Add app.py

app.py

@@ -1,7 +1,861 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+import gradio as gr
+from gradio_client import Client, handle_file
+import spaces
+from concurrent.futures import ThreadPoolExecutor
+
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = '1'
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+os.environ["ATTN_BACKEND"] = "flash_attn_3"
+os.environ["FLEX_GEMM_AUTOTUNE_CACHE_PATH"] = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'autotune_cache.json')
+os.environ["FLEX_GEMM_AUTOTUNER_VERBOSE"] = '1'
+from datetime import datetime
+import shutil
+import cv2
+from typing import *
+import numpy as np
+from PIL import Image
+import base64
+import io
+import tempfile
+
+# Lazy imports - will be loaded when GPU is available
+torch = None
+SparseTensor = None
+Trellis2ImageTo3DPipeline = None
+EnvMap = None
+render_utils = None
+o_voxel = None
+
+# Global state - initialized on first GPU call
+pipeline = None
+envmap = None
+_initialized = False
+
+
+def _lazy_import():
+    """Import GPU-dependent modules. Must be called from within a @spaces.GPU function."""
+    global torch, SparseTensor, Trellis2ImageTo3DPipeline, EnvMap, render_utils, o_voxel
+    if torch is None:
+        import torch as _torch
+        torch = _torch
+    if SparseTensor is None:
+        from trellis2.modules.sparse import SparseTensor as _SparseTensor
+        SparseTensor = _SparseTensor
+    if Trellis2ImageTo3DPipeline is None:
+        from trellis2.pipelines import Trellis2ImageTo3DPipeline as _Trellis2ImageTo3DPipeline
+        Trellis2ImageTo3DPipeline = _Trellis2ImageTo3DPipeline
+    if EnvMap is None:
+        from trellis2.renderers import EnvMap as _EnvMap
+        EnvMap = _EnvMap
+    if render_utils is None:
+        from trellis2.utils import render_utils as _render_utils
+        render_utils = _render_utils
+    if o_voxel is None:
+        import o_voxel as _o_voxel
+        o_voxel = _o_voxel
+
+
+def _initialize_pipeline():
+    """Initialize the pipeline and environment maps. Must be called from within a @spaces.GPU function."""
+    global pipeline, envmap, _initialized
+    if _initialized:
+        return
+
+    _lazy_import()
+
+    pipeline = Trellis2ImageTo3DPipeline.from_pretrained('microsoft/TRELLIS.2-4B')
+    pipeline.rembg_model = None
+    pipeline.low_vram = False
+    pipeline.cuda()
+
+    envmap = {
+        'forest': EnvMap(torch.tensor(
+            cv2.cvtColor(cv2.imread('assets/hdri/forest.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB),
+            dtype=torch.float32, device='cuda'
+        )),
+        'sunset': EnvMap(torch.tensor(
+            cv2.cvtColor(cv2.imread('assets/hdri/sunset.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB),
+            dtype=torch.float32, device='cuda'
+        )),
+        'courtyard': EnvMap(torch.tensor(
+            cv2.cvtColor(cv2.imread('assets/hdri/courtyard.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB),
+            dtype=torch.float32, device='cuda'
+        )),
+    }
+
+    _initialized = True
+
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+MODES = [
+    {"name": "Normal", "icon": "assets/app/normal.png", "render_key": "normal"},
+    {"name": "Clay render", "icon": "assets/app/clay.png", "render_key": "clay"},
+    {"name": "Base color", "icon": "assets/app/basecolor.png", "render_key": "base_color"},
+    {"name": "HDRI forest", "icon": "assets/app/hdri_forest.png", "render_key": "shaded_forest"},
+    {"name": "HDRI sunset", "icon": "assets/app/hdri_sunset.png", "render_key": "shaded_sunset"},
+    {"name": "HDRI courtyard", "icon": "assets/app/hdri_courtyard.png", "render_key": "shaded_courtyard"},
+]
+STEPS = 8
+DEFAULT_MODE = 3
+DEFAULT_STEP = 3
+
+
+css = """
+/* Overwrite Gradio Default Style */
+.stepper-wrapper {
+    padding: 0;
+}
+
+.stepper-container {
+    padding: 0;
+    align-items: center;
+}
+
+.step-button {
+    flex-direction: row;
+}
+
+.step-connector {
+    transform: none;
