Upload folder using huggingface_hub

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+assets/teaser.webp filter=lfs diff=lfs merge=lfs -text
+assets/images/0_img.png filter=lfs diff=lfs merge=lfs -text
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.gitignore

@@ -0,0 +1,20 @@
+__pycache__/
+*.pyc
+.venv/
+venv/
+*.ckpt
+*.pt
+*.bin
+*.safetensors
+wandb/
+.wandb/
+node_modules/
+*.egg-info/
+.gradio/
+example.py
+
+outputs*/
+results*/
+ckpts*/
+tmp/example.py
+tmp/

README.md

@@ -1,15 +1,15 @@
 ---
-title: Pixal3D Server
-emoji: 👁
-colorFrom: blue
-colorTo: blue
+title: Pixal3D
+emoji: 🏆
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: 6.13.0
+python_version: "3.10"
 app_file: app.py
 pinned: false
-license: apache-2.0
-short_description: Pixal3D Server
+license: mit
+short_description: "High-fidelity pixel-aligned image-to-3D generation."
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

@@ -0,0 +1,379 @@
+"""
+Pixal3D HF Space Proxy
+======================
+This is a lightweight proxy app for HF Space that redirects users to a 
+locally deployed Gradio share link.
+
+Setup:
+1. Deploy this as your HF Space app.py
+2. Set HF Space Secret: REMOTE_URL = your local share link (e.g. https://xxxxx.gradio.live)
+3. Users visiting the HF Space will be seamlessly redirected to your local instance.
+
+To update the share link:
+- Go to HF Space Settings -> Variables and secrets -> Update REMOTE_URL
+"""
+
+import os
+import gradio as gr
+
+REMOTE_URL = os.environ.get("REMOTE_URL", "")
+GPU_NAME = os.environ.get("GPU_NAME", "")
+
+# Multi-instance support: REMOTE_URL as #0, REMOTE_URL_1, REMOTE_URL_2, REMOTE_URL_3
+REMOTE_URLS = []
+if REMOTE_URL:
+    name0 = os.environ.get("REMOTE_NAME", "Instance 0")
+    REMOTE_URLS.append({"url": REMOTE_URL, "name": name0})
+for i in range(1, 4):
+    url = os.environ.get(f"REMOTE_URL_{i}", "")
+    name = os.environ.get(f"REMOTE_NAME_{i}", f"Instance {i}")
+    if url:
+        REMOTE_URLS.append({"url": url, "name": name})
+
+PROXY_HTML = """
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Pixal3D | AI Image-to-3D</title>
+    <style>
+        * {{ margin: 0; padding: 0; box-sizing: border-box; }}
+        html, body {{
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: #0b0f1a;
+            color: #f1f5f9;
+            height: 100%;
+            overflow: hidden;
+            display: flex;
+            flex-direction: column;
+        }}
+        .header {{
+            padding: 8px 24px;
+            background: rgba(22, 28, 45, 0.9);
+            border-bottom: 1px solid rgba(255,255,255,0.08);
+            display: flex;
+            align-items: center;
+            gap: 16px;
+            backdrop-filter: blur(12px);
+        }}
+        .header h1 {{
+            font-size: 16px;
+            font-weight: 700;
+            background: linear-gradient(135deg, #818cf8, #10b981);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+            white-space: nowrap;
+        }}
+        .header .notice {{
+            flex: 1;
+            font-size: 12px;
+            color: #fbbf24;
+            text-align: center;
+        }}
+        .status {{
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            font-size: 12px;
+            color: #94a3b8;
+            white-space: nowrap;
+        }}
+        .status-dot {{
+            width: 7px;
+            height: 7px;
+            border-radius: 50%;
+            background: {status_color};
+            animation: {status_anim};
+        }}
+        @keyframes pulse {{
+            0%, 100% {{ opacity: 1; }}
+            50% {{ opacity: 0.4; }}
+        }}
+        .iframe-container {{
+            flex: 1;
+            position: relative;
+        }}
+        .iframe-container iframe {{
+            width: 100%;
+            height: 100%;
+            border: none;
+            position: absolute;
+            top: 0;
+            left: 0;
+        }}
+        .no-url {{
+            flex: 1;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            padding: 40px;
+        }}
+        .no-url-card {{
+            max-width: 560px;
+            background: rgba(22, 28, 45, 0.8);
+            border: 1px solid rgba(255,255,255,0.08);
+            border-radius: 16px;
+            padding: 48px;
+            text-align: center;
+        }}
+        .no-url-card h2 {{
+            font-size: 24px;
+            margin-bottom: 16px;
+        }}
+        .no-url-card p {{
+            color: #94a3b8;
+            line-height: 1.7;
+            margin-bottom: 12px;
+        }}
+        .no-url-card code {{
+            background: rgba(129, 140, 248, 0.15);
+            color: #818cf8;
+            padding: 2px 8px;
+            border-radius: 4px;
+            font-size: 13px;
+        }}
+        .cards-container {{
+            flex: 1;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+            padding: 40px;
+            overflow-y: auto;
+        }}
+        .cards-grid {{
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 28px;
+            max-width: 1000px;
+            width: 100%;
+        }}
+        .instance-card {{
+            width: 100%;
+            background: rgba(22, 28, 45, 0.8);
+            border: 1px solid rgba(255,255,255,0.08);
+            border-radius: 24px;
+            padding: 60px 48px;
+            text-align: center;
+            transition: transform 0.2s, border-color 0.2s;
+        }}
+        .instance-card:hover {{
+            transform: translateY(-4px);
+            border-color: rgba(129, 140, 248, 0.4);
+        }}
+        .instance-card h3 {{
+            font-size: 26px;
+            margin-bottom: 16px;
+            color: #f1f5f9;
+        }}
+        .queue-status {{
+            display: inline-flex;
+            align-items: center;
+            gap: 8px;
+            padding: 8px 16px;
+            border-radius: 20px;
+            font-size: 15px;
+            font-weight: 600;
+            margin-bottom: 8px;
+            background: rgba(148, 163, 184, 0.1);
+            color: #94a3b8;
+        }}
+        .queue-status.idle {{
+            background: rgba(16, 185, 129, 0.15);
+            color: #10b981;
+        }}
+        .queue-status.busy {{
+            background: rgba(251, 146, 60, 0.15);
+            color: #fb923c;
+        }}
+        .queue-status.offline {{
+            background: rgba(239, 68, 68, 0.15);
+            color: #ef4444;
+        }}
+        .queue-dot {{
+            width: 8px;
+            height: 8px;
+            border-radius: 50%;
+            background: currentColor;
+            animation: pulse 2s infinite;
+        }}
+        .instance-card .url-hint {{
+            font-size: 13px;
+            color: #64748b;
+            margin-top: 18px;
+            word-break: break-all;
+        }}
+        .instance-card .btn-go {{
+            display: inline-block;
+            margin-top: 24px;
+            padding: 16px 44px;
+            background: linear-gradient(135deg, #818cf8, #10b981);
+            color: #ffffff !important;
+            border-radius: 12px;
+            text-decoration: none !important;
+            font-weight: 700;
+            font-size: 18px;
+            transition: opacity 0.2s;
+        }}
+        .instance-card .btn-go:hover {{
+            opacity: 0.85;
+            text-decoration: none !important;
+        }}
+        .instance-card .btn-go:hover {{
+            opacity: 0.85;
+        }}
+        .link-bar {{
+            padding: 8px 24px;
+            background: rgba(16, 185, 129, 0.08);
+            border-top: 1px solid rgba(16, 185, 129, 0.2);
+            font-size: 12px;
+            color: #94a3b8;
+            text-align: center;
+        }}
+        .link-bar a {{
+            color: #10b981;
+            text-decoration: none;
+        }}
+        .link-bar a:hover {{ text-decoration: underline; }}
+    </style>
+</head>
+<body>
+    <div class="header">
+        <h1>Pixal3D</h1>
+        <span class="notice"></span>
+        <div class="status">
+            <div class="status-dot"></div>
+            <span>{status_text}</span>
+        </div>
+    </div>
+    {content}
+</body>
+</html>
+"""
+
+
+def build_page():
+    # If multi-instance URLs are configured, show cards
+    if REMOTE_URLS:
+        status_color = "#10b981"
+        status_anim = "pulse 2s infinite"
+        status_text = f"{len(REMOTE_URLS)} instance(s) available"
+        
+        cards_html = ""
+        for i, inst in enumerate(REMOTE_URLS):
+            cards_html += f"""
+            <div class="instance-card">
+                <h3>🖥️ {inst['name']}</h3>
+                <p style="color:#94a3b8; font-size:14px; margin-bottom:8px;">⚡ Shared GPU — requests are queued</p>
+                <div class="queue-status" id="queue-status-{i}">
+                    <span class="queue-dot"></span>
+                    <span id="queue-text-{i}">Checking...</span>
+                </div>
+                <a href="{inst['url']}" target="_blank" rel="noopener noreferrer" class="btn-go">
+                    Open Instance {i}
+                </a>
+                <p class="url-hint"><code>{inst['url']}</code></p>
