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README.md

@@ -1,6 +1,5 @@
 ---
 title: "WorldGen Text-to-3D"
-emoji: "🌍"
 colorFrom: "blue"
 colorTo: "purple"
 sdk: "gradio"
@@ -12,10 +11,24 @@ pinned: false
 
 # WorldGen — Text -> 3D Scene (Hugging Face Spaces)
 
-只做「文字 ➜ 3D 場景」。請先在 Settings → Hardware 升級到 GPU（T4/A10G）再正式生成；CPU 只適合驗證流程。
----
+This Space provides a minimal Text -> 3D demo using the open-source WorldGen project.
+
+## Quick start
+1) Open the Space and enter an English scene description.
+2) Click "Generate". A ZIP file with 3D outputs will appear for download.
+
+> Tip: On free CPU it will be very slow and may timeout. Switch the Space hardware to a GPU (T4/A10G) for practical generation.
+
+## Environment
+- Python 3.11 (set via the YAML block above)
+- Gradio 5.x (set via sdk_version)
+
+## Dependencies
+We keep requirements minimal so pip can resolve versions:
+- torch>=2.7 (required by WorldGen)
+- websockets>=13.1 (required by viser used inside WorldGen)
+- WorldGen from GitHub
 
-## 使用方式
-1. 在左側輸入英文場景描述。
-2. 按「生成 3D 場景」。
-3. 下載 ZIP 檔（內含 3D 資產）。
+## Notes
+- This app disables CUDA and evaluation at runtime to avoid GPU-only/compiled ops when running on CPU.
+- Temporary outputs are cleaned a few minutes after each run to avoid hitting the 50GB ephemeral disk limit.

app.py

@@ -1,30 +1,26 @@
-# app.py —— 放在檔案最上方的環境設定（✦✦✦ 這段要在所有 import 之前 ✦✦✦）
 import os
-
-# 1) 禁用 CUDA（強制在 CPU 上跑，避免 CUDA 警告）
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-# 2) 關閉評估（若 WorldGen 支援此旗標，可避免 KNN/自訂外掛）
-os.environ["WORLDGEN_DISABLE_EVAL"] = "1"
-
-# 3) 指定 Gradio 暫存目錄（選用；集中暫存防止亂堆）
-os.environ["GRADIO_TEMP_DIR"] = "/tmp/gradio"
-
-
-# ===== 下面才開始 import 其他套件 =====
 import shutil
 import tempfile
 import threading
 from pathlib import Path
 
+# === Runtime environment tweaks (must be set before importing heavy libs) ===
+os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")        # force CPU
+os.environ.setdefault("WORLDGEN_DISABLE_EVAL", "1")       # avoid KNN/eval paths if supported
+os.environ.setdefault("GRADIO_TEMP_DIR", "/tmp/gradio")  # centralize gradio cache
+
 import gradio as gr
 import torch
 from worldgen import WorldGen
 
+# Lazy init to avoid long cold start
+_wg = None
 
-# 初始化 WorldGen（✦ CPU + low_vram ✦）
-wg = WorldGen(mode="t2s", device="cpu", low_vram=True)
-
+def get_worldgen():
+    global _wg
+    if _wg is None:
+        _wg = WorldGen(mode="t2s", device="cpu", low_vram=True)
+    return _wg
 
 def _zip_directory(dir_path: Path) -> str:
     zip_path = dir_path.with_suffix(".zip")
@@ -33,72 +29,75 @@ def _zip_directory(dir_path: Path) -> str:
     shutil.make_archive(str(dir_path), "zip", root_dir=str(dir_path))
     return str(zip_path)
 
-
 def _save_scene(generated, out_dir: Path) -> Path:
     out_dir.mkdir(parents=True, exist_ok=True)
 
-    # 嘗試通用保存方法（不同版本 WorldGen 返回物件可能不同）
+    # 1) try common object methods
     for meth in ("save", "export", "write", "to_disk"):
         if hasattr(generated, meth):
             getattr(generated, meth)(str(out_dir))
             return out_dir
 
+    # 2) single-file export fallbacks
     for meth in ("save_as_glb", "to_glb", "save_as_ply", "to_ply"):
         if hasattr(generated, meth):
             fp = out_dir / ("scene.glb" if "glb" in meth else "scene.ply")
             getattr(generated, meth)(str(fp))
             return out_dir
 
-    # 最後手段：提示需依你使用的 WorldGen 版本更新保存邏輯
+    # 3) last resort: leave a note
     (out_dir / "README.txt").write_text(
-        "未自動偵測到保存方法。請依 WorldGen 版本的 demo/README 更新保存流程。"
+        "No known save/export method detected. Please adjust _save_scene according to your WorldGen version."
     )
     return out_dir
 