+}
+
+.step-number {
+    width: 16px;
+    height: 16px;
+}
+
+.step-label {
+    position: relative;
+    bottom: 0;
+}
+
+.wrap.center.full {
+    inset: 0;
+    height: 100%;
+}
+
+.wrap.center.full.translucent {
+    background: var(--block-background-fill);
+}
+
+.meta-text-center {
+    display: block !important;
+    position: absolute !important;
+    top: unset !important;
+    bottom: 0 !important;
+    right: 0 !important;
+    transform: unset !important;
+}
+
+/* Previewer */
+.previewer-container {
+    position: relative;
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+    width: 100%;
+    height: 722px;
+    margin: 0 auto;
+    padding: 20px;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+}
+
+.previewer-container .tips-icon {
+    position: absolute;
+    right: 10px;
+    top: 10px;
+    z-index: 10;
+    border-radius: 10px;
+    color: #fff;
+    background-color: var(--color-accent);
+    padding: 3px 6px;
+    user-select: none;
+}
+
+.previewer-container .tips-text {
+    position: absolute;
+    right: 10px;
+    top: 50px;
+    color: #fff;
+    background-color: var(--color-accent);
+    border-radius: 10px;
+    padding: 6px;
+    text-align: left;
+    max-width: 300px;
+    z-index: 10;
+    transition: all 0.3s;
+    opacity: 0%;
+    user-select: none;
+}
+
+.previewer-container .tips-text p {
+    font-size: 14px;
+    line-height: 1.2;
+}
+
+.tips-icon:hover + .tips-text {
+    display: block;
+    opacity: 100%;
+}
+
+/* Row 1: Display Modes */
+.previewer-container .mode-row {
+    width: 100%;
+    display: flex;
+    gap: 8px;
+    justify-content: center;
+    margin-bottom: 20px;
+    flex-wrap: wrap;
+}
+.previewer-container .mode-btn {
+    width: 24px;
+    height: 24px;
+    border-radius: 50%;
+    cursor: pointer;
+    opacity: 0.5;
+    transition: all 0.2s;
+    border: 2px solid #ddd;
+    object-fit: cover;
+}
+.previewer-container .mode-btn:hover { opacity: 0.9; transform: scale(1.1); }
+.previewer-container .mode-btn.active {
+    opacity: 1;
+    border-color: var(--color-accent);
+    transform: scale(1.1);
+}
+
+/* Row 2: Display Image */
+.previewer-container .display-row {
+    margin-bottom: 20px;
+    min-height: 400px;
+    width: 100%;
+    flex-grow: 1;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+}
+.previewer-container .previewer-main-image {
+    max-width: 100%;
+    max-height: 100%;
+    flex-grow: 1;
+    object-fit: contain;
+    display: none;
+}
+.previewer-container .previewer-main-image.visible {
+    display: block;
+}
+
+/* Row 3: Custom HTML Slider */
+.previewer-container .slider-row {
+    width: 100%;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 10px;
+    padding: 0 10px;
+}
+
+.previewer-container input[type=range] {
+    -webkit-appearance: none;
+    width: 100%;
+    max-width: 400px;
+    background: transparent;
+}
+.previewer-container input[type=range]::-webkit-slider-runnable-track {
+    width: 100%;
+    height: 8px;
+    cursor: pointer;
+    background: #ddd;
+    border-radius: 5px;
+}
+.previewer-container input[type=range]::-webkit-slider-thumb {
+    height: 20px;
+    width: 20px;
+    border-radius: 50%;
+    background: var(--color-accent);
+    cursor: pointer;
+    -webkit-appearance: none;
+    margin-top: -6px;
+    box-shadow: 0 2px 5px rgba(0,0,0,0.2);
+    transition: transform 0.1s;
+}
+.previewer-container input[type=range]::-webkit-slider-thumb:hover {
+    transform: scale(1.2);
+}
+
+/* Overwrite Previewer Block Style */
+.gradio-container .padded:has(.previewer-container) {
+    padding: 0 !important;
+}
+
+.gradio-container:has(.previewer-container) [data-testid="block-label"] {
+    position: absolute;
+    top: 0;
+    left: 0;
+}
+"""
+
+
+head = """
+<script>
+    function refreshView(mode, step) {
+        // 1. Find current mode and step
+        const allImgs = document.querySelectorAll('.previewer-main-image');
+        for (let i = 0; i < allImgs.length; i++) {