+            </div>
+            """
+        
+        # Build JS array of instance URLs for direct polling (Gradio share links support CORS natively)
+        urls_js = ", ".join(['"' + inst["url"].rstrip("/") + '"' for inst in REMOTE_URLS])
+        
+        content = f"""
+        <div class="cards-container">
+            <div style="width:100%; text-align:center; margin-bottom:16px;">
+                <h2 style="font-size:28px; margin-bottom:12px;">🚀 Choose a Pixal3D Instance</h2>
+                <p style="color:#fbbf24; font-size:15px; margin-bottom:8px;">⚠️ Due to a temporary HuggingFace error, this Space is currently unavailable. Please use one of the instances below.</p>
+                <p style="color:#10b981; font-size:14px; margin-top:10px; font-weight:600;">💡 Choose the instance with the shortest queue!</p>
+            </div>
+            <div class="cards-grid">
+                {cards_html}
+            </div>
+        </div>
+        """
+        
+        poll_script = f"""
+            const INSTANCE_URLS = [{urls_js}];
+            async function pollQueues() {{
+                for (let i = 0; i < INSTANCE_URLS.length; i++) {{
+                    try {{
+                        const controller = new AbortController();
+                        const timeout = setTimeout(() => controller.abort(), 5000);
+                        const resp = await fetch(INSTANCE_URLS[i] + '/queue?session_id=', {{
+                            signal: controller.signal
+                        }});
+                        clearTimeout(timeout);
+                        if (resp.ok) {{
+                            const data = await resp.json();
+                            const total = data.total_waiting + (data.gpu_busy ? 1 : 0);
+                            const el = document.getElementById('queue-text-' + i);
+                            const status = document.getElementById('queue-status-' + i);
+                            if (total === 0) {{
+                                el.textContent = 'Idle — no queue';
+                                status.className = 'queue-status idle';
+                            }} else {{
+                                el.textContent = total + ' in queue';
+                                status.className = 'queue-status busy';
+                            }}
+                        }} else {{
+                            const el = document.getElementById('queue-text-' + i);
+                            const status = document.getElementById('queue-status-' + i);
+                            if (el) {{
+                                el.textContent = 'Offline';
+                                status.className = 'queue-status offline';
+                            }}
+                        }}
+                    }} catch (e) {{
+                        const el = document.getElementById('queue-text-' + i);
+                        const status = document.getElementById('queue-status-' + i);
+                        if (el) {{
+                            el.textContent = 'Offline';
+                            status.className = 'queue-status offline';
+                        }}
+                    }}
+                }}
+            }}
+            pollQueues();
+            setInterval(pollQueues, 5000);
+        """
+    else:
+        status_color = "#ef4444"
+        status_anim = "pulse 1.5s infinite"
+        status_text = "Remote instance not configured"
+        poll_script = ""
+        content = """
+        <div class="no-url">
+            <div class="no-url-card">
+                <h2>⚡ Remote GPU Instance Not Connected</h2>
+                <p>This Space acts as a proxy to a locally-deployed Pixal3D instance running on a dedicated GPU.</p>
+                <p>To connect, set the <code>REMOTE_URL</code> secret in this Space's settings to your Gradio share link.</p>
+                <p style="margin-top:24px; font-size:13px;">
+                    Example: <code>https://abcdef123456.gradio.live</code>
+                </p>
+            </div>
+        </div>
+        """
+
+    html = PROXY_HTML.format(
+        status_color=status_color,
+        status_anim=status_anim,
+        status_text=status_text,
+        gpu_name=GPU_NAME,
+        content=content,
+    )
+    return html, poll_script
+
+
+# Use a simple Gradio Blocks app with HTML component
+page_html, page_script = build_page()
+
+with gr.Blocks(
+    title="Pixal3D | AI Image-to-3D",
+    css="footer {display:none !important;} .gradio-container {padding:0 !important; max-width:100% !important; height:100vh !important; overflow:hidden !important;} #proxy-frame {height:100%; max-height:100vh; padding:0; overflow:hidden;}",
+    theme=gr.themes.Base(),
+) as demo:
+    gr.HTML(page_html, elem_id="proxy-frame", js_on_load=page_script if page_script else None)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

app_bak.py

@@ -0,0 +1,493 @@
+import os
+import subprocess
+import argparse
+import math
+import time
+import shutil
+import cv2
+import torch
+import numpy as np
+import base64
+import io
+import json
+from datetime import datetime
+from typing import *
+from PIL import Image
+
+import threading
+try:
+    import nest_asyncio
+    nest_asyncio.apply()
+except ImportError:
+    pass
+
+# Lock for model initialization
+init_lock = threading.Lock()
+
+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'
+
+import spaces
+from gradio import Server
+from gradio.data_classes import FileData
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+
+from trellis2.modules.sparse import SparseTensor
+from trellis2.pipelines import Pixal3DImageTo3DPipeline
+from trellis2.renderers import EnvMap
+from trellis2.utils import render_utils
+import o_voxel
+
+# ============================================================================
+# Constants & Defaults
+# ============================================================================
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+os.makedirs(TMP_DIR, exist_ok=True)
+
+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
+
+# Cascade parameters
+CASCADE_LR_RESOLUTION = 512
+CASCADE_MAX_NUM_TOKENS = 49152
+
+# MoGe defaults
+MOGE_MODEL_NAME = "Ruicheng/moge-2-vitl"
+WILD_MESH_SCALE = 1.0
+WILD_EXTEND_PIXEL = 0
+WILD_IMAGE_RESOLUTION = 512
+
+# Image Cond Model configs
+IMAGE_COND_CONFIGS = {
+    "ss": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 512,
+        "grid_resolution": 16,
+    },
+    "shape_512": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 512,
+        "grid_resolution": 32,
+        "use_naf_upsample": True,
+        "naf_target_size": 512,
+    },
+    "shape_1024": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 1024,
+        "grid_resolution": 64,
+        "use_naf_upsample": True,
+        "naf_target_size": 512,
+    },
+    "tex_1024": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 1024,
+        "grid_resolution": 64,
+        "use_naf_upsample": True,
+        "naf_target_size": 1024,
+    },
+}
+
+# ============================================================================
+# Model Loading
+# ============================================================================
+
+def build_image_cond_model(config: dict):
+    from trellis2.trainers.flow_matching.mixins.image_conditioned_proj import DinoV3ProjFeatureExtractor
+    model = DinoV3ProjFeatureExtractor(**config)
+    model.eval()
+    return model
+
+def load_moge_model(device="cuda", model_name=MOGE_MODEL_NAME):
+    from moge.model.v2 import MoGeModel
+    moge_model = MoGeModel.from_pretrained(model_name).to(device)
+    moge_model.eval()
+    return moge_model
+
+# Global instances (lazy loaded or loaded at start)
+pipeline = None
+moge_model = None
+envmap = None
+
+def init_models():
+    global pipeline, moge_model, envmap
+    with init_lock:
+        if pipeline is not None:
+            return
+
+        # GPU / CUDA Diagnostics (runs when GPU is allocated)
+        import subprocess as _sp
+        print("=" * 60)
+        print("[Diagnostics] PyTorch version:", torch.__version__)
+        print("[Diagnostics] CUDA available:", torch.cuda.is_available())
+        if torch.cuda.is_available():
+            print("[Diagnostics] CUDA version:", torch.version.cuda)
+            print("[Diagnostics] cuDNN version:", torch.backends.cudnn.version())
+            for i in range(torch.cuda.device_count()):
+                name = torch.cuda.get_device_name(i)
+                cap = torch.cuda.get_device_capability(i)
+                mem = torch.cuda.get_device_properties(i).total_memory / 1024**3
+                print(f"[Diagnostics] GPU {i}: {name}, sm_{cap[0]}{cap[1]}, {mem:.1f} GB")
+        try:
+            res = _sp.run(["nvidia-smi", "--query-gpu=name,compute_cap,memory.total", "--format=csv,noheader"], capture_output=True, text=True, timeout=10)
+            print("[Diagnostics] nvidia-smi:", res.stdout.strip())
+        except Exception as e:
+            print(f"[Diagnostics] nvidia-smi failed: {e}")
+        print("=" * 60)
+
+        model_path = "TencentARC/Pixal3D-T"
+        print(f"[Pipeline] Loading from {model_path}...")