-
 def generate_from_text(prompt: str):
     if not prompt or not prompt.strip():
-        return None, "請輸入場景描述（英文更穩定）。"
+        return None, "Please enter a scene description (English works best)."
+
+    wg = get_worldgen()
 
     try:
-        generated = wg.generate_world(prompt.strip())
+        result = wg.generate_world(prompt.strip())
     except Exception as e:
-        return None, f"生成失敗：{e}"
+        return None, f"Generation failed: {e}"
 
-    # 臨時目錄寫入與壓縮
     tmpdir = Path(tempfile.mkdtemp())
     out_dir = tmpdir / "worldgen_output"
-    out_dir = _save_scene(generated, out_dir)
+    out_dir = _save_scene(result, out_dir)
     zip_path = _zip_directory(out_dir)
 
-    # ✦ 延時清理臨時目錄，避免磁碟爆掉（50GB 上限）
+    # delayed cleanup to avoid filling the 50GB ephemeral disk
     def _cleanup(p: Path):
         try:
             shutil.rmtree(p, ignore_errors=True)
         except Exception:
             pass
 
-    threading.Timer(300, _cleanup, args=(tmpdir,)).start()  # 5 分鐘後清理
+    threading.Timer(300, _cleanup, args=(tmpdir,)).start()  # 5 minutes later
 
-    return zip_path, "✅ 生成完成！請下載 ZIP（含 3D 資產）。"
+    return zip_path, "Done. Download the ZIP (contains 3D assets)."
 
-
-# ===== Gradio 介面 =====
 with gr.Blocks(title="WorldGen • Text -> 3D Scene") as demo:
-    # ✦ 用「三重引號」寫多行 Markdown，避免未終止的字串錯誤
-    gr.Markdown("""# 🌍 WorldGen — 文字 -> 3D 場景- 僅啟用 Text -> 3D。- Free CPU 會很慢，建議升級 GPU（T4/A10G）。""")
+    gr.Markdown(
+        """# WorldGen - Text -> 3D
+- CPU-only fallback is enabled. For real runs, switch the Space to a GPU (T4/A10G).
+- Outputs are zipped for download; temp files are cleaned shortly after each run.
+
+"""
+    )
     with gr.Row():
         with gr.Column():
             prompt = gr.Textbox(
-                label="輸入英文場景描述",
+                label="Scene prompt (English)",
                 placeholder="a neon-lit cyberpunk street with rain reflections, highly detailed",
                 lines=3,
             )
-            run_btn = gr.Button("🚀 生成 3D 場景")
+            btn = gr.Button("Generate 3D Scene")
         with gr.Column():
-            out_zip = gr.File(label="下載輸出（ZIP）")
+            out_zip = gr.File(label="Download output (ZIP)")
             status = gr.Markdown()
 
-    run_btn.click(generate_from_text, inputs=[prompt], outputs=[out_zip, status])
+    btn.click(generate_from_text, inputs=[prompt], outputs=[out_zip, status])
 
-demo.launch()
+demo.launch()

pytorch3d/transforms.py

@@ -0,0 +1,24 @@
+import torch
+
+def matrix_to_quaternion(R: torch.Tensor) -> torch.Tensor:
+    """Minimal CPU-only implementation supporting [..., 3, 3] rotation matrices.
+    Returns quaternion in (x, y, z, w) order.
+    """
+    R = R.float()
+    single = False
+    if R.dim() == 2:
+        R = R.unsqueeze(0)
+        single = True
+    m00 = R[..., 0, 0]; m01 = R[..., 0, 1]; m02 = R[..., 0, 2]
+    m10 = R[..., 1, 0]; m11 = R[..., 1, 1]; m12 = R[..., 1, 2]
+    m20 = R[..., 2, 0]; m21 = R[..., 2, 1]; m22 = R[..., 2, 2]
+    trace = m00 + m11 + m22
+
+    qw = torch.sqrt(torch.clamp(trace + 1.0, min=1e-8)) / 2.0
+    qx = torch.sign(m21 - m12) * torch.sqrt(torch.clamp(1.0 + m00 - m11 - m22, min=1e-8)) / 2.0
+    qy = torch.sign(m02 - m20) * torch.sqrt(torch.clamp(1.0 - m00 + m11 - m22, min=1e-8)) / 2.0
+    qz = torch.sign(m10 - m01) * torch.sqrt(torch.clamp(1.0 - m00 - m11 + m22, min=1e-8)) / 2.0
+    q = torch.stack([qx, qy, qz, qw], dim=-1)
+    if single:
+        q = q.squeeze(0)
+    return q

requirements.txt

@@ -1,4 +1,4 @@
 torch>=2.7
-# torchvision>=0.22   # 若 WorldGen 實際需要再開
-websockets>=13.1      # 給 viser 相容
-git+https://github.com/ZiYang-xie/WorldGen.git
+# torchvision>=0.22  # uncomment if needed by your WorldGen setup
+websockets>=13.1
+git+https://github.com/ZiYang-xie/WorldGen.git