Sync deps: Dockerfile system deps + ffmpeg, single pip flow

Dockerfile

@@ -19,6 +19,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
+# torchmcubes: build deps for --no-build-isolation (scikit-build-core, pybind11, ninja)
+RUN pip install --no-cache-dir scikit-build-core pybind11 ninja
 # torchmcubes must be built after torch is installed (CMake needs TorchConfig.cmake)
 RUN pip install --no-cache-dir --no-build-isolation "git+https://github.com/tatsy/torchmcubes.git"
 

README.md

@@ -34,6 +34,20 @@ Add a token so the Space can download the model:
 
 After that, run Skybox or Mesh again; the model will download on first use.
 
+## Ready to use
+
+1. Push this repo to a **Docker** Space (SDK: docker, port 7860).
+2. Set the **HF_TOKEN** secret (Settings → Variables and secrets).
+3. Restart the Space. First build may take **15–25 minutes** (installing PyTorch, TripoSR, torchmcubes).
+4. Open the app and generate a skybox or mesh. First run will download the Stable Diffusion model (~4 GB).
+
+## Troubleshooting
+
+- **"Could not load Stable Diffusion"** → Add `HF_TOKEN` in Settings → Variables and secrets, then restart the Space.
+- **"TripoSR not found"** → Rebuild the Space (the Dockerfile clones TripoSR). If you forked the Space, ensure the Dockerfile and `scripts/` are present.
+- **Mesh generation times out** → Use **Mesh resolution 256** and disable **Bake texture atlas** for a faster run; GPU Spaces are much faster than CPU.
+- **Build fails on torchmcubes** → The Dockerfile installs torch first, then builds torchmcubes with `--no-build-isolation` so CMake finds Torch. Do not change the order of `pip install` steps.
+
 ---
 
 Model: [evoneural/evoneuralIn3D](https://huggingface.co/evoneural/evoneuralIn3D)

app.py

@@ -18,6 +18,9 @@ import streamlit as st
 OUTPUTS = ROOT / "outputs"
 OUTPUTS.mkdir(exist_ok=True)
 
+# Hugging Face Space: HF sets SPACE_ID when running in a Space
+IS_HF_SPACE = bool(os.environ.get("SPACE_ID") or os.environ.get("SPACE_REPO_ID"))
+
 
 def main() -> None:
     st.set_page_config(
@@ -25,29 +28,41 @@ def main() -> None:
         page_icon="🎮",
         layout="wide",
     )
-    st.title("Evoneural MVP – Local Mesh & Skybox")
-    st.caption("Text → 3D mesh (TripoSR) and Text → 360° skybox (Stable Diffusion). Runs on localhost.")
+    if IS_HF_SPACE:
+        st.title("EvoneuralIn3D – Mesh & Skybox")
+        st.caption("Text → 3D mesh (TripoSR) and Text → 360° skybox (Stable Diffusion). Running on Hugging Face Space.")
+    else:
+        st.title("Evoneural MVP – Local Mesh & Skybox")
+        st.caption("Text → 3D mesh (TripoSR) and Text → 360° skybox (Stable Diffusion). Runs on localhost.")
 
     # Sidebar: model setup (token + download)
     with st.sidebar:
         st.subheader("Stable Diffusion model")
+        hf_token_env = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
+        if IS_HF_SPACE:
+            if hf_token_env:
+                st.success("HF_TOKEN is set (from Space secrets)")
+            else:
+                st.error("HF_TOKEN not set")
+                st.caption("Add it in this Space: **Settings** → **Variables and secrets** → New secret: `HF_TOKEN`. Then restart the Space.")
         from scripts.skybox_generator import _default_local_weights_dir
         local_model = _default_local_weights_dir()
         if local_model:
-            st.success(f"Local model: found")
+            st.success("Local model: found")
             st.caption(os.path.basename(local_model))
-        else:
+        elif not IS_HF_SPACE:
             st.warning("No local model. Download below or need internet on first generate.")
-        hf_token = st.text_input(
-            "Hugging Face token (optional, if behind firewall)",
-            type="password",
-            key="hf_token",
-            placeholder="hf_...",
-            help="Get a token at huggingface.co/settings/tokens",
-        )
-        if hf_token:
-            os.environ["HF_TOKEN"] = hf_token
-        if st.button("Download model (~4GB to ./weights/sd-v1-5)", key="btn_download"):
+        if not IS_HF_SPACE:
+            hf_token = st.text_input(
+                "Hugging Face token (optional, if behind firewall)",
+                type="password",
+                key="hf_token",
+                placeholder="hf_...",
+                help="Get a token at huggingface.co/settings/tokens",
+            )
+            if hf_token:
+                os.environ["HF_TOKEN"] = hf_token
+        if not IS_HF_SPACE and st.button("Download model (~4GB to ./weights/sd-v1-5)", key="btn_download"):
             with st.spinner("Downloading model... (may take several minutes)"):
                 try:
                     from scripts.download_sd_model import download_sd_model
@@ -57,6 +72,13 @@ def main() -> None:
                 except Exception as e:
                     st.error(str(e))
                     st.caption("Set a Hugging Face token above if your network blocks Hugging Face.")
+        # Environment check: TripoSR (useful in Space)
+        from scripts.mesh_generator import find_triposr_root as _find_triposr
+        triposr_ok = _find_triposr(str(ROOT)) is not None
+        if triposr_ok:
+            st.caption("TripoSR: ready")
+        else:
+            st.caption("TripoSR: not found (mesh tab will show instructions)")
 
     tab_mesh, tab_skybox = st.tabs(["🟦 Text → 3D Mesh", "🌅 Text → Skybox"])
 