+        pipeline = Pixal3DImageTo3DPipeline.from_pretrained(model_path)
+        
+        print("[ImageCond] Building DinoV3ProjFeatureExtractor models...")
+        pipeline.image_cond_model_ss = build_image_cond_model(IMAGE_COND_CONFIGS["ss"])
+        pipeline.image_cond_model_shape_512 = build_image_cond_model(IMAGE_COND_CONFIGS["shape_512"])
+        pipeline.image_cond_model_shape_1024 = build_image_cond_model(IMAGE_COND_CONFIGS["shape_1024"])
+        pipeline.image_cond_model_tex_1024 = build_image_cond_model(IMAGE_COND_CONFIGS["tex_1024"])
+        
+        pipeline.low_vram = False
+        pipeline.cuda()
+        
+        # Ensure image_cond_models are on GPU
+        pipeline.image_cond_model_ss.cuda()
+        pipeline.image_cond_model_shape_512.cuda()
+        pipeline.image_cond_model_shape_1024.cuda()
+        pipeline.image_cond_model_tex_1024.cuda()
+        
+        print("[NAF] Pre-loading NAF upsampler model...")
+        for attr in ['image_cond_model_ss', 'image_cond_model_shape_512', 'image_cond_model_shape_1024', 'image_cond_model_tex_1024']:
+            model = getattr(pipeline, attr, None)
+            if model is not None and getattr(model, 'use_naf_upsample', False):
+                model._load_naf()
+                
+        print("[MoGe-2] Loading model for camera estimation...")
+        moge_model = load_moge_model(device="cuda")
+        
+        print("[EnvMap] Loading environment maps...")
+        _base = os.path.dirname(os.path.abspath(__file__))
+        envmap = {
+            'forest': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/forest.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+            'sunset': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/sunset.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+            'courtyard': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/courtyard.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+        }
+
+# ============================================================================
+# Utilities
+# ============================================================================
+
+def compute_f_pixels(camera_angle_x: float, resolution: int) -> float:
+    focal_length = 16.0 / torch.tan(torch.tensor(camera_angle_x / 2.0))
+    f_pixels = focal_length * resolution / 32.0
+    return float(f_pixels.item())
+
+def distance_from_fov(camera_angle_x, grid_point, target_point, mesh_scale, image_resolution):
+    rotation_matrix = torch.tensor([[1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
+    gp = grid_point.to(torch.float32) @ rotation_matrix.T
+    gp = gp / mesh_scale / 2
+    xw, yw, zw = gp[0].item(), gp[1].item(), gp[2].item()
+    xt, yt = float(target_point[0].item()), float(target_point[1].item())
+    f_pixels = compute_f_pixels(camera_angle_x, image_resolution)
+    x_ndc = xt - image_resolution / 2.0
+    y_ndc = -(yt - image_resolution / 2.0)
+    distance_x = f_pixels * xw / x_ndc - yw
+    return {"distance_from_x": float(distance_x), "f_pixels": float(f_pixels)}
+
+def get_camera_params_wild_moge(image_path, device="cuda", mesh_scale=1.0, extend_pixel=0, image_resolution=512):
+    pil_image = Image.open(image_path).convert("RGB")
+    width, height = pil_image.size
+    image_np = np.array(pil_image).astype(np.float32) / 255.0
+    image_tensor = torch.from_numpy(image_np).permute(2, 0, 1).to(device)
+    with torch.no_grad():
+        output = moge_model.infer(image_tensor)
+    intrinsics = output["intrinsics"].squeeze().cpu().numpy()
+    fx_normalized = intrinsics[0, 0]
+    fx = fx_normalized * width
+    camera_angle_x = 2 * math.atan(width / (2 * fx))
+
+    grid_point = torch.tensor([-1.0, 0.0, 0.0])
+    distance = distance_from_fov(
+        camera_angle_x, grid_point,
+        torch.tensor([0 - extend_pixel, image_resolution - 1 + extend_pixel]),
+        mesh_scale, image_resolution
+    )["distance_from_x"]
+    return {'camera_angle_x': camera_angle_x, 'distance': distance, 'mesh_scale': mesh_scale}
+
+def pack_state(shape_slat, tex_slat, res):
+    state_data = {
+        'shape_slat_feats': shape_slat.feats.cpu().numpy(),
+        'tex_slat_feats': tex_slat.feats.cpu().numpy(),
+        'coords': shape_slat.coords.cpu().numpy(),
+        'res': res,
+    }
+    import random
+    state_path = os.path.join(TMP_DIR, f"state_{int(time.time()*1000)}_{random.randint(0,9999):04d}.npz")
+    np.savez_compressed(state_path, **state_data)
+    return state_path
+
+def unpack_state(state_path):
+    data = np.load(state_path)
+    shape_slat = SparseTensor(
+        feats=torch.from_numpy(data['shape_slat_feats']).cuda(),
+        coords=torch.from_numpy(data['coords']).cuda(),
+    )
+    tex_slat = shape_slat.replace(torch.from_numpy(data['tex_slat_feats']).cuda())
+    return shape_slat, tex_slat, int(data['res'])
+
+# ============================================================================
+# Progress Tracking (file-based, cross-process safe for @spaces.GPU)
+# ============================================================================
+
+import asyncio
+from fastapi.responses import JSONResponse
+from fastapi import Request
+
+PROGRESS_DIR = os.path.join(TMP_DIR, '_progress')
+os.makedirs(PROGRESS_DIR, exist_ok=True)
+
+_thread_local = threading.local()
+
+def _progress_file(session_id: str) -> str:
+    """Return path to a session's progress JSON file."""
+    return os.path.join(PROGRESS_DIR, f"{session_id}.json")
+
+def _reset_progress(session_id: str):
+    _thread_local.active_session = session_id
+    _write_progress_file(session_id, {"stage": "Initializing...", "step": 0, "total": 0, "done": False})
+
+def _update_progress(stage: str, step: int, total: int):
+    session_id = getattr(_thread_local, 'active_session', '')
+    if session_id:
+        _write_progress_file(session_id, {"stage": stage, "step": step, "total": total, "done": False})
+
+def _finish_progress():
+    session_id = getattr(_thread_local, 'active_session', '')
+    if session_id:
+        _write_progress_file(session_id, {"done": True})
+
+def _write_progress_file(session_id: str, data: dict):
+    """Atomically write progress JSON to a file (cross-process safe)."""
+    path = _progress_file(session_id)
+    tmp_path = path + ".tmp"
+    try:
+        with open(tmp_path, 'w') as f:
+            json.dump(data, f)
+        os.replace(tmp_path, path)  # atomic on POSIX
+    except Exception:
+        pass
+
+# Monkey-patch tqdm to intercept progress
+import tqdm as _tqdm_module
+
+_original_tqdm = _tqdm_module.tqdm
+
+class _TqdmProgressInterceptor(_original_tqdm):
+    """Wraps tqdm to push progress updates to SSE."""
+    def __init__(self, *args, **kwargs):
+        self._stage_desc = kwargs.get('desc', 'Processing')
+        super().__init__(*args, **kwargs)
+    
+    def set_description(self, desc=None, refresh=True):
+        self._stage_desc = desc or 'Processing'
+        super().set_description(desc, refresh)
+    
+    def update(self, n=1):
+        super().update(n)
+        _update_progress(self._stage_desc, self.n, self.total or 0)
+
+# Patch tqdm globally
+_tqdm_module.tqdm = _TqdmProgressInterceptor
+# Also patch the direct import in the sampler module and render_utils
+import trellis2.pipelines.samplers.flow_euler as _fe_module
+_fe_module.tqdm = _TqdmProgressInterceptor
+import trellis2.utils.render_utils as _ru_module
+_ru_module.tqdm = _TqdmProgressInterceptor
+import o_voxel.postprocess as _ovp_module
+_ovp_module.tqdm = _TqdmProgressInterceptor
+
+# ============================================================================
+# API Implementation
+# ============================================================================
+
+app = Server()
+
+@app.get("/")
+async def homepage():
+    html_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "index.html")
+    with open(html_path, "r", encoding="utf-8") as f:
+        return HTMLResponse(content=f.read())
+
+@app.get("/progress")
+async def progress_poll(request: Request):
+    """Polling endpoint for real-time progress updates during generation."""
