Update app.py

app.py

@@ -13,7 +13,6 @@ from PIL import Image
 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)
@@ -28,9 +27,6 @@ def end_session(req: gr.Request):
     shutil.rmtree(user_dir)
 
 def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
-    """
-    Preprocesa una lista de imágenes.
-    """
     images = [image[0] for image in images]
     processed_images = [pipeline.preprocess_image(image) for image in images]
     return processed_images
@@ -72,9 +68,6 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
     return gs, mesh
 
 def get_seed(randomize_seed: bool, seed: int) -> int:
-    """
-    Obtiene una semilla aleatoria.
-    """
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
 @spaces.GPU
@@ -88,9 +81,6 @@ def image_to_3d(
     multiimage_algo: Literal["multidiffusion", "stochastic"],
     req: gr.Request,
 ) -> Tuple[dict, str]:
-    """
-    Convierte múltiples imágenes en un modelo 3D.
-    """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     outputs = pipeline.run_multi_image(
         [image[0] for image in multiimages],
@@ -123,9 +113,6 @@ def extract_glb(
     texture_size: int,
     req: gr.Request,
 ) -> Tuple[str, str]:
-    """
-    Extrae un archivo GLB del modelo 3D.
-    """
     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)
@@ -134,41 +121,14 @@ 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]:
-    """
-    Extrae un archivo Gaussiano del modelo 3D.
-    """
-    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 prepare_multi_example() -> List[Tuple[str, str]]:
-    """
-    Prepara ejemplos de múltiples imágenes para la galería.
-    """
-    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
-    examples = []
-    for case in multi_case:
-        case_images = []
-        for i in range(1, 4):  # Suponemos 3 vistas por caso
-            img_path = f'assets/example_multi_image/{case}_{i}.png'
-            if os.path.exists(img_path):  # Asegurarse de que la imagen existe
-                case_images.append((img_path, f"View {i}"))
-        if case_images:  # Solo añadir casos con imágenes válidas
-            examples.append(case_images)
-    return examples
-
 # Interfaz Gradio
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
-    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
-    * Upload multiple images of an object from different views and click "Generate" to create a 3D asset.
-    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
-    ✨New: Experimental multi-image support and Gaussian file extraction.
+    # UTPL - Conversión de Multiples Imágenes a objetos 3D usando IA  
+    ### Tesis: *"Objetos tridimensionales creados por IA: Innovación en entornos virtuales"*  
+    **Autor:** Carlos Vargas  
+    **Base técnica:** Adaptación de [TRELLIS](https://trellis3d.github.io/) (herramienta de código abierto para generación 3D)  
+    **Propósito educativo:** Demostraciones académicas e Investigación en modelado 3D automático  
     """)
     
     with gr.Row():
@@ -176,11 +136,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
             with gr.Tabs() as input_tabs:
                 with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
                     multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
-                    gr.Markdown("""
-                    Input different views of the object in separate images. 
-                    NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
-                    """)
-                    
+
             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)
@@ -200,45 +156,25 @@ 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)
-            
-            with gr.Row():
-                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
+            model_output = LitModel3D(label="Extracted GLB", exposure=10.0, height=300)
+            download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
 
     output_buf = gr.State()
 
-    # Ejemplos de imágenes múltiples
-    with gr.Row(visible=True) as multiimage_example:
-        examples_multi = gr.Examples(
-            examples=prepare_multi_example(),
-            inputs=[multiimage_prompt],
-            fn=lambda x: x,  # Pasar la entrada directamente (sin preprocesamiento adicional)
-            outputs=[multiimage_prompt],
-            run_on_click=True,
-            examples_per_page=8,
-        )
-
     # Manejadores
     demo.load(start_session)
     demo.unload(end_session)
-
+    
     multiimage_prompt.upload(
         preprocess_images,
         inputs=[multiimage_prompt],
         outputs=[multiimage_prompt],
     )
-
+    
     generate_btn.click(
         get_seed,
         inputs=[randomize_seed, seed],
@@ -248,15 +184,15 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         inputs=[multiimage_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
         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(
         extract_glb,
         inputs=[output_buf, mesh_simplify, texture_size],
@@ -265,16 +201,7 @@ 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],