update

InfiniDepth/model/block/implicit_decoder.py

@@ -3,10 +3,6 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import sys
-from grpc import insecure_channel
-from sympy import use
-from pathlib import Path
 
 
 def exists(val):

InfiniDepth/model/block/prompt_models/__init__.py

@@ -1,5 +1,4 @@
 import torch.nn as nn
-from .sam import SAMPromptModel
 from .selfattn import SelfAttnPromptModel
 
 __all__ = [
@@ -29,3 +28,11 @@ class GeneralPromptModel(nn.Module):
                 patch_w,
             )
         return features
+
+
+def __getattr__(name: str):
+    if name == "SAMPromptModel":
+        from .sam import SAMPromptModel
+
+        return SAMPromptModel
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

InfiniDepth/model/block/prompt_models/utils/transformer.py

@@ -238,6 +238,8 @@ class Attention(nn.Module):
 
 class MemEffAttention(Attention):
     def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            return super().forward(q, k, v)
         q = self.q_proj(q)
         k = self.k_proj(k)
         v = self.v_proj(v)

InfiniDepth/utils/vis_utils.py

@@ -6,7 +6,6 @@ from PIL import Image
 import copy
 import cv2
 import numpy as np
-import onnxruntime
 
 
 def visualize_normal(n, save_path="normal_map.png"):
@@ -85,6 +84,12 @@ def clip_outliers_by_percentile(depthmap: torch.Tensor,
 
 
 def build_sky_model(model_path="checkpoints/skyseg.onnx"):
+    try:
+        import onnxruntime
+    except ImportError as exc:
+        raise ImportError(
+            "onnxruntime is only required for the optional sky-segmentation path."
+        ) from exc
     return onnxruntime.InferenceSession(model_path)
 
 

requirements.txt

@@ -1,52 +1,33 @@
-# python version >= 3.9  <= 3.11
-# torch
+# Runtime dependencies for the Hugging Face Space demo.
+# Keep this list tight: ZeroGPU is more stable with a smaller dependency surface.
+
 torch==2.9.1
 torchvision==0.24.1
-torchaudio==2.9.1
-xformers==0.0.33.post2
-hydra-colorlog
-hydra-core
-h5py
-lightning==2.1.3
-imageio>=2.33.1
-Pillow>=10.1.0
-imageio-ffmpeg
-einops
-# Diffusion
-diffusers[torch]
-transformers
-# ray
-termcolor
-rich
-tensorboardX
+
 numpy==1.26.4
+Pillow>=10.1.0
 opencv-python==4.9.0.80
 scipy
 matplotlib
+imageio>=2.33.1
+imageio-ffmpeg
+h5py
 open3d
-omegaconf
-tyro
-ipdb
-tensorboard
-timm
-pre-commit
-openexr
-numba
-kornia
-trimesh
-transformations
-# flash_attn
-scikit-image
+einops
 huggingface_hub>=0.24.0
-plotly>=5.22.0
-onnxruntime
 scikit-learn
+trimesh
+timm
 
-# HuggingFace Spaces ZeroGPU support
-# NOTE: Do NOT list 'gradio' here — it is managed by sdk_version in README.md.
-# Listing it again can downgrade/conflict with the pre-installed version and break
-# the ZeroGPU proxy token mechanism.
-spaces
+# Hugging Face Spaces manages Gradio from README.md `sdk_version`.
+# Do not pin `gradio` here.
+#
+# ZeroGPU support comes from the Space runtime itself; avoid pinning `spaces`
+# unless debugging a specific runtime regression.
+#
+# `xformers` and `onnxruntime` are intentionally omitted:
+# - the active demo inference path has safe fallbacks without `xformers`
+# - `onnxruntime` is only needed for optional sky-segmentation utilities
 
-# moge
+# MoGe-2 dependency used when no depth prior is uploaded.
 git+https://github.com/microsoft/MoGe.git