+    session_id = request.query_params.get("session_id", "")
+    path = _progress_file(session_id)
+    try:
+        with open(path, 'r') as f:
+            data = json.load(f)
+        return JSONResponse(data)
+    except (FileNotFoundError, json.JSONDecodeError):
+        return JSONResponse({"stage": "Waiting...", "step": 0, "total": 0, "done": False})
+
+@app.api()
+@spaces.GPU(duration=30)
+def preprocess(image: FileData) -> FileData:
+    init_models()
+    img = Image.open(image["path"])
+    processed = pipeline.preprocess_image(img)
+    out_path = os.path.join(TMP_DIR, f"preprocessed_{int(time.time()*1000)}.png")
+    processed.save(out_path)
+    return FileData(path=out_path)
+
+@app.api()
+@spaces.GPU(duration=120)
+def generate_3d(
+    image: FileData, 
+    seed: int, 
+    resolution: int,
+    ss_guidance_strength: float = 7.5,
+    ss_guidance_rescale: float = 0.7,
+    ss_sampling_steps: int = 12,
+    ss_rescale_t: float = 5.0,
+    shape_slat_guidance_strength: float = 7.5,
+    shape_slat_guidance_rescale: float = 0.5,
+    shape_slat_sampling_steps: int = 12,
+    shape_slat_rescale_t: float = 3.0,
+    tex_slat_guidance_strength: float = 1.0,
+    tex_slat_guidance_rescale: float = 0.0,
+    tex_slat_sampling_steps: int = 12,
+    tex_slat_rescale_t: float = 3.0,
+    session_id: str = "",
+) -> Dict:
+    init_models()
+    _reset_progress(session_id)
+    _update_progress("Preprocessing & Camera Estimation", 0, 1)
+    
+    torch.manual_seed(seed)
+    hr_resolution = int(resolution)
+    
+    img = Image.open(image["path"])
+    # Image is already preprocessed by /preprocess endpoint, use directly
+    image_preprocessed = img
+    temp_processed_path = os.path.join(TMP_DIR, f"temp_proc_{session_id[:8]}_{int(time.time()*1000)}.png")
+    image_preprocessed.save(temp_processed_path)
+    
+    camera_params = get_camera_params_wild_moge(
+        temp_processed_path, device="cuda",
+        mesh_scale=WILD_MESH_SCALE, extend_pixel=WILD_EXTEND_PIXEL,
+        image_resolution=WILD_IMAGE_RESOLUTION,
+    )
+    _update_progress("Preprocessing & Camera Estimation", 1, 1)
+    
+    ss_sampler_override = {"steps": ss_sampling_steps, "guidance_strength": ss_guidance_strength,
+                           "guidance_rescale": ss_guidance_rescale, "rescale_t": ss_rescale_t}
+    shape_sampler_override = {"steps": shape_slat_sampling_steps, "guidance_strength": shape_slat_guidance_strength,
+                              "guidance_rescale": shape_slat_guidance_rescale, "rescale_t": shape_slat_rescale_t}
+    tex_sampler_override = {"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 = f"{hr_resolution}_cascade"
+    mesh_list, (shape_slat, tex_slat, res) = pipeline.run(
+        image_preprocessed,
+        camera_params=camera_params,
+        seed=seed,
+        sparse_structure_sampler_params=ss_sampler_override,
+        shape_slat_sampler_params=shape_sampler_override,
+        tex_slat_sampler_params=tex_sampler_override,
+        preprocess_image=False,
+        return_latent=True,
+        pipeline_type=pipeline_type,
+        max_num_tokens=CASCADE_MAX_NUM_TOKENS,
+    )
+    
+    mesh = mesh_list[0]
+    state_path = pack_state(shape_slat, tex_slat, res)
+    
+    _update_progress("Rendering views", 0, 1)
+    mesh.simplify(16777216)
+    cam_dist = camera_params['distance']
+    near = max(0.01, cam_dist - 2.0)
+    far = cam_dist + 10.0
+    renders = render_utils.render_proj_aligned_video(
+        mesh, camera_angle_x=camera_params['camera_angle_x'],
+        distance=cam_dist, resolution=1024,
+        num_frames=STEPS, envmap=envmap,
+        near=near, far=far,
+    )
+    _update_progress("Rendering views", 1, 1)
+    
+    # Save renders and return paths
+    render_files = {}
+    for mode_key, frames in renders.items():
+        mode_files = []
+        for i, frame in enumerate(frames):
+            p = os.path.abspath(os.path.join(TMP_DIR, f"render_{mode_key}_{i}_{int(time.time()*1000)}.jpg"))
+            Image.fromarray(frame).save(p, quality=85)
+            mode_files.append(FileData(path=p))
+        render_files[mode_key] = mode_files
+
+    _finish_progress()
+    return {
+        "render_paths": render_files,
+        "state_path": os.path.abspath(state_path),
+        "camera_angle_x": camera_params['camera_angle_x'],
+        "distance": camera_params['distance'],
+    }
+
+@app.api()
+@spaces.GPU(duration=240)
+def extract_glb_api(state_path: str, decimation_target: int, texture_size: int, session_id: str = "") -> FileData:
+    init_models()
+    _reset_progress(session_id)
+    _update_progress("Decoding latent", 0, 1)
+    
+    shape_slat, tex_slat, res = unpack_state(state_path)
+    mesh = pipeline.decode_latent(shape_slat, tex_slat, res)[0]
+    _update_progress("Decoding latent", 1, 1)
+    
+    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,
+    )
+    rot = np.array([
+        [-1,  0,  0,  0],
+        [ 0,  0, -1,  0],
+        [ 0, -1,  0,  0],
+        [ 0,  0,  0,  1],
+    ], dtype=np.float64)
+    glb.apply_transform(rot)
+    
+    out_glb = os.path.join(TMP_DIR, f"result_{int(time.time()*1000)}.glb")
+    glb.export(out_glb, extension_webp=True)
+    _finish_progress()
+    return FileData(path=out_glb)
+
+# Mount assets and tmp for direct access
+app.mount("/assets", StaticFiles(directory="assets"), name="assets")
+app.mount("/tmp", StaticFiles(directory=TMP_DIR), name="tmp")
+
+if __name__ == "__main__":
+    # Re-install utils3d as in original app.py
+    subprocess.run([
+        "pip", "install", "--force-reinstall", "--no-deps",
+        "https://github.com/LDYang694/Storages/releases/download/20260430/utils3d-0.0.2-py3-none-any.whl"
+    ], check=True)
+    
+    # Pre-initialize models before launching the server
+    init_models()
+    
+    app.launch(show_error=True, share=True)

app_local.py

@@ -0,0 +1,597 @@
+import os
+import subprocess
+import argparse
+import math
+import time
+import shutil
+import cv2
+import torch
+import numpy as np
+import base64
+import io
+import json
+from datetime import datetime
+from typing import *
+from PIL import Image
+
+import threading
+from contextlib import contextmanager
+try:
+    import nest_asyncio
+    nest_asyncio.apply()
+except ImportError:
+    pass
+
+# Lock for model initialization
+init_lock = threading.Lock()
+# Lock for serializing GPU inference (one user at a time)
+inference_lock = threading.Lock()
+# Queue tracking
+_queue_lock = threading.Lock()
+_queue_running_session = ""  # session_id of the currently running request
+_queue_start_time = 0.0  # when the current request started
+_pending_sessions: list = []  # ordered list of ALL session_ids waiting
+_pending_times: dict = {}  # session_id -> registration timestamp
+_PENDING_TIMEOUT = 600  # auto-remove after 10 minutes (safety net)
+
+@contextmanager
+def acquire_inference(session_id: str = ""):
+    """Context manager that serializes GPU access and tracks queue state."""
