debug 2

app.py

@@ -33,176 +33,176 @@ def end_session(req: gr.Request):
     shutil.rmtree(user_dir)
 
 
-def preprocess_image(image: Image.Image) -> Image.Image:
-    """
-    Preprocess the input image.
-
-    Args:
-        image (Image.Image): The input image.
-
-    Returns:
-        Image.Image: The preprocessed image.
-    """
-    processed_image = pipeline.preprocess_image(image)
-    return processed_image
-
-
-def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
-    return {
-        'gaussian': {
-            **gs.init_params,
-            '_xyz': gs._xyz.cpu().numpy(),
-            '_features_dc': gs._features_dc.cpu().numpy(),
-            '_scaling': gs._scaling.cpu().numpy(),
-            '_rotation': gs._rotation.cpu().numpy(),
-            '_opacity': gs._opacity.cpu().numpy(),
-        },
-        'mesh': {
-            'vertices': mesh.vertices.cpu().numpy(),
-            'faces': mesh.faces.cpu().numpy(),
-        },
-    }
+# def preprocess_image(image: Image.Image) -> Image.Image:
+#     """
+#     Preprocess the input image.
+
+#     Args:
+#         image (Image.Image): The input image.
+
+#     Returns:
+#         Image.Image: The preprocessed image.
+#     """
+#     processed_image = pipeline.preprocess_image(image)
+#     return processed_image
+
+
+# def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
+#     return {
+#         'gaussian': {
+#             **gs.init_params,
+#             '_xyz': gs._xyz.cpu().numpy(),
+#             '_features_dc': gs._features_dc.cpu().numpy(),
+#             '_scaling': gs._scaling.cpu().numpy(),
+#             '_rotation': gs._rotation.cpu().numpy(),
+#             '_opacity': gs._opacity.cpu().numpy(),
+#         },
+#         'mesh': {
+#             'vertices': mesh.vertices.cpu().numpy(),
+#             'faces': mesh.faces.cpu().numpy(),
+#         },
+#     }
     
     
-def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
-    gs = Gaussian(
-        aabb=state['gaussian']['aabb'],
-        sh_degree=state['gaussian']['sh_degree'],
-        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
-        scaling_bias=state['gaussian']['scaling_bias'],
-        opacity_bias=state['gaussian']['opacity_bias'],
-        scaling_activation=state['gaussian']['scaling_activation'],
-    )
-    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
-    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
-    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
-    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
-    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
+# def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
+#     gs = Gaussian(
+#         aabb=state['gaussian']['aabb'],
+#         sh_degree=state['gaussian']['sh_degree'],
+#         mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
+#         scaling_bias=state['gaussian']['scaling_bias'],
+#         opacity_bias=state['gaussian']['opacity_bias'],
+#         scaling_activation=state['gaussian']['scaling_activation'],
+#     )
+#     gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
+#     gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
+#     gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
+#     gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
+#     gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
     
-    mesh = edict(
-        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
-        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
-    )
+#     mesh = edict(
+#         vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
+#         faces=torch.tensor(state['mesh']['faces'], device='cuda'),
+#     )
     
-    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,
-    seed: int,
-    ss_guidance_strength: float,
-    ss_sampling_steps: int,
-    slat_guidance_strength: float,
-    slat_sampling_steps: int,
-    req: gr.Request,
-) -> Tuple[dict, str]:
-    """
-    Convert an image to a 3D model.
-
-    Args:
-        image (Image.Image): The input image.
-        seed (int): The random seed.
-        ss_guidance_strength (float): The guidance strength for sparse structure generation.
-        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.
-
-    Returns:
-        dict: The information of the generated 3D model.
-        str: The path to the video of the 3D model.
-    """
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    outputs = pipeline.run(
-        image,
-        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,
-        },
-    )
-    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))]
-    video_path = os.path.join(user_dir, 'sample.mp4')
-    imageio.mimsave(video_path, video, fps=15)
-    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
-    torch.cuda.empty_cache()
-    return state, video_path
-
-
-@spaces.GPU(duration=90)
-def extract_glb(
-    state: dict,
-    mesh_simplify: float,
-    texture_size: int,
-    req: gr.Request,
-) -> Tuple[str, str]:
-    """
-    Extract a GLB file from the 3D model.
-
-    Args:
-        state (dict): The state of the generated 3D model.
-        mesh_simplify (float): The mesh simplification factor.
-        texture_size (int): The texture resolution.
-
-    Returns:
-        str: The path to the extracted GLB file.
-    """
-    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)
-    glb_path = os.path.join(user_dir, 'sample.glb')
-    glb.export(glb_path)
-    torch.cuda.empty_cache()
-    return glb_path, glb_path
-
-
-@spaces.GPU
-def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-    """
-    Extract a Gaussian file from the 3D model.
-
-    Args:
-        state (dict): The state of the generated 3D model.
-
-    Returns:
-        str: The path to the extracted Gaussian file.
-    """
-    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)
-    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
-    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
-    images = []
-    for s, e in zip(start_pos, end_pos):
-        images.append(Image.fromarray(image[:, s:e+1]))
-    return [preprocess_image(image) for image in images]
+#     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,
+#     seed: int,
+#     ss_guidance_strength: float,
+#     ss_sampling_steps: int,
+#     slat_guidance_strength: float,
+#     slat_sampling_steps: int,
+#     req: gr.Request,
+# ) -> Tuple[dict, str]:
+#     """
+#     Convert an image to a 3D model.
+
+#     Args:
+#         image (Image.Image): The input image.
+#         seed (int): The random seed.
+#         ss_guidance_strength (float): The guidance strength for sparse structure generation.
+#         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.
+
+#     Returns:
+#         dict: The information of the generated 3D model.
+#         str: The path to the video of the 3D model.
+#     """
+#     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+#     outputs = pipeline.run(
+#         image,
+#         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,
+#         },
+#     )
+#     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))]
+#     video_path = os.path.join(user_dir, 'sample.mp4')
+#     imageio.mimsave(video_path, video, fps=15)
+#     state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
+#     torch.cuda.empty_cache()
+#     return state, video_path
+
+
+# @spaces.GPU(duration=90)
+# def extract_glb(
+#     state: dict,
+#     mesh_simplify: float,
+#     texture_size: int,
+#     req: gr.Request,
+# ) -> Tuple[str, str]:
+#     """
+#     Extract a GLB file from the 3D model.
+
+#     Args:
+#         state (dict): The state of the generated 3D model.
+#         mesh_simplify (float): The mesh simplification factor.
+#         texture_size (int): The texture resolution.
+
+#     Returns:
+#         str: The path to the extracted GLB file.
+#     """
+#     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)
+#     glb_path = os.path.join(user_dir, 'sample.glb')
+#     glb.export(glb_path)
+#     torch.cuda.empty_cache()
+#     return glb_path, glb_path
+
+
+# @spaces.GPU
+# def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
+#     """
+#     Extract a Gaussian file from the 3D model.
+
+#     Args:
+#         state (dict): The state of the generated 3D model.
+
+#     Returns:
+#         str: The path to the extracted Gaussian file.
+#     """
+#     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)
+#     start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
+#     end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
+#     images = []
+#     for s, e in zip(start_pos, end_pos):
+#         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:
@@ -257,52 +257,52 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     demo.unload(end_session)
     
     
-    image_prompt.upload(
-        preprocess_image,
-        inputs=[image_prompt],
-        outputs=[image_prompt],
-    )
-
-    generate_btn.click(
-        get_seed,
-        inputs=[randomize_seed, seed],
-        outputs=[seed],
-    ).then(
-        image_to_3d,
-        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],
-    )
-
-    video_output.clear(
-        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
-    )
-
-    extract_glb_btn.click(
-        extract_glb,
-        inputs=[output_buf, mesh_simplify, texture_size],
-        outputs=[model_output, download_glb],
-    ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_glb],
-    )
+    # image_prompt.upload(
+    #     preprocess_image,
+    #     inputs=[image_prompt],
+    #     outputs=[image_prompt],
+    # )
+
+    # generate_btn.click(
+    #     get_seed,
+    #     inputs=[randomize_seed, seed],
+    #     outputs=[seed],
+    # ).then(
+    #     image_to_3d,
+    #     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],
+    # )
+
+    # video_output.clear(
+    #     lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
+    #     outputs=[extract_glb_btn, extract_gs_btn],
+    # )
+
+    # extract_glb_btn.click(
+    #     extract_glb,
+    #     inputs=[output_buf, mesh_simplify, texture_size],
+    #     outputs=[model_output, download_glb],
+    # ).then(
+    #     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],
-    )
+    # 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