Update app.py

app.py

@@ -110,14 +110,11 @@ def get_seed(randomize_seed: bool, seed: int) -> int:
 @spaces.GPU
 def image_to_3d(
     image: Image.Image,
-    multiimages: List[Tuple[Image.Image, str]],
-    is_multiimage: bool,
     seed: int,
     ss_guidance_strength: float,
     ss_sampling_steps: int,
     slat_guidance_strength: float,
     slat_sampling_steps: int,
-    multiimage_algo: Literal["multidiffusion", "stochastic"],
     req: gr.Request,
 ) -> Tuple[dict, str]:
     """
@@ -139,7 +136,6 @@ def image_to_3d(
         str: The path to the video of the 3D model.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    if not is_multiimage:
         outputs = pipeline.run(
             image,
             seed=seed,
@@ -154,22 +150,6 @@ def image_to_3d(
                 "cfg_strength": slat_guidance_strength,
             },
         )
-    else:
-        outputs = pipeline.run_multi_image(
-            [image[0] for image in multiimages],
-            seed=seed,
-            formats=["gaussian", "mesh"],
-            preprocess_image=False,
-            sparse_structure_sampler_params={
-                "steps": ss_sampling_steps,
-                "cfg_strength": ss_guidance_strength,
-            },
-            slat_sampler_params={
-                "steps": slat_sampling_steps,
-                "cfg_strength": slat_guidance_strength,
-            },
-            mode=multiimage_algo,
-        )
     video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
     video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
     video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
@@ -278,7 +258,6 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                 with gr.Row():
                     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)
-                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
 
             generate_btn = gr.Button("Generate")
             
@@ -301,7 +280,6 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                 download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
                 download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
     
-    is_multiimage = gr.State(False)
     output_buf = gr.State()
 
     # Example images at the bottom of the page
@@ -317,15 +295,6 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
             run_on_click=True,
             examples_per_page=64,
         )
-    with gr.Row(visible=False) as multiimage_example:
-        examples_multi = gr.Examples(
-            examples=prepare_multi_example(),
-            inputs=[image_prompt],
-            fn=split_image,
-            outputs=[multiimage_prompt],
-            run_on_click=True,
-            examples_per_page=8,
-        )
 
     # Handlers
     demo.load(start_session)
@@ -333,11 +302,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     
     single_image_input_tab.select(
         lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
-    )
-    multiimage_input_tab.select(
-        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
+        outputs=[single_image_example]
     )
     
     image_prompt.upload(
@@ -345,11 +310,6 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         inputs=[image_prompt],
         outputs=[image_prompt],
     )
-    multiimage_prompt.upload(
-        preprocess_images,
-        inputs=[multiimage_prompt],
-        outputs=[multiimage_prompt],
-    )
 
     generate_btn.click(
         get_seed,
@@ -357,7 +317,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         outputs=[seed],
     ).then(
         image_to_3d,
-        inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
+        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)]),