+    global _queue_running_session, _queue_start_time
+    # Register in pending list BEFORE waiting for lock
+    with _queue_lock:
+        if session_id and session_id not in _pending_sessions:
+            _pending_sessions.append(session_id)
+            _pending_times[session_id] = time.time()
+    try:
+        with inference_lock:
+            with _queue_lock:
+                if session_id and session_id in _pending_sessions:
+                    _pending_sessions.remove(session_id)
+                _pending_times.pop(session_id, None)
+                _queue_running_session = session_id
+                _queue_start_time = time.time()
+            try:
+                yield
+            finally:
+                with _queue_lock:
+                    _queue_running_session = ""
+                    _queue_start_time = 0.0
+    except BaseException:
+        with _queue_lock:
+            if session_id and session_id in _pending_sessions:
+                _pending_sessions.remove(session_id)
+            _pending_times.pop(session_id, None)
+        raise
+
+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'
+
+try:
+    import spaces
+except ImportError:
+    # Local deployment: create a no-op decorator
+    class _FakeSpaces:
+        @staticmethod
+        def GPU(*args, **kwargs):
+            def decorator(fn):
+                return fn
+            return decorator
+    spaces = _FakeSpaces()
+from gradio import Server
+from gradio.data_classes import FileData
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+
+from trellis2.modules.sparse import SparseTensor
+from trellis2.pipelines import Pixal3DImageTo3DPipeline
+from trellis2.renderers import EnvMap
+from trellis2.utils import render_utils
+import o_voxel
+
+# ============================================================================
+# Constants & Defaults
+# ============================================================================
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+os.makedirs(TMP_DIR, exist_ok=True)
+
+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
+
+# Cascade parameters
+CASCADE_LR_RESOLUTION = 512
+CASCADE_MAX_NUM_TOKENS = 49152
+
+# MoGe defaults
+MOGE_MODEL_NAME = "Ruicheng/moge-2-vitl"
+WILD_MESH_SCALE = 1.0
+WILD_EXTEND_PIXEL = 0
+WILD_IMAGE_RESOLUTION = 512
+
+# Image Cond Model configs
+IMAGE_COND_CONFIGS = {
+    "ss": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 512,
+        "grid_resolution": 16,
+    },
+    "shape_512": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 512,
+        "grid_resolution": 32,
+        "use_naf_upsample": True,
+        "naf_target_size": 512,
+    },
+    "shape_1024": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 1024,
+        "grid_resolution": 64,
+        "use_naf_upsample": True,
+        "naf_target_size": 512,
+    },
+    "tex_1024": {
+        "model_name": "camenduru/dinov3-vitl16-pretrain-lvd1689m",
+        "image_size": 1024,
+        "grid_resolution": 64,
+        "use_naf_upsample": True,
+        "naf_target_size": 1024,
+    },
+}
+
+# ============================================================================
+# Model Loading
+# ============================================================================
+
+def build_image_cond_model(config: dict):
+    from trellis2.trainers.flow_matching.mixins.image_conditioned_proj import DinoV3ProjFeatureExtractor
+    model = DinoV3ProjFeatureExtractor(**config)
+    model.eval()
+    return model
+
+def load_moge_model(device="cuda", model_name=MOGE_MODEL_NAME):
+    from moge.model.v2 import MoGeModel
+    moge_model = MoGeModel.from_pretrained(model_name).to(device)
+    moge_model.eval()
+    return moge_model
+
+# Global instances (lazy loaded or loaded at start)
+pipeline = None
+moge_model = None
+envmap = None
+
+def init_models():
+    global pipeline, moge_model, envmap
+    with init_lock:
+        if pipeline is not None:
+            return
+
+        # GPU / CUDA Diagnostics (runs when GPU is allocated)
+        import subprocess as _sp
+        print("=" * 60)
+        print("[Diagnostics] PyTorch version:", torch.__version__)
+        print("[Diagnostics] CUDA available:", torch.cuda.is_available())
+        if torch.cuda.is_available():
+            print("[Diagnostics] CUDA version:", torch.version.cuda)
+            print("[Diagnostics] cuDNN version:", torch.backends.cudnn.version())
+            for i in range(torch.cuda.device_count()):
+                name = torch.cuda.get_device_name(i)
+                cap = torch.cuda.get_device_capability(i)
+                mem = torch.cuda.get_device_properties(i).total_memory / 1024**3
+                print(f"[Diagnostics] GPU {i}: {name}, sm_{cap[0]}{cap[1]}, {mem:.1f} GB")
+        try:
+            res = _sp.run(["nvidia-smi", "--query-gpu=name,compute_cap,memory.total", "--format=csv,noheader"], capture_output=True, text=True, timeout=10)
+            print("[Diagnostics] nvidia-smi:", res.stdout.strip())
+        except Exception as e:
+            print(f"[Diagnostics] nvidia-smi failed: {e}")
+        print("=" * 60)
+
+        model_path = "TencentARC/Pixal3D-T"
+        print(f"[Pipeline] Loading from {model_path}...")
+        pipeline = Pixal3DImageTo3DPipeline.from_pretrained(model_path)
+        
+        print("[ImageCond] Building DinoV3ProjFeatureExtractor models...")
+        pipeline.image_cond_model_ss = build_image_cond_model(IMAGE_COND_CONFIGS["ss"])
+        pipeline.image_cond_model_shape_512 = build_image_cond_model(IMAGE_COND_CONFIGS["shape_512"])
+        pipeline.image_cond_model_shape_1024 = build_image_cond_model(IMAGE_COND_CONFIGS["shape_1024"])
+        pipeline.image_cond_model_tex_1024 = build_image_cond_model(IMAGE_COND_CONFIGS["tex_1024"])
+        
+        pipeline.low_vram = False
+        pipeline.cuda()
+        
+        # Ensure image_cond_models are on GPU
+        pipeline.image_cond_model_ss.cuda()
+        pipeline.image_cond_model_shape_512.cuda()
+        pipeline.image_cond_model_shape_1024.cuda()
+        pipeline.image_cond_model_tex_1024.cuda()
+        
+        print("[NAF] Pre-loading NAF upsampler model...")
+        for attr in ['image_cond_model_ss', 'image_cond_model_shape_512', 'image_cond_model_shape_1024', 'image_cond_model_tex_1024']:
+            model = getattr(pipeline, attr, None)
+            if model is not None and getattr(model, 'use_naf_upsample', False):
+                model._load_naf()
+                
+        print("[MoGe-2] Loading model for camera estimation...")
+        moge_model = load_moge_model(device="cuda")
+        
+        print("[EnvMap] Loading environment maps...")
+        _base = os.path.dirname(os.path.abspath(__file__))
+        envmap = {
+            'forest': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/forest.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+            'sunset': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/sunset.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+            'courtyard': EnvMap(torch.tensor(cv2.cvtColor(cv2.imread(os.path.join(_base, 'assets/hdri/courtyard.exr'), cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda')),
+        }
+
+# ============================================================================
+# Utilities
+# ============================================================================
+
+def compute_f_pixels(camera_angle_x: float, resolution: int) -> float:
+    focal_length = 16.0 / torch.tan(torch.tensor(camera_angle_x / 2.0))
+    f_pixels = focal_length * resolution / 32.0
+    return float(f_pixels.item())
+
+def distance_from_fov(camera_angle_x, grid_point, target_point, mesh_scale, image_resolution):
+    rotation_matrix = torch.tensor([[1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
+    gp = grid_point.to(torch.float32) @ rotation_matrix.T
+    gp = gp / mesh_scale / 2
+    xw, yw, zw = gp[0].item(), gp[1].item(), gp[2].item()
+    xt, yt = float(target_point[0].item()), float(target_point[1].item())
+    f_pixels = compute_f_pixels(camera_angle_x, image_resolution)
+    x_ndc = xt - image_resolution / 2.0
+    y_ndc = -(yt - image_resolution / 2.0)
+    distance_x = f_pixels * xw / x_ndc - yw
+    return {"distance_from_x": float(distance_x), "f_pixels": float(f_pixels)}
+
+def get_camera_params_wild_moge(image_path, device="cuda", mesh_scale=1.0, extend_pixel=0, image_resolution=512):
+    pil_image = Image.open(image_path).convert("RGB")
+    width, height = pil_image.size
+    image_np = np.array(pil_image).astype(np.float32) / 255.0
+    image_tensor = torch.from_numpy(image_np).permute(2, 0, 1).to(device)
+    with torch.no_grad():
+        output = moge_model.infer(image_tensor)
+    intrinsics = output["intrinsics"].squeeze().cpu().numpy()
+    fx_normalized = intrinsics[0, 0]
+    fx = fx_normalized * width
+    camera_angle_x = 2 * math.atan(width / (2 * fx))
+
+    grid_point = torch.tensor([-1.0, 0.0, 0.0])
+    distance = distance_from_fov(
+        camera_angle_x, grid_point,
+        torch.tensor([0 - extend_pixel, image_resolution - 1 + extend_pixel]),
+        mesh_scale, image_resolution
+    )["distance_from_x"]
+    return {'camera_angle_x': camera_angle_x, 'distance': distance, 'mesh_scale': mesh_scale}
+
+def pack_state(shape_slat, tex_slat, res):
+    state_data = {
+        'shape_slat_feats': shape_slat.feats.cpu().numpy(),
+        'tex_slat_feats': tex_slat.feats.cpu().numpy(),
+        'coords': shape_slat.coords.cpu().numpy(),
+        'res': res,
+    }
+    import random
+    state_path = os.path.join(TMP_DIR, f"state_{int(time.time()*1000)}_{random.randint(0,9999):04d}.npz")
+    np.savez_compressed(state_path, **state_data)
+    return state_path
+
+def unpack_state(state_path):
+    data = np.load(state_path)
+    shape_slat = SparseTensor(
+        feats=torch.from_numpy(data['shape_slat_feats']).cuda(),
+        coords=torch.from_numpy(data['coords']).cuda(),
+    )
+    tex_slat = shape_slat.replace(torch.from_numpy(data['tex_slat_feats']).cuda())
+    return shape_slat, tex_slat, int(data['res'])
+
+# ============================================================================
+# Progress Tracking (file-based, cross-process safe for @spaces.GPU)
+# ============================================================================
+
+import asyncio
+from fastapi.responses import JSONResponse
+from fastapi import Request
+
+PROGRESS_DIR = os.path.join(TMP_DIR, '_progress')
+os.makedirs(PROGRESS_DIR, exist_ok=True)
+
+_thread_local = threading.local()
+
+def _progress_file(session_id: str) -> str:
+    """Return path to a session's progress JSON file."""