+            const img = allImgs[i];
+            if (img.classList.contains('visible')) {
+                const id = img.id;
+                const [_, m, s] = id.split('-');
+                if (mode === -1) mode = parseInt(m.slice(1));
+                if (step === -1) step = parseInt(s.slice(1));
+                break;
+            }
+        }
+
+        // 2. Hide ALL images
+        // We select all elements with class 'previewer-main-image'
+        allImgs.forEach(img => img.classList.remove('visible'));
+
+        // 3. Construct the specific ID for the current state
+        // Format: view-m{mode}-s{step}
+        const targetId = 'view-m' + mode + '-s' + step;
+        const targetImg = document.getElementById(targetId);
+
+        // 4. Show ONLY the target
+        if (targetImg) {
+            targetImg.classList.add('visible');
+        }
+
+        // 5. Update Button Highlights
+        const allBtns = document.querySelectorAll('.mode-btn');
+        allBtns.forEach((btn, idx) => {
+            if (idx === mode) btn.classList.add('active');
+            else btn.classList.remove('active');
+        });
+    }
+
+    // --- Action: Switch Mode ---
+    function selectMode(mode) {
+        refreshView(mode, -1);
+    }
+
+    // --- Action: Slider Change ---
+    function onSliderChange(val) {
+        refreshView(-1, parseInt(val));
+    }
+</script>
+"""
+
+
+empty_html = f"""
+<div class="previewer-container">
+    <svg style=" opacity: .5; height: var(--size-5); color: var(--body-text-color);"
+    xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-image"><rect x="3" y="3" width="18" height="18" rx="2" ry="2"></rect><circle cx="8.5" cy="8.5" r="1.5"></circle><polyline points="21 15 16 10 5 21"></polyline></svg>
+</div>
+"""
+
+
+def image_to_base64(image):
+    buffered = io.BytesIO()
+    image = image.convert("RGB")
+    image.save(buffered, format="jpeg", quality=85)
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    return f"data:image/jpeg;base64,{img_str}"
+
+
+def start_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(user_dir, exist_ok=True)
+
+
+def end_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    if os.path.exists(user_dir):
+        shutil.rmtree(user_dir)
+
+
+def remove_background(input: Image.Image) -> Image.Image:
+    with tempfile.NamedTemporaryFile(suffix='.png') as f:
+        input = input.convert('RGB')
+        input.save(f.name)
+        output = rmbg_client.predict(handle_file(f.name), api_name="/image")[0][0]
+        output = Image.open(output)
+        return output
+
+
+def preprocess_image(input: Image.Image) -> Image.Image:
+    """
+    Preprocess the input image.
+    """
+    # if has alpha channel, use it directly; otherwise, remove background
+    has_alpha = False
+    if input.mode == 'RGBA':
+        alpha = np.array(input)[:, :, 3]
+        if not np.all(alpha == 255):
+            has_alpha = True
+    max_size = max(input.size)
+    scale = min(1, 1024 / max_size)
+    if scale < 1:
+        input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS)
+    if has_alpha:
+        output = input
+    else:
+        output = remove_background(input)
+    output_np = np.array(output)
+    alpha = output_np[:, :, 3]
+    bbox = np.argwhere(alpha > 0.8 * 255)
+    bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0])
+    center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2
+    size = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
+    size = int(size * 1)
+    bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2
+    output = output.crop(bbox)  # type: ignore
+    output = np.array(output).astype(np.float32) / 255
+    output = output[:, :, :3] * output[:, :, 3:4]
+    output = Image.fromarray((output * 255).astype(np.uint8))
+    return output
+
+
+def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
+    """
+    Preprocess a list of input images for multi-image conditioning.
+    Uses parallel processing for faster background removal.
