        flux_image = query(prompt_real, steps, scales, randomize_seed, seed, width, height)
        if flux_image is None:
            raise gr.Error("Failed to generate image")
            
        processed_image = preprocess(flux_image, do_remove_background=True)
        mv_images, show_image = generate_mvs(processed_image, steps, seed)
        obj_path, glb_path = make3d(mv_images)
        return flux_image, show_image, obj_path, glb_path

    generate_btn.click(
        fn=process_pipeline,
        inputs=[prompt, height, width, steps, scales, seed, randomize_seed],
        outputs=[flux_output, mv_show_images, output_model_obj, output_model_glb]
    )

if __name__ == "__main__":
    demo.queue().launch()