+    return os.path.join(PROGRESS_DIR, f"{session_id}.json")
+
+def _reset_progress(session_id: str):
+    _thread_local.active_session = session_id
+    _write_progress_file(session_id, {"stage": "Initializing...", "step": 0, "total": 0, "done": False})
+
+def _update_progress(stage: str, step: int, total: int):
+    session_id = getattr(_thread_local, 'active_session', '')
+    if session_id:
+        _write_progress_file(session_id, {"stage": stage, "step": step, "total": total, "done": False})
+
+def _finish_progress():
+    session_id = getattr(_thread_local, 'active_session', '')
+    if session_id:
+        _write_progress_file(session_id, {"done": True})
+
+def _write_progress_file(session_id: str, data: dict):
+    """Atomically write progress JSON to a file (cross-process safe)."""
+    path = _progress_file(session_id)
+    tmp_path = path + ".tmp"
+    try:
+        with open(tmp_path, 'w') as f:
+            json.dump(data, f)
+        os.replace(tmp_path, path)  # atomic on POSIX
+    except Exception:
+        pass
+
+# Monkey-patch tqdm to intercept progress
+import tqdm as _tqdm_module
+
+_original_tqdm = _tqdm_module.tqdm
+
+class _TqdmProgressInterceptor(_original_tqdm):
+    """Wraps tqdm to push progress updates to SSE."""
+    def __init__(self, *args, **kwargs):
+        self._stage_desc = kwargs.get('desc', 'Processing')
+        super().__init__(*args, **kwargs)
+    
+    def set_description(self, desc=None, refresh=True):
+        self._stage_desc = desc or 'Processing'
+        super().set_description(desc, refresh)
+    
+    def update(self, n=1):
+        super().update(n)
+        _update_progress(self._stage_desc, self.n, self.total or 0)
+
+# Patch tqdm globally
+_tqdm_module.tqdm = _TqdmProgressInterceptor
+# Also patch the direct import in the sampler module and render_utils
+import trellis2.pipelines.samplers.flow_euler as _fe_module
+_fe_module.tqdm = _TqdmProgressInterceptor
+import trellis2.utils.render_utils as _ru_module
+_ru_module.tqdm = _TqdmProgressInterceptor
+import o_voxel.postprocess as _ovp_module
+_ovp_module.tqdm = _TqdmProgressInterceptor
+
+# ============================================================================
+# API Implementation
+# ============================================================================
+
+app = Server()
+
+@app.get("/")
+async def homepage():
+    html_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "index.html")
+    with open(html_path, "r", encoding="utf-8") as f:
+        return HTMLResponse(content=f.read())
+
+@app.get("/progress")
+async def progress_poll(request: Request):
+    """Polling endpoint for real-time progress updates during generation."""
+    session_id = request.query_params.get("session_id", "")
+    path = _progress_file(session_id)
+    try:
+        with open(path, 'r') as f:
+            data = json.load(f)
+        return JSONResponse(data)
+    except (FileNotFoundError, json.JSONDecodeError):
+        return JSONResponse({"stage": "Waiting...", "step": 0, "total": 0, "done": False})
+
+@app.get("/queue/join")
+async def queue_join(request: Request):
+    """Register a session in the pending queue BEFORE the actual API call.
+    This ensures accurate queue position even when Gradio's thread pool delays dispatch."""
+    session_id = request.query_params.get("session_id", "")
+    if session_id:
+        with _queue_lock:
+            if session_id not in _pending_sessions and session_id != _queue_running_session:
+                _pending_sessions.append(session_id)
+                _pending_times[session_id] = time.time()
+    return JSONResponse({"ok": True})
+
+@app.get("/queue")
+async def queue_status(request: Request):
+    """Query queue status: how many are waiting, who is running."""
+    session_id = request.query_params.get("session_id", "")
+    now = time.time()
+    with _queue_lock:
+        # Auto-cleanup stale sessions (safety net for disconnected clients)
+        stale = [s for s in _pending_sessions if now - _pending_times.get(s, now) > _PENDING_TIMEOUT]
+        for s in stale:
+            _pending_sessions.remove(s)
+            _pending_times.pop(s, None)
+        
+        running_session = _queue_running_session
+        pending = list(_pending_sessions)  # snapshot
+        gpu_busy = bool(running_session)
+    
+    # Calculate position for the requesting session
+    # position > 0: number of tasks ahead; position == 0: currently running; position == -1: not registered
+    if session_id and session_id == running_session:
+        position = 0  # you are currently being processed
+    elif session_id and session_id in pending:
+        idx = pending.index(session_id)
+        running_count = 1 if gpu_busy else 0
+        ahead = idx + running_count
+        # If nothing is ahead (we're first and GPU free), treat as "about to start"
+        # Use position = -2 to distinguish from "not registered" (-1)
+        position = ahead if ahead > 0 else -2
+    else:
+        position = -1  # not registered yet
+
+    # Total ahead for an unregistered session (they'd join at the back)
+    total_ahead_for_unregistered = len(pending) + (1 if gpu_busy else 0)
+
+    return JSONResponse({
+        "position": position,
+        "total_waiting": len(pending),
+        "gpu_busy": gpu_busy,
+        "total_ahead_for_unregistered": total_ahead_for_unregistered,
+    })
+
+@app.api()
+@spaces.GPU(duration=30)
+def preprocess(image: FileData) -> FileData:
+    init_models()
+    img = Image.open(image["path"])
+    processed = pipeline.preprocess_image(img)
+    out_path = os.path.join(TMP_DIR, f"preprocessed_{int(time.time()*1000)}.png")
+    processed.save(out_path)
+    return FileData(path=out_path)
+
+@app.api()
+@spaces.GPU(duration=120)
+def generate_3d(
+    image: FileData, 
+    seed: int, 
+    resolution: int,
+    ss_guidance_strength: float = 7.5,
+    ss_guidance_rescale: float = 0.7,
+    ss_sampling_steps: int = 12,
+    ss_rescale_t: float = 5.0,
+    shape_slat_guidance_strength: float = 7.5,
+    shape_slat_guidance_rescale: float = 0.5,
+    shape_slat_sampling_steps: int = 12,
+    shape_slat_rescale_t: float = 3.0,
+    tex_slat_guidance_strength: float = 1.0,
+    tex_slat_guidance_rescale: float = 0.0,
+    tex_slat_sampling_steps: int = 12,
+    tex_slat_rescale_t: float = 3.0,
+    session_id: str = "",
+) -> Dict:
+    with acquire_inference(session_id):
+        init_models()
+        _reset_progress(session_id)
+        _update_progress("Preprocessing & Camera Estimation", 0, 1)
+        
+        torch.manual_seed(seed)
+        hr_resolution = int(resolution)
+        
+        img = Image.open(image["path"])
+        # Image is already preprocessed by /preprocess endpoint, use directly
+        image_preprocessed = img
+        temp_processed_path = os.path.join(TMP_DIR, f"temp_proc_{session_id[:8]}_{int(time.time()*1000)}.png")
+        image_preprocessed.save(temp_processed_path)
+        
+        camera_params = get_camera_params_wild_moge(
+            temp_processed_path, device="cuda",
+            mesh_scale=WILD_MESH_SCALE, extend_pixel=WILD_EXTEND_PIXEL,
+            image_resolution=WILD_IMAGE_RESOLUTION,
+        )
+        _update_progress("Preprocessing & Camera Estimation", 1, 1)
+        
+        ss_sampler_override = {"steps": ss_sampling_steps, "guidance_strength": ss_guidance_strength,
+                               "guidance_rescale": ss_guidance_rescale, "rescale_t": ss_rescale_t}
+        shape_sampler_override = {"steps": shape_slat_sampling_steps, "guidance_strength": shape_slat_guidance_strength,
+                                  "guidance_rescale": shape_slat_guidance_rescale, "rescale_t": shape_slat_rescale_t}
+        tex_sampler_override = {"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 = f"{hr_resolution}_cascade"
+        mesh_list, (shape_slat, tex_slat, res) = pipeline.run(
+            image_preprocessed,
+            camera_params=camera_params,
+            seed=seed,
+            sparse_structure_sampler_params=ss_sampler_override,
+            shape_slat_sampler_params=shape_sampler_override,
+            tex_slat_sampler_params=tex_sampler_override,
+            preprocess_image=False,
+            return_latent=True,
+            pipeline_type=pipeline_type,
+            max_num_tokens=CASCADE_MAX_NUM_TOKENS,
+        )
+        
+        mesh = mesh_list[0]
+        state_path = pack_state(shape_slat, tex_slat, res)
+        
+        _update_progress("Rendering views", 0, 1)
+        mesh.simplify(16777216)
+        cam_dist = camera_params['distance']
+        near = max(0.01, cam_dist - 2.0)
+        far = cam_dist + 10.0
+        renders = render_utils.render_proj_aligned_video(
+            mesh, camera_angle_x=camera_params['camera_angle_x'],
+            distance=cam_dist, resolution=1024,
+            num_frames=STEPS, envmap=envmap,
+            near=near, far=far,
+        )
+        _update_progress("Rendering views", 1, 1)
+        
+        # Save renders and return paths
+        render_files = {}
+        for mode_key, frames in renders.items():
+            mode_files = []
+            for i, frame in enumerate(frames):
+                p = os.path.abspath(os.path.join(TMP_DIR, f"render_{mode_key}_{i}_{int(time.time()*1000)}.jpg"))
+                Image.fromarray(frame).save(p, quality=85)
+                mode_files.append(FileData(path=p))
+            render_files[mode_key] = mode_files
+
+        _finish_progress()
+        return {
+            "render_paths": render_files,
+            "state_path": os.path.abspath(state_path),
+            "camera_angle_x": camera_params['camera_angle_x'],
+            "distance": camera_params['distance'],
+        }
+
+@app.api()
+@spaces.GPU(duration=240)
+def extract_glb_api(state_path: str, decimation_target: int, texture_size: int, session_id: str = "") -> FileData:
+    with acquire_inference(session_id):
+        init_models()
+        _reset_progress(session_id)
+        _update_progress("Decoding latent", 0, 1)
+        
+        shape_slat, tex_slat, res = unpack_state(state_path)
+        mesh = pipeline.decode_latent(shape_slat, tex_slat, res)[0]
+        _update_progress("Decoding latent", 1, 1)
+        
+        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,
+        )
+        rot = np.array([
+            [-1,  0,  0,  0],
+            [ 0,  0, -1,  0],
+            [ 0, -1,  0,  0],
+            [ 0,  0,  0,  1],
+        ], dtype=np.float64)
+        glb.apply_transform(rot)
+        
+        out_glb = os.path.join(TMP_DIR, f"result_{int(time.time()*1000)}.glb")
+        glb.export(out_glb, extension_webp=True)
+        _finish_progress()
+        return FileData(path=out_glb)
+
+# Mount assets and tmp for direct access
+app.mount("/assets", StaticFiles(directory="assets"), name="assets")
+app.mount("/tmp", StaticFiles(directory=TMP_DIR), name="tmp")
+
+if __name__ == "__main__":
+    # Re-install utils3d as in original app.py
+    subprocess.run([
+        "pip", "install", "--force-reinstall", "--no-deps",
+        "https://github.com/LDYang694/Storages/releases/download/20260430/utils3d-0.0.2-py3-none-any.whl"
+    ], check=True)
+    
+    # Pre-initialize models before launching the server
+    init_models()
+    
+    app.launch(show_error=True, share=True)

app_proxy.py

@@ -0,0 +1,379 @@
+"""
+Pixal3D HF Space Proxy
+======================
+This is a lightweight proxy app for HF Space that redirects users to a 
+locally deployed Gradio share link.
+
+Setup:
+1. Deploy this as your HF Space app.py
+2. Set HF Space Secret: REMOTE_URL = your local share link (e.g. https://xxxxx.gradio.live)
+3. Users visiting the HF Space will be seamlessly redirected to your local instance.
+
+To update the share link:
+- Go to HF Space Settings -> Variables and secrets -> Update REMOTE_URL
+"""
+
+import os
+import gradio as gr
+
+REMOTE_URL = os.environ.get("REMOTE_URL", "")
+GPU_NAME = os.environ.get("GPU_NAME", "")
+
+# Multi-instance support: REMOTE_URL as #0, REMOTE_URL_1, REMOTE_URL_2, REMOTE_URL_3
+REMOTE_URLS = []
+if REMOTE_URL:
+    name0 = os.environ.get("REMOTE_NAME", "Instance 0")
+    REMOTE_URLS.append({"url": REMOTE_URL, "name": name0})
+for i in range(1, 4):
+    url = os.environ.get(f"REMOTE_URL_{i}", "")
+    name = os.environ.get(f"REMOTE_NAME_{i}", f"Instance {i}")
+    if url:
+        REMOTE_URLS.append({"url": url, "name": name})
+
+PROXY_HTML = """
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Pixal3D | AI Image-to-3D</title>
+    <style>
+        * {{ margin: 0; padding: 0; box-sizing: border-box; }}
+        html, body {{
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: #0b0f1a;
+            color: #f1f5f9;
+            height: 100%;
+            overflow: hidden;
+            display: flex;
+            flex-direction: column;
+        }}
+        .header {{
+            padding: 8px 24px;
+            background: rgba(22, 28, 45, 0.9);
+            border-bottom: 1px solid rgba(255,255,255,0.08);
+            display: flex;
+            align-items: center;
+            gap: 16px;
+            backdrop-filter: blur(12px);
+        }}
+        .header h1 {{
+            font-size: 16px;
+            font-weight: 700;
+            background: linear-gradient(135deg, #818cf8, #10b981);
+            -webkit-background-clip: text;
+            -webkit-text-fill-color: transparent;
+            white-space: nowrap;
+        }}
+        .header .notice {{
+            flex: 1;
+            font-size: 12px;
+            color: #fbbf24;
+            text-align: center;
+        }}
+        .status {{
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            font-size: 12px;
+            color: #94a3b8;
+            white-space: nowrap;
+        }}
+        .status-dot {{
+            width: 7px;
+            height: 7px;
+            border-radius: 50%;
+            background: {status_color};
+            animation: {status_anim};
+        }}
+        @keyframes pulse {{
+            0%, 100% {{ opacity: 1; }}
+            50% {{ opacity: 0.4; }}
+        }}
+        .iframe-container {{
+            flex: 1;
+            position: relative;
+        }}
+        .iframe-container iframe {{
+            width: 100%;
+            height: 100%;
+            border: none;
+            position: absolute;
+            top: 0;
+            left: 0;
+        }}
+        .no-url {{
+            flex: 1;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            padding: 40px;
+        }}
+        .no-url-card {{
+            max-width: 560px;
+            background: rgba(22, 28, 45, 0.8);
+            border: 1px solid rgba(255,255,255,0.08);
+            border-radius: 16px;
+            padding: 48px;
+            text-align: center;
+        }}
+        .no-url-card h2 {{
+            font-size: 24px;
+            margin-bottom: 16px;
+        }}
+        .no-url-card p {{
+            color: #94a3b8;
+            line-height: 1.7;
+            margin-bottom: 12px;
+        }}
+        .no-url-card code {{
+            background: rgba(129, 140, 248, 0.15);
+            color: #818cf8;
+            padding: 2px 8px;
+            border-radius: 4px;
+            font-size: 13px;
+        }}
+        .cards-container {{
+            flex: 1;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+            padding: 40px;
+            overflow-y: auto;
+        }}
+        .cards-grid {{
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 28px;
+            max-width: 1000px;
+            width: 100%;
+        }}
+        .instance-card {{
+            width: 100%;
+            background: rgba(22, 28, 45, 0.8);
+            border: 1px solid rgba(255,255,255,0.08);
+            border-radius: 24px;
+            padding: 60px 48px;
+            text-align: center;
+            transition: transform 0.2s, border-color 0.2s;
+        }}
+        .instance-card:hover {{
+            transform: translateY(-4px);
+            border-color: rgba(129, 140, 248, 0.4);
+        }}
+        .instance-card h3 {{
+            font-size: 26px;
+            margin-bottom: 16px;
+            color: #f1f5f9;
+        }}
+        .queue-status {{
+            display: inline-flex;
+            align-items: center;
+            gap: 8px;
+            padding: 8px 16px;
+            border-radius: 20px;
+            font-size: 15px;
+            font-weight: 600;
+            margin-bottom: 8px;
+            background: rgba(148, 163, 184, 0.1);
+            color: #94a3b8;
+        }}
+        .queue-status.idle {{
+            background: rgba(16, 185, 129, 0.15);
+            color: #10b981;
+        }}
+        .queue-status.busy {{
+            background: rgba(251, 146, 60, 0.15);
+            color: #fb923c;
+        }}
+        .queue-status.offline {{
+            background: rgba(239, 68, 68, 0.15);
