debug

app.py

@@ -22,8 +22,10 @@ os.makedirs(TMP_DIR, exist_ok=True)
 
 
 def start_session(req: gr.Request):
+    gr.Warning('start start session')
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     os.makedirs(user_dir, exist_ok=True)
+    gr.Warning('end start session')
     
     
 def end_session(req: gr.Request):
@@ -45,21 +47,6 @@ def preprocess_image(image: Image.Image) -> Image.Image:
     return processed_image
 
 
-def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
-    """
-    Preprocess a list of input images.
-    
-    Args:
-        images (List[Tuple[Image.Image, str]]): The input images.
-        
-    Returns:
-        List[Image.Image]: The preprocessed images.
-    """
-    images = [image[0] for image in images]
-    processed_images = [pipeline.preprocess_image(image) for image in images]
-    return processed_images
-
-
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     return {
         'gaussian': {
@@ -115,7 +102,6 @@ def image_to_3d(
     ss_sampling_steps: int,
     slat_guidance_strength: float,
     slat_sampling_steps: int,
-    multiimage_algo: Literal["multidiffusion", "stochastic"],
     req: gr.Request,
 ) -> Tuple[dict, str]:
     """
@@ -128,7 +114,6 @@ def image_to_3d(
         ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
         slat_guidance_strength (float): The guidance strength for structured latent generation.
         slat_sampling_steps (int): The number of sampling steps for structured latent generation.
-        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
 
     Returns:
         dict: The information of the generated 3D model.
@@ -222,11 +207,8 @@ def split_image(image: Image.Image) -> List[Image.Image]:
 
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
-    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
+    ## Text to 3D Asset with Mistral + Flux + 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.
     """)
     
     with gr.Row():
@@ -244,7 +226,6 @@ 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")
             
@@ -255,6 +236,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
             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.*
                         """)
@@ -287,7 +269,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         outputs=[seed],
     ).then(
         image_to_3d,
-        inputs=[image_prompt, 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)]),