@@ -130,8 +152,8 @@ def main() -> None:
                         triposr_root = find_triposr_root(str(ROOT))
                         if not triposr_root:
                             st.error(
-                                "TripoSR not found. Clone it: "
-                                "`git clone https://github.com/VAST-AI-Research/TripoSR.git TripoSR`"
+                                "TripoSR not found. In this Space the Docker image should include it. "
+                                "If you see this, rebuild the Space or check the Dockerfile."
                             )
                         elif image_path_to_use:
                             path = image_path_to_use
@@ -140,6 +162,8 @@ def main() -> None:
                                 with open(path, "wb") as f:
                                     f.write(uploaded.getvalue())
                             if path != "upload" and os.path.isfile(path):
+                                import torch as _torch
+                                _dev = "cuda:0" if _torch.cuda.is_available() else "cpu"
                                 mesh_path, elapsed, msg = generate_mesh_from_image(
                                     path,
                                     output_dir=str(OUTPUTS / "mesh_run"),
@@ -147,6 +171,7 @@ def main() -> None:
                                     mc_resolution=mc_resolution,
                                     bake_texture=bake_texture,
                                     smooth_mesh=smooth_mesh,
+                                    device=_dev,
                                 )
                                 if mesh_path:
                                     st.success(f"Done in {elapsed:.1f}s. {msg}")
@@ -157,6 +182,8 @@ def main() -> None:
                             elif path == "upload":
                                 st.warning("Upload an image first.")
                         else:
+                            import torch as _torch
+                            _dev = "cuda:0" if _torch.cuda.is_available() else "cpu"
                             mesh_path, elapsed, msg = generate_mesh_from_text(
                                 prompt_mesh,
                                 output_dir=str(OUTPUTS),
@@ -165,6 +192,7 @@ def main() -> None:
                                 mc_resolution=mc_resolution,
                                 bake_texture=bake_texture,
                                 smooth_mesh=smooth_mesh,
+                                device=_dev,
                             )
                             if mesh_path:
                                 st.success(f"Done in {elapsed:.1f}s. {msg}")

scripts/mesh_generator.py

@@ -82,7 +82,7 @@ def generate_mesh_from_image(
     output_dir: str = "outputs",
     mesh_format: str = "glb",
     triposr_root: str | None = None,
-    device: str = "cuda:0",
+    device: str | None = None,
     use_hunyuan3d2: bool | None = None,
     mc_resolution: int = 512,
     bake_texture: bool = True,
@@ -118,6 +118,7 @@ def generate_mesh_from_image(
             "TripoSR not found. Clone it: git clone https://github.com/VAST-AI-Research/TripoSR.git",
         )
 
+    device = device or _infer_device()
     Path(output_dir).mkdir(parents=True, exist_ok=True)
     # When baking texture, xatlas.export() always writes OBJ format (ignores .glb extension).
     # Ask for OBJ when bake_texture + glb, then we convert OBJ+texture to real GLB.
@@ -204,6 +205,15 @@ def find_hunyuan3d2_root(project_root: str | None = None) -> str | None:
         return None
 
 
+def _infer_device() -> str:
+    """Use GPU if available, else CPU (for CPU-only Spaces)."""
+    try:
+        import torch
+        return "cuda:0" if torch.cuda.is_available() else "cpu"
+    except Exception:
+        return "cpu"
+
+
 def generate_mesh_from_text(
     prompt: str,
     output_dir: str = "outputs",
@@ -214,6 +224,7 @@ def generate_mesh_from_text(
     bake_texture: bool = True,
     texture_resolution: int = 2048,
     smooth_mesh: bool = True,
+    device: str | None = None,
 ) -> tuple[str | None, float, str]:
     """
     Text → 3D mesh. Uses Hunyuan3D-2 full pipeline when available (use_hunyuan3d2=True or repo found),
@@ -248,6 +259,7 @@ def generate_mesh_from_text(
         image_path, _ = text_to_image(prompt, output_dir=output_dir, seed=seed)
     except Exception as e:
         return (None, 0.0, f"Text-to-image failed: {e}")
+    device = device or _infer_device()
     mesh_path, mesh_time, msg = generate_mesh_from_image(
         image_path,
         output_dir=os.path.join(output_dir, "mesh_run"),
@@ -256,6 +268,7 @@ def generate_mesh_from_text(
         bake_texture=bake_texture,
         texture_resolution=texture_resolution,
         smooth_mesh=smooth_mesh,
+        device=device,
     )
     total_time = time.perf_counter() - t0
     if mesh_path: