Update app.py

app.py

@@ -141,26 +141,27 @@ 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
+    # 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
+    nsfw_score = 0  # label 1 = NSFW
     return nsfw_score > threshold
 
 
 
 progress=gr.Progress()
 
-@spaces.GPU(duration=40)
+# @spaces.GPU(duration=40)
 def _gen_shape_on_gpu(
     image=None,
-    steps=50,
-    guidance_scale=7.5,
+    steps=10, # 50
+    guidance_scale=7.5, # 7.5
     seed=1234,
-    octree_resolution=256,
-    num_chunks=200000,
-    target_face_num=10000,
+    octree_resolution=128, # 256
+    num_chunks=50000, # 200000
+    target_face_num=2500, # 10000
     randomize_seed: bool = False,
 ):
     progress(0,desc="Starting")
@@ -270,8 +271,8 @@ def gen_shape(
     guidance_scale=7.5,
     seed=1234,
     octree_resolution=256,
-    num_chunks=200000,
-    target_face_num=10000,
+    num_chunks=50000, # 2000000
+    target_face_num=2500, # 10000
     randomize_seed: bool = False,
 ):
     # 调用 GPU 函数
@@ -328,11 +329,11 @@ with gr.Blocks().queue() as demo:
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                 with gr.Column():
                     num_steps = gr.Slider(maximum=100, minimum=1, value=5, step=1, label='Inference Steps')
-                    octree_resolution = gr.Slider(maximum=512, minimum=16, value=256, label='Octree Resolution')
+                    octree_resolution = gr.Slider(maximum=512, minimum=16, value=128, label='Octree Resolution')
                 with gr.Column():
                     cfg_scale = gr.Slider(maximum=20.0, minimum=1.0, value=5.5, step=0.1, label='Guidance Scale')
-                    num_chunks = gr.Slider(maximum=5000000, minimum=1000, value=8000, label='Number of Chunks')
-                target_face_num = gr.Slider(maximum=1000000, minimum=100, value=10000, label='Target Face Number')
+                    num_chunks = gr.Slider(maximum=50000, minimum=1000, value=2000, label='Number of Chunks') # old maximum=5000000
+                target_face_num = gr.Slider(maximum=1000000, minimum=100, value=2500, label='Target Face Number') # old maximum=1000000
 
         with gr.Column(scale=6):
             gr.Markdown("#### Generated Mesh")