+    """
+    images = [image[0] for image in images]
+    with ThreadPoolExecutor(max_workers=min(4, len(images))) as executor:
+        processed_images = list(executor.map(preprocess_image, images))
+    return processed_images
+
+
+def pack_state(latents):
+    shape_slat, tex_slat, res = latents
+    return {
+        'shape_slat_feats': shape_slat.feats.cpu().numpy(),
+        'tex_slat_feats': tex_slat.feats.cpu().numpy(),
+        'coords': shape_slat.coords.cpu().numpy(),
+        'res': res,
+    }
+
+
+def unpack_state(state: dict):
+    _lazy_import()
+    shape_slat = SparseTensor(
+        feats=torch.from_numpy(state['shape_slat_feats']).cuda(),
+        coords=torch.from_numpy(state['coords']).cuda(),
+    )
+    tex_slat = shape_slat.replace(torch.from_numpy(state['tex_slat_feats']).cuda())
+    return shape_slat, tex_slat, state['res']
+
+
+def get_seed(randomize_seed: bool, seed: int) -> int:
+    """
+    Get the random seed.
+    """
+    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
+
+
+def prepare_multi_example() -> List[Image.Image]:
+    """
+    Prepare multi-image examples for the gallery.
+    """
+    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
+    images = []
+    for case in multi_case:
+        _images = []
+        for i in range(1, 4):
+            img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
+            W, H = img.size
+            img = img.resize((int(W / H * 512), 512))
+            _images.append(np.array(img))
+        images.append(Image.fromarray(np.concatenate(_images, axis=1)))
+    return images
+
+
+def split_image(image: Image.Image) -> List[Image.Image]:
+    """
+    Split a concatenated image into multiple views.
+    """
+    image = np.array(image)
+    alpha = image[..., 3]
+    alpha = np.any(alpha > 0, axis=0)
+    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
+    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
+    images = []
+    for s, e in zip(start_pos, end_pos):
+        images.append(Image.fromarray(image[:, s:e+1]))
+    return [preprocess_image(image) for image in images]
+
+
+@spaces.GPU(duration=120)
+def image_to_3d(
+    image: Image.Image,
+    seed: int,
+    resolution: str,
+    ss_guidance_strength: float,
+    ss_guidance_rescale: float,
+    ss_sampling_steps: int,
+    ss_rescale_t: float,
+    shape_slat_guidance_strength: float,
+    shape_slat_guidance_rescale: float,
+    shape_slat_sampling_steps: int,
+    shape_slat_rescale_t: float,
+    tex_slat_guidance_strength: float,
+    tex_slat_guidance_rescale: float,
+    tex_slat_sampling_steps: int,
+    tex_slat_rescale_t: float,
+    req: gr.Request,
+    progress=gr.Progress(track_tqdm=True),
+    multiimages: List[Tuple[Image.Image, str]] = None,
+    is_multiimage: bool = False,
+    multiimage_algo: Literal["multidiffusion", "stochastic"] = "stochastic",
+) -> str:
+    # Initialize pipeline on first call
+    _initialize_pipeline()
+
+    # --- Sampling ---
+    if not is_multiimage:
+        outputs, latents = pipeline.run(
+            image,
+            seed=seed,
+            preprocess_image=False,
+            sparse_structure_sampler_params={
+                "steps": ss_sampling_steps,
+                "guidance_strength": ss_guidance_strength,
+                "guidance_rescale": ss_guidance_rescale,
+                "rescale_t": ss_rescale_t,
+            },
+            shape_slat_sampler_params={
+                "steps": shape_slat_sampling_steps,
+                "guidance_strength": shape_slat_guidance_strength,
+                "guidance_rescale": shape_slat_guidance_rescale,
+                "rescale_t": shape_slat_rescale_t,
+            },
+            tex_slat_sampler_params={
+                "steps": tex_slat_sampling_steps,
+                "guidance_strength": tex_slat_guidance_strength,
+                "guidance_rescale": tex_slat_guidance_rescale,
+                "rescale_t": tex_slat_rescale_t,
+            },
+            pipeline_type={
+                "512": "512",
+                "1024": "1024_cascade",
+                "1536": "1536_cascade",
+            }[resolution],
+            return_latent=True,
+        )
+    else:
+        outputs, latents = pipeline.run_multi_image(
+            [image[0] for image in multiimages],
+            seed=seed,
