Update app.py

app.py

@@ -17,12 +17,13 @@ from trellis.pipelines import TrellisImageTo3DPipeline
 from trellis.representations import Gaussian, MeshExtractResult
 from trellis.utils import render_utils, postprocessing_utils
 
+
 MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
 
 # Initialize pipeline here
-pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+pipeline = TrellisImageTo3DPipeline.from_pretrained("aidevhund/3dgameasset")
 pipeline.cuda()
 
 # Preload rembg (optional)
@@ -31,6 +32,7 @@ try:
 except:
     pass
 
+
 def start_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     print(f'Creating user directory: {user_dir}')
@@ -42,6 +44,7 @@ def end_session(req: gr.Request):
     print(f'Removing user directory: {user_dir}')
     shutil.rmtree(user_dir)
 
+
 def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
     """
     Preprocess the input image.
@@ -56,6 +59,7 @@ def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
     processed_image = pipeline.preprocess_image(image)
     return processed_image
 
+
 def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> dict:
     return {
         'gaussian': {
@@ -96,12 +100,14 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     
     return gs, mesh, state['trial_id']
 
+
 def get_seed(randomize_seed: bool, seed: int) -> int:
     """
     Get the random seed.
     """
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
+
 @spaces.GPU
 def image_to_3d(
     image: Image.Image,
@@ -152,6 +158,7 @@ def image_to_3d(
     torch.cuda.empty_cache()
     return state, video_path
 
+
 @spaces.GPU
 def extract_glb(
     state: dict,
@@ -178,14 +185,14 @@ def extract_glb(
     torch.cuda.empty_cache()
     return glb_path, glb_path
 
-# UI Initialization with Custom CSS
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown("""# 3D Model Generator with Enhanced UI""")
 
+with gr.Blocks(delete_cache=(600, 600)) as demo:
+    gr.Markdown("""#HundAI 3D Game Asset Creator""")
+    
     with gr.Row():
         with gr.Column():
             image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
-
+            
             with gr.Accordion(label="Generation Settings", open=False):
                 seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                 randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
@@ -198,25 +205,39 @@ with gr.Blocks(css="style.css") as demo:
                     slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
                     slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
 
-            generate_btn = gr.Button("Generate")
-
+            generate_btn = gr.Button("Create")
+            
             with gr.Accordion(label="GLB Extraction Settings", open=False):
                 mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
                 texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
-
+            
             extract_glb_btn = gr.Button("Extract GLB", interactive=False)
 
         with gr.Column():
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
+            video_output = gr.Video(label="Created 3D Asset", autoplay=True, loop=True, height=300)
             model_output = LitModel3D(label="Extracted GLB", exposure=20.0, height=300)
             download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-
+            
     output_buf = gr.State()
 
+    # Example images at the bottom of the page
+    with gr.Row():
+        examples = gr.Examples(
+            examples=[
+                f'assets/example_image/{image}'
+                for image in os.listdir("assets/example_image")
+            ],
+            inputs=[image_prompt],
+            fn=preprocess_image,
+            outputs=[image_prompt],
+            run_on_click=True,
+            examples_per_page=5,
+        )
+
     # Handlers
     demo.load(start_session)
     demo.unload(end_session)
-
+    
     image_prompt.upload(
         preprocess_image,
         inputs=[image_prompt],
@@ -254,7 +275,8 @@ with gr.Blocks(css="style.css") as demo:
         lambda: gr.Button(interactive=False),
         outputs=[download_glb],
     )
+    
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()