Update app.py

app.py

@@ -15,22 +15,18 @@ 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)
 
-
 def start_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     os.makedirs(user_dir, exist_ok=True)
     
-    
 def end_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     shutil.rmtree(user_dir)
 
-
 def preprocess_image(image: Image.Image) -> Image.Image:
     processed_image = pipeline.preprocess_image(image)
     return processed_image
@@ -51,7 +47,6 @@ def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
         },
     }
     
-    
 def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     gs = Gaussian(
         aabb=state['gaussian']['aabb'],
@@ -74,14 +69,9 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     
     return gs, mesh
 
-
 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,
@@ -117,7 +107,6 @@ def image_to_3d(
     torch.cuda.empty_cache()
     return state, video_path
 
-
 @spaces.GPU(duration=90)
 def extract_glb(
     state: dict,
@@ -125,7 +114,6 @@ def extract_glb(
     texture_size: int,
     req: gr.Request,
 ) -> Tuple[str, str]:
-
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, mesh = unpack_state(state)
     glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
@@ -134,20 +122,7 @@ def extract_glb(
     torch.cuda.empty_cache()
     return glb_path, glb_path
 
-@spaces.GPU
-def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    gs, _ = unpack_state(state)
-    gaussian_path = os.path.join(user_dir, 'sample.ply')
-    gs.save_ply(gaussian_path)
-    torch.cuda.empty_cache()
-    return gaussian_path, gaussian_path
-
 def split_image(image: Image.Image) -> List[Image.Image]:
-    """
-    Split an image into multiple views.
-    """
     image = np.array(image)
     alpha = image[..., 3]
     alpha = np.any(alpha>0, axis=0)
@@ -158,14 +133,13 @@ def split_image(image: Image.Image) -> List[Image.Image]:
         images.append(Image.fromarray(image[:, s:e+1]))
     return [preprocess_image(image) for image in images]
 
-
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
     ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
     * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
     * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
     
-    ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
+    ✨New: Experimental multi-image support.
     """)
     
     with gr.Row():
@@ -192,24 +166,17 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                 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)
             
-            with gr.Row():
-                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
-                extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
-            gr.Markdown("""
-                        *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
-                        """)
+            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)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
+            model_output = LitModel3D(label="Extracted GLB", exposure=10.0, height=300)
             
             with gr.Row():
                 download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
-    
+
     output_buf = gr.State()
 
-    # Handlers
     demo.load(start_session)
     demo.unload(end_session)
     
@@ -228,13 +195,13 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         inputs=[image_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
         outputs=[output_buf, video_output],
     ).then(
-        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
+        lambda: gr.Button(interactive=True),
+        outputs=[extract_glb_btn],
     )
 
     video_output.clear(
-        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
+        lambda: gr.Button(interactive=False),
+        outputs=[extract_glb_btn],
     )
 
     extract_glb_btn.click(
@@ -245,23 +212,12 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         lambda: gr.Button(interactive=True),
         outputs=[download_glb],
     )
-    
-    extract_gs_btn.click(
-        extract_gaussian,
-        inputs=[output_buf],
-        outputs=[model_output, download_gs],
-    ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_gs],
-    )
 
     model_output.clear(
         lambda: gr.Button(interactive=False),
         outputs=[download_glb],
     )
     
-
-# Launch the Gradio app
 if __name__ == "__main__":
     pipeline = TrellisImageTo3DPipeline.from_pretrained("cavargas10/TRELLIS")
     pipeline.cuda()
@@ -269,4 +225,4 @@ if __name__ == "__main__":
         pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
     except:
         pass
-    demo.launch()
+    demo.launch()