+            preprocess_image=False,
+            sparse_structure_sampler_params={
+                "steps": ss_sampling_steps,
+                "guidance_strength": ss_guidance_strength,
+                "guidance_rescale": ss_guidance_rescale,
+                "rescale_t": ss_rescale_t,
+            },
+            shape_slat_sampler_params={
+                "steps": shape_slat_sampling_steps,
+                "guidance_strength": shape_slat_guidance_strength,
+                "guidance_rescale": shape_slat_guidance_rescale,
+                "rescale_t": shape_slat_rescale_t,
+            },
+            tex_slat_sampler_params={
+                "steps": tex_slat_sampling_steps,
+                "guidance_strength": tex_slat_guidance_strength,
+                "guidance_rescale": tex_slat_guidance_rescale,
+                "rescale_t": tex_slat_rescale_t,
+            },
+            pipeline_type={
+                "512": "512",
+                "1024": "1024_cascade",
+                "1536": "1536_cascade",
+            }[resolution],
+            return_latent=True,
+            mode=multiimage_algo,
+        )
+    mesh = outputs[0]
+    mesh.simplify(16777216)  # nvdiffrast limit
+    images = render_utils.render_snapshot(mesh, resolution=1024, r=2, fov=36, nviews=STEPS, envmap=envmap)
+    state = pack_state(latents)
+    torch.cuda.empty_cache()
+
+    # --- HTML Construction ---
+    # The Stack of 48 Images - encode in parallel for speed
+    def encode_preview_image(args):
+        m_idx, s_idx, render_key = args
+        img_base64 = image_to_base64(Image.fromarray(images[render_key][s_idx]))
+        return (m_idx, s_idx, img_base64)
+
+    encode_tasks = [
+        (m_idx, s_idx, mode['render_key'])
+        for m_idx, mode in enumerate(MODES)
+        for s_idx in range(STEPS)
+    ]
+
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        encoded_results = list(executor.map(encode_preview_image, encode_tasks))
+
+    # Build HTML from encoded results
+    encoded_map = {(m, s): b64 for m, s, b64 in encoded_results}
+    images_html = ""
+    for m_idx, mode in enumerate(MODES):
+        for s_idx in range(STEPS):
+            unique_id = f"view-m{m_idx}-s{s_idx}"
+            is_visible = (m_idx == DEFAULT_MODE and s_idx == DEFAULT_STEP)
+            vis_class = "visible" if is_visible else ""
+            img_base64 = encoded_map[(m_idx, s_idx)]
+
+            images_html += f"""
+                <img id="{unique_id}"
+                     class="previewer-main-image {vis_class}"
+                     src="{img_base64}"
+                     loading="eager">
+            """
+
+    # Button Row HTML
+    btns_html = ""
+    for idx, mode in enumerate(MODES):
+        active_class = "active" if idx == DEFAULT_MODE else ""
+        # Note: onclick calls the JS function defined in Head
+        btns_html += f"""
+            <img src="{mode['icon_base64']}"
+                 class="mode-btn {active_class}"
+                 onclick="selectMode({idx})"
+                 title="{mode['name']}">
+        """
+
+    # Assemble the full component
+    full_html = f"""
+    <div class="previewer-container">
+        <div class="tips-wrapper">
+            <div class="tips-icon">💡Tips</div>
+            <div class="tips-text">
+                <p>● <b>Render Mode</b> - Click on the circular buttons to switch between different render modes.</p>
+                <p>● <b>View Angle</b> - Drag the slider to change the view angle.</p>
+            </div>
+        </div>
+
+        <!-- Row 1: Viewport containing 48 static <img> tags -->
+        <div class="display-row">
+            {images_html}
+        </div>
+
+        <!-- Row 2 -->
+        <div class="mode-row" id="btn-group">
+            {btns_html}
+        </div>
+
+        <!-- Row 3: Slider -->
+        <div class="slider-row">
+            <input type="range" id="custom-slider" min="0" max="{STEPS - 1}" value="{DEFAULT_STEP}" step="1" oninput="onSliderChange(this.value)">
+        </div>
+    </div>
+    """
+
+    return state, full_html
+
+
+@spaces.GPU(duration=120)
+def extract_glb(
+    state: dict,
+    decimation_target: int,
+    texture_size: int,
+    req: gr.Request,
+    progress=gr.Progress(track_tqdm=True),
+) -> Tuple[str, str]:
+    """
+    Extract a GLB file from the 3D model.
