asdfghj

app.py

@@ -12,8 +12,6 @@ import uvicorn
 from fastapi import FastAPI
 from fastapi.staticfiles import StaticFiles
 import trimesh
-from transformers import AutoProcessor, AutoModelForImageClassification
-from PIL import Image
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mini')
@@ -40,11 +38,6 @@ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
 HTML_HEIGHT = 500
 HTML_WIDTH = 500
 
-# -------------------- NSFW 检测模型加载 --------------------
-nsfw_processor = AutoProcessor.from_pretrained("Falconsai/nsfw_image_detection")
-nsfw_model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection").to(args.device)
-# -----------------------------------------------------------
-
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -138,22 +131,10 @@ floater_remove_worker = FloaterRemover()
 degenerate_face_remove_worker = DegenerateFaceRemover()
 face_reduce_worker = FaceReducer()
 
-
-def detect_nsfw(image: Image.Image, threshold: float = 0.5) -> bool:
-    """Returns True if image is NSFW"""
-    inputs = nsfw_processor(images=image, return_tensors="pt").to(args.device)
-    with torch.no_grad():
-        outputs = nsfw_model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        nsfw_score = probs[0][1].item()  # label 1 = NSFW
-    return nsfw_score > threshold
-
-
-
 progress=gr.Progress()
 
 @spaces.GPU(duration=40)
-def _gen_shape_on_gpu(
+def gen_shape(
     image=None,
     steps=50,
     guidance_scale=7.5,
@@ -171,28 +152,14 @@ def _gen_shape_on_gpu(
 
 
     if image is None:
-        error_info = {
-            "error": "Please provide either a caption or an image.",
-            "status": "failed",
-        }
-        return None,None,None,None,error_info
+        raise gr.Error("Please provide either a caption or an image.")
 
-    rgbImage = image.convert('RGB')
-    
-    # NSFW 检测
-    if nsfw_model and nsfw_processor:
-        if detect_nsfw(rgbImage):
-            error_info = {
-                "error": "The input image contains NSFW content and cannot be used. Please provide a different image and try again.",
-                "status": "failed",
-            }
-            return None,None,None,None,error_info
 
     seed = int(randomize_seed_fn(seed, randomize_seed))
     octree_resolution = int(octree_resolution)
     save_folder = gen_save_folder()
     # 先移除背景
-    image = rmbg_worker(rgbImage)
+    image = rmbg_worker(image.convert('RGB'))
 
     # 生成模型
     generator = torch.Generator()
@@ -221,11 +188,7 @@ def _gen_shape_on_gpu(
         torch.cuda.empty_cache()
     
     if path is None:
-        error_info = {
-            "error": "'Please generate a mesh first.'",
-            "status": "failed",
-        }
-        return None,None,None,None,error_info
+        raise gr.Error('Please generate a mesh first.')
 
     # 简化模型
     print(f'exporting {path}')
@@ -258,37 +221,9 @@ def _gen_shape_on_gpu(
 
 
     progress(1,desc="Complete")
-    info = {
-        "status": "success"
-    }
-    return model_viewer_html, gr.update(value=sourceObjPath, interactive=True), glbPath, objPath, info
+    return model_viewer_html, gr.update(value=sourceObjPath, interactive=True), glbPath, objPath
 
 
-def gen_shape(
-    image=None,
-    steps=50,
-    guidance_scale=7.5,
-    seed=1234,
-    octree_resolution=256,
-    num_chunks=200000,
-    target_face_num=10000,
-    randomize_seed: bool = False,
-):
-    # 调用 GPU 函数
-    html_export_mesh,file_export,glbPath_output,objPath_output, info = _gen_shape_on_gpu(
-        image, 
-        steps,
-        guidance_scale, 
-        seed,
-        octree_resolution,
-        num_chunks, 
-        target_face_num,
-        randomize_seed
-    )
-    # 如果出错，抛出异常
-    if info["status"] == "failed":
-        raise gr.Error(info["error"])
-    return html_export_mesh, file_export, glbPath_output, objPath_output
     
 def get_example_img_list():
     print('Loading example img list ...')

requirements.txt

@@ -1,52 +1,41 @@
-# --- Build Tools ---
-ninja==1.11.1
-pybind11==2.13.6
+ninja
+pybind11
 
-# --- Hugging Face ZeroGPU Infrastructure ---
-# 提前安装，确保硬件调度层最优先
-spaces==0.50.0
-huggingface-hub==0.23.5
+diffusers
+einops
+opencv-python
+numpy
+torch==2.4.0
+transformers==4.37.0
+torchvision==0.19.0
+#taming-transformers-rom1504
+#ConfigArgParse
+#ipdb
+omegaconf
 
-# --- Core ML/Deep Learning ---
-# 引擎放在最前面
-torch==2.8.0
-torchvision
-transformers==4.45.1
-diffusers==0.30.1
-accelerate==0.32.0
-safetensors==0.4.3
+#sentencepiece
+tqdm
 
-# --- Scientific Computing ---
-numpy==1.26.4
-scipy==1.13.1
-einops==0.8.0
-pandas==2.2.2
+# Mesh Processing
+trimesh
+pymeshlab
+pygltflib
+xatlas
+#kornia
+#facexlib
 
-# --- Computer Vision & Image Processing ---
-opencv-python-headless==4.10.0.84
-imageio==2.34.2
-scikit-image==0.24.0
-rembg==2.0.59
+# Training
+accelerate
+#pytorch_lightning
+#scikit-learn
+#scikit-image
 
-# --- 3D Mesh Processing ---
-trimesh==4.4.9
-pymeshlab==2023.12.post2
-pygltflib==1.16.2
-xatlas==0.0.9
-
-# --- Web Framework ---
-gradio==4.44.1
-gradio-client==1.3.0
-fastapi==0.112.2
-starlette==0.38.2
-jinja2==3.1.4
-uvicorn==0.30.6
-pydantic==2.8.2
-pydantic-core==2.20.1
-
-# --- Configuration & Utilities ---
-omegaconf==2.3.0
-pyyaml==6.0.2
-tqdm==4.66.4
-psutil==5.9.8
-onnxruntime==1.18.1
+# Demo only
+gradio
+fastapi
+uvicorn
+rembg
+onnxruntime
+pydantic==2.10.6
+#gevent
+#geventhttpclient