clear cache

app.py

@@ -7,48 +7,6 @@ import gradio as gr
 import spaces
 
 import os
-# Hugging Face 相关缓存
-os.environ["HF_HOME"] = "/cache_data/.huggingface"
-os.environ["HF_HUB_CACHE"] = "/cache_data/.cache/huggingface/hub"
-os.environ["HF_DATASETS_CACHE"] = "/cache_data/.cache/huggingface/datasets"
-os.environ["TRANSFORMERS_CACHE"] = "/cache_data/.cache/huggingface/hub"
-
-# PyTorch / Torch Hub / TorchVision / 部分 timm 权重
-os.environ["TORCH_HOME"] = "/cache_data/.cache/torch"
-os.environ["XDG_CACHE_HOME"] = "/cache_data/.cache"
-
-# 关键：PyTorch JIT CUDA/C++ 扩展编译缓存目录
-os.environ["TORCH_EXTENSIONS_DIR"] = "/cache_data/.cache/torch_extensions"
-
-os.environ["TMPDIR"] = "/cache_data/bucket/tmp"
-
-
-path = "/cache_data"
-print("exists:", os.path.exists(path), flush=True)
-print("isdir:", os.path.isdir(path), flush=True)
-
-try:
-    print("listdir:", os.listdir(path)[:10], flush=True)
-except Exception as e:
-    print("listdir failed:", repr(e), flush=True)
-
-try:
-    test_file = os.path.join(path, "_write_test.txt")
-    with open(test_file, "w") as f:
-        f.write("ok")
-    print("write test ok:", test_file, flush=True)
-except Exception as e:
-    print("write test failed:", repr(e), flush=True)
-
-os.makedirs(os.environ["HF_HOME"], exist_ok=True)
-os.makedirs(os.environ["HF_HUB_CACHE"], exist_ok=True)
-os.makedirs(os.environ["HF_DATASETS_CACHE"], exist_ok=True)
-os.makedirs(os.environ["TRANSFORMERS_CACHE"], exist_ok=True)
-os.makedirs(os.environ["TORCH_HOME"], exist_ok=True)
-os.makedirs(os.environ["XDG_CACHE_HOME"], exist_ok=True)
-os.makedirs(os.environ["TORCH_EXTENSIONS_DIR"], exist_ok=True)
-os.makedirs(os.environ["TMPDIR"], exist_ok=True)
-
 import uuid
 from typing import Any, List, Optional, Union
 import cv2
@@ -63,7 +21,6 @@ from huggingface_hub import snapshot_download
 from moge.model.v2 import MoGeModel
 from transformers import AutoModelForMaskGeneration, AutoProcessor
 from scripts.grounding_sam import plot_segmentation, segment
-from scripts.check_packages import install_mipsplatting
 import copy
 import shutil
 import time
@@ -74,7 +31,7 @@ MARKDOWN = """
 1. Upload an image, and draw bounding boxes for each instance by holding and dragging the mouse. Then click "Run Segmentation" to generate the segmentation result.
 2. If you find the generated 3D scene satisfactory, download it by clicking the "Download scene GLB" button, and you can also download each islolated 3D instance.
 3. In this implementation, we generate each instances one by one, and update the scene results at the "Generated GLB" area, besides, we display isolated instances below.
-4. It may take some time to download the ckpts, thank you for your patience to wait. We recommend to deploy the demo locally.
+4. It may take some time to download the ckpts, and compile the gsplat. Thank you for your patience to wait. We recommend to deploy the demo locally.
 """
 MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp")
@@ -88,7 +45,7 @@ generated_object_map = {}
 
 ############## 3D-Fixer model
 model_dir = 'HorizonRobotics/3D-Fixer'
-local_dir = "/cache_data/checkpoints/3D-Fixer"
+local_dir = "./checkpoints/3D-Fixer"
 os.makedirs(local_dir, exist_ok=True)
 snapshot_download(repo_id=model_dir, local_dir=local_dir)
 ############## 3D-Fixer model
@@ -353,7 +310,7 @@ def get_duration(rgb_image, seg_image, seed, randomize_seed,
                  cfg_interval_end, t_rescale, work_space):
     instance_labels = np.unique(np.array(seg_image).reshape(-1, 3), axis=0)
     step_duration = 15.0
-    return instance_labels.shape[0] * step_duration + 75
+    return instance_labels.shape[0] * step_duration + 120
 
 @spaces.GPU(duration=get_duration)
 def run_generation(