+
+    Args:
+        state (dict): The state of the generated 3D model.
+        decimation_target (int): The target face count for decimation.
+        texture_size (int): The texture resolution.
+
+    Returns:
+        str: The path to the extracted GLB file.
+    """
+    # Initialize pipeline on first call
+    _initialize_pipeline()
+
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    shape_slat, tex_slat, res = unpack_state(state)
+    mesh = pipeline.decode_latent(shape_slat, tex_slat, res)[0]
+    mesh.simplify(16777216)  # nvdiffrast limit
+    glb = o_voxel.postprocess.to_glb(
+        vertices=mesh.vertices,
+        faces=mesh.faces,
+        attr_volume=mesh.attrs,
+        coords=mesh.coords,
+        attr_layout=pipeline.pbr_attr_layout,
+        grid_size=res,
+        aabb=[[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
+        decimation_target=decimation_target,
+        texture_size=texture_size,
+        remesh=True,
+        remesh_band=1,
+        remesh_project=0,
+        use_tqdm=True,
+    )
+    now = datetime.now()
+    timestamp = now.strftime("%Y-%m-%dT%H%M%S") + f".{now.microsecond // 1000:03d}"
+    os.makedirs(user_dir, exist_ok=True)
+    glb_path = os.path.join(user_dir, f'sample_{timestamp}.glb')
+    glb.export(glb_path, extension_webp=True)
+    torch.cuda.empty_cache()
+    return glb_path, glb_path
+
+
+with gr.Blocks(delete_cache=(600, 600)) as demo:
+    gr.Markdown("""
+    ## Image to 3D Asset with [TRELLIS.2](https://microsoft.github.io/TRELLIS.2)
+    * Upload an image (preferably with an alpha-masked foreground object) and click Generate to create a 3D asset.
+    * Click Extract GLB to export and download the generated GLB file if you're satisfied with the result. Otherwise, try another time.
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1, min_width=360):
+            with gr.Tabs() as input_tabs:
+                with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
+                    image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=400)
+                with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
+                    multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=400, columns=3)
+                    gr.Markdown("""
+                        Input different views of the object in separate images.
+                        *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
+                    """)
+
+            resolution = gr.Radio(["512", "1024", "1536"], label="Resolution", value="1024")
+            seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+            decimation_target = gr.Slider(100000, 500000, label="Decimation Target", value=300000, step=10000)
+            texture_size = gr.Slider(1024, 4096, label="Texture Size", value=2048, step=1024)
+
+            generate_btn = gr.Button("Generate")
+
+            with gr.Accordion(label="Advanced Settings", open=False):
+                gr.Markdown("Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(1.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_guidance_rescale = gr.Slider(0.0, 1.0, label="Guidance Rescale", value=0.7, step=0.01)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                    ss_rescale_t = gr.Slider(1.0, 6.0, label="Rescale T", value=5.0, step=0.1)
+                gr.Markdown("Stage 2: Shape Generation")
+                with gr.Row():
+                    shape_slat_guidance_strength = gr.Slider(1.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    shape_slat_guidance_rescale = gr.Slider(0.0, 1.0, label="Guidance Rescale", value=0.5, step=0.01)
+                    shape_slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                    shape_slat_rescale_t = gr.Slider(1.0, 6.0, label="Rescale T", value=3.0, step=0.1)
+                gr.Markdown("Stage 3: Material Generation")
+                with gr.Row():
+                    tex_slat_guidance_strength = gr.Slider(1.0, 10.0, label="Guidance Strength", value=1.0, step=0.1)
+                    tex_slat_guidance_rescale = gr.Slider(0.0, 1.0, label="Guidance Rescale", value=0.0, step=0.01)
+                    tex_slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                    tex_slat_rescale_t = gr.Slider(1.0, 6.0, label="Rescale T", value=3.0, step=0.1)
+                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
+
+        with gr.Column(scale=10):
+            with gr.Walkthrough(selected=0) as walkthrough:
+                with gr.Step("Preview", id=0):
+                    preview_output = gr.HTML(empty_html, label="3D Asset Preview", show_label=True, container=True)
+                    extract_btn = gr.Button("Extract GLB")
+                with gr.Step("Extract", id=1):
+                    glb_output = gr.Model3D(label="Extracted GLB", height=724, show_label=True, display_mode="solid", clear_color=(0.25, 0.25, 0.25, 1.0))
+                    download_btn = gr.DownloadButton(label="Download GLB")
+            gr.Markdown("*We are actively working on improving the speed of GLB extraction. Currently, it may take half a minute or more and face count is limited.*")
+
+        with gr.Column(scale=1, min_width=172) as single_image_example:
+            examples = gr.Examples(
+                examples=[
+                    f'assets/example_image/{image}'
+                    for image in os.listdir("assets/example_image")
+                ],
+                inputs=[image_prompt],
+                fn=preprocess_image,
+                outputs=[image_prompt],
+                run_on_click=True,
+                examples_per_page=18,
+            )
+
+        with gr.Column(visible=True) as multiimage_example:
+            examples_multi = gr.Examples(
+                examples=prepare_multi_example(),
+                label="Multi Image Examples",
+                inputs=[image_prompt],
+                fn=split_image,
+                outputs=[multiimage_prompt],
+                run_on_click=True,
+                examples_per_page=8,
+            )
+
+    is_multiimage = gr.State(False)
+    output_buf = gr.State()
+
+
+    # Handlers
+    demo.load(start_session)
+    demo.unload(end_session)
+
+    single_image_input_tab.select(
+        lambda: (False, gr.update(visible=True), gr.update(visible=True)),
+        outputs=[is_multiimage, single_image_example, multiimage_example]
+    )
+    multiimage_input_tab.select(
+        lambda: (True, gr.update(visible=True), gr.update(visible=True)),
+        outputs=[is_multiimage, single_image_example, multiimage_example]
+    )
+
+    image_prompt.upload(
+        preprocess_image,
+        inputs=[image_prompt],
+        outputs=[image_prompt],
+    )
+    multiimage_prompt.upload(
+        preprocess_images,
+        inputs=[multiimage_prompt],
+        outputs=[multiimage_prompt],
+    )
+
+    generate_btn.click(
+        get_seed,
+        inputs=[randomize_seed, seed],
+        outputs=[seed],
+    ).then(
+        lambda: gr.Walkthrough(selected=0), outputs=walkthrough
+    ).then(
+        image_to_3d,
+        inputs=[
+            image_prompt, seed, resolution,
+            ss_guidance_strength, ss_guidance_rescale, ss_sampling_steps, ss_rescale_t,
+            shape_slat_guidance_strength, shape_slat_guidance_rescale, shape_slat_sampling_steps, shape_slat_rescale_t,
+            tex_slat_guidance_strength, tex_slat_guidance_rescale, tex_slat_sampling_steps, tex_slat_rescale_t,
+            multiimage_prompt, is_multiimage, multiimage_algo
+        ],
+        outputs=[output_buf, preview_output],
+    )
+
+    extract_btn.click(
+        lambda: gr.Walkthrough(selected=1), outputs=walkthrough
+    ).then(
+        extract_glb,
+        inputs=[output_buf, decimation_target, texture_size],
+        outputs=[glb_output, download_btn],
+    )
+
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    os.makedirs(TMP_DIR, exist_ok=True)
+
+    # Construct ui components (CPU-only, no GPU needed)
+    btn_img_base64_strs = {}
+    for i in range(len(MODES)):
+        icon = Image.open(MODES[i]['icon'])
+        MODES[i]['icon_base64'] = image_to_base64(icon)
+
+    rmbg_client = Client("briaai/BRIA-RMBG-2.0")
+
+    demo.launch(css=css, head=head)