+            color: #ef4444;
+        }}
+        .queue-dot {{
+            width: 8px;
+            height: 8px;
+            border-radius: 50%;
+            background: currentColor;
+            animation: pulse 2s infinite;
+        }}
+        .instance-card .url-hint {{
+            font-size: 13px;
+            color: #64748b;
+            margin-top: 18px;
+            word-break: break-all;
+        }}
+        .instance-card .btn-go {{
+            display: inline-block;
+            margin-top: 24px;
+            padding: 16px 44px;
+            background: linear-gradient(135deg, #818cf8, #10b981);
+            color: #ffffff !important;
+            border-radius: 12px;
+            text-decoration: none !important;
+            font-weight: 700;
+            font-size: 18px;
+            transition: opacity 0.2s;
+        }}
+        .instance-card .btn-go:hover {{
+            opacity: 0.85;
+            text-decoration: none !important;
+        }}
+        .instance-card .btn-go:hover {{
+            opacity: 0.85;
+        }}
+        .link-bar {{
+            padding: 8px 24px;
+            background: rgba(16, 185, 129, 0.08);
+            border-top: 1px solid rgba(16, 185, 129, 0.2);
+            font-size: 12px;
+            color: #94a3b8;
+            text-align: center;
+        }}
+        .link-bar a {{
+            color: #10b981;
+            text-decoration: none;
+        }}
+        .link-bar a:hover {{ text-decoration: underline; }}
+    </style>
+</head>
+<body>
+    <div class="header">
+        <h1>Pixal3D</h1>
+        <span class="notice"></span>
+        <div class="status">
+            <div class="status-dot"></div>
+            <span>{status_text}</span>
+        </div>
+    </div>
+    {content}
+</body>
+</html>
+"""
+
+
+def build_page():
+    # If multi-instance URLs are configured, show cards
+    if REMOTE_URLS:
+        status_color = "#10b981"
+        status_anim = "pulse 2s infinite"
+        status_text = f"{len(REMOTE_URLS)} instance(s) available"
+        
+        cards_html = ""
+        for i, inst in enumerate(REMOTE_URLS):
+            cards_html += f"""
+            <div class="instance-card">
+                <h3>🖥️ {inst['name']}</h3>
+                <p style="color:#94a3b8; font-size:14px; margin-bottom:8px;">⚡ Shared GPU — requests are queued</p>
+                <div class="queue-status" id="queue-status-{i}">
+                    <span class="queue-dot"></span>
+                    <span id="queue-text-{i}">Checking...</span>
+                </div>
+                <a href="{inst['url']}" target="_blank" rel="noopener noreferrer" class="btn-go">
+                    Open Instance {i}
+                </a>
+                <p class="url-hint"><code>{inst['url']}</code></p>
+            </div>
+            """
+        
+        # Build JS array of instance URLs for direct polling (Gradio share links support CORS natively)
+        urls_js = ", ".join(['"' + inst["url"].rstrip("/") + '"' for inst in REMOTE_URLS])
+        
+        content = f"""
+        <div class="cards-container">
+            <div style="width:100%; text-align:center; margin-bottom:16px;">
+                <h2 style="font-size:28px; margin-bottom:12px;">🚀 Choose a Pixal3D Instance</h2>
+                <p style="color:#fbbf24; font-size:15px; margin-bottom:8px;">⚠️ Due to a temporary HuggingFace error, this Space is currently unavailable. Please use one of the instances below.</p>
+                <p style="color:#10b981; font-size:14px; margin-top:10px; font-weight:600;">💡 Choose the instance with the shortest queue!</p>
+            </div>
+            <div class="cards-grid">
+                {cards_html}
+            </div>
+        </div>
+        """
+        
+        poll_script = f"""
+            const INSTANCE_URLS = [{urls_js}];
+            async function pollQueues() {{
+                for (let i = 0; i < INSTANCE_URLS.length; i++) {{
+                    try {{
+                        const controller = new AbortController();
+                        const timeout = setTimeout(() => controller.abort(), 5000);
+                        const resp = await fetch(INSTANCE_URLS[i] + '/queue?session_id=', {{
+                            signal: controller.signal
+                        }});
+                        clearTimeout(timeout);
+                        if (resp.ok) {{
+                            const data = await resp.json();
+                            const total = data.total_waiting + (data.gpu_busy ? 1 : 0);
+                            const el = document.getElementById('queue-text-' + i);
+                            const status = document.getElementById('queue-status-' + i);
+                            if (total === 0) {{
+                                el.textContent = 'Idle — no queue';
+                                status.className = 'queue-status idle';
+                            }} else {{
+                                el.textContent = total + ' in queue';
+                                status.className = 'queue-status busy';
+                            }}
+                        }} else {{
+                            const el = document.getElementById('queue-text-' + i);
+                            const status = document.getElementById('queue-status-' + i);
+                            if (el) {{
+                                el.textContent = 'Offline';
+                                status.className = 'queue-status offline';
+                            }}
+                        }}
+                    }} catch (e) {{
+                        const el = document.getElementById('queue-text-' + i);
+                        const status = document.getElementById('queue-status-' + i);
+                        if (el) {{
+                            el.textContent = 'Offline';
+                            status.className = 'queue-status offline';
+                        }}
+                    }}
+                }}
+            }}
+            pollQueues();
+            setInterval(pollQueues, 5000);
+        """
+    else:
+        status_color = "#ef4444"
+        status_anim = "pulse 1.5s infinite"
+        status_text = "Remote instance not configured"
+        poll_script = ""
+        content = """
+        <div class="no-url">
+            <div class="no-url-card">
+                <h2>⚡ Remote GPU Instance Not Connected</h2>
+                <p>This Space acts as a proxy to a locally-deployed Pixal3D instance running on a dedicated GPU.</p>
+                <p>To connect, set the <code>REMOTE_URL</code> secret in this Space's settings to your Gradio share link.</p>
+                <p style="margin-top:24px; font-size:13px;">
+                    Example: <code>https://abcdef123456.gradio.live</code>
+                </p>
+            </div>
+        </div>
+        """
+
+    html = PROXY_HTML.format(
+        status_color=status_color,
+        status_anim=status_anim,
+        status_text=status_text,
+        gpu_name=GPU_NAME,
+        content=content,
+    )
+    return html, poll_script
+
+
+# Use a simple Gradio Blocks app with HTML component
+page_html, page_script = build_page()
+
+with gr.Blocks(
+    title="Pixal3D | AI Image-to-3D",
+    css="footer {display:none !important;} .gradio-container {padding:0 !important; max-width:100% !important; height:100vh !important; overflow:hidden !important;} #proxy-frame {height:100%; max-height:100vh; padding:0; overflow:hidden;}",
+    theme=gr.themes.Base(),
+) as demo:
+    gr.HTML(page_html, elem_id="proxy-frame", js_on_load=page_script if page_script else None)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

assets/hdri/license.txt

@@ -0,0 +1,15 @@
+All HDRIs are licensed as CC0.
+
+These were created by Greg Zaal (Poly Haven https://polyhaven.com).
+Originals used for each HDRI:
+- City: https://polyhaven.com/a/portland_landing_pad
+- Courtyard: https://polyhaven.com/a/courtyard
+- Forest: https://polyhaven.com/a/ninomaru_teien
+- Interior: https://polyhaven.com/a/hotel_room
+- Night: Probably https://polyhaven.com/a/moonless_golf
+- Studio: Probably https://polyhaven.com/a/studio_small_01
+- Sunrise: https://polyhaven.com/a/spruit_sunrise
+- Sunset: https://polyhaven.com/a/venice_sunset
+
+1K resolution of each was taken, and compressed with oiiotool:
+oiiotool input.exr --ch R,G,B -d float --compression dwab:300 --clamp:min=0.0:max=32000.0 -o output.exr