Update app.py

app.py

@@ -1,113 +1,439 @@
 import gradio as gr
+import spaces
+from gradio_litmodel3d import LitModel3D
 
-def greet(name: str, enthusiasm: int = 1) -> str:
+import os
+import shutil
+os.environ['SPCONV_ALGO'] = 'native'
+from typing import *
+import torch
+import numpy as np
+import imageio
+from easydict import EasyDict as edict
+from PIL import Image
+from trellis.pipelines import TrellisVGGTTo3DPipeline
+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')
+# TMP_DIR = "tmp/Trellis-demo"
+# os.environ['GRADIO_TEMP_DIR'] = '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)
+
+@spaces.GPU
+def preprocess_image(image: Image.Image) -> Image.Image:
     """
-    Enhanced greeting function with customizable enthusiasm.
+    Preprocess the input image for 3D generation.
+    
+    This function is called when a user uploads an image or selects an example.
+    It applies background removal and other preprocessing steps necessary for
+    optimal 3D model generation.
+    Args:
+        image (Image.Image): The input image from the user
+    Returns:
+        Image.Image: The preprocessed image ready for 3D generation
+    """
+    processed_image = pipeline.preprocess_image(image)
+    return processed_image
+
+@spaces.GPU
+def preprocess_videos(video: str) -> List[Tuple[Image.Image, str]]:
     """
-    if not name.strip():
-        return "Please enter a name!"
-    greeting = f"Hello, {name}!"
-    exclamation = "!" * enthusiasm
-    return f"{greeting}{exclamation} Welcome to your Gradio 6 application."
-
-# Version 1: Enhanced with Examples and Validation
-with gr.Blocks() as demo:
-    
-    gr.HTML(
-        """
-        <div style="text-align: center; margin-bottom: 20px;">
-            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="text-decoration: none; color: #007bff; font-weight: bold;">
-                Built with anycoder
-            </a>
-        </div>
-        """
+    Preprocess the input video for multi-image 3D generation.
+    
+    This function is called when a user uploads a video.
+    It extracts frames from the video and processes each frame to prepare them
+    for the multi-image 3D generation pipeline.
+    
+    Args:
+        video (str): The path to the input video file
+        
+    Returns:
+        List[Tuple[Image.Image, str]]: The list of preprocessed images ready for 3D generation
+    """
+    vid = imageio.get_reader(video, 'ffmpeg')
+    fps = vid.get_meta_data()['fps']
+    images = []
+    for i, frame in enumerate(vid):
+        if i % max(int(fps * 1), 1) == 0:
+            img = Image.fromarray(frame)
+            W, H = img.size
+            img = img.resize((int(W / H * 512), 512))
+            images.append(img)
+    vid.close()
+    processed_images = [pipeline.preprocess_image(image) for image in images]
+    return processed_images
+
+@spaces.GPU
+def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
+    """
+    Preprocess a list of input images for multi-image 3D generation.
+    
+    This function is called when users upload multiple images in the gallery.
+    It processes each image to prepare them for the multi-image 3D generation pipeline.
+    
+    Args:
+        images (List[Tuple[Image.Image, str]]): The input images from the gallery
+        
+    Returns:
+        List[Image.Image]: The preprocessed images ready for 3D generation
+    """
+    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': {
+            **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')
+    
+    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 for generation.
+    
+    This function is called by the generate button to determine whether to use
+    a random seed or the user-specified seed value.
+    
+    Args:
+        randomize_seed (bool): Whether to generate a random seed
+        seed (int): The user-specified seed value
+        
+    Returns:
+        int: The seed to use for generation
+    """
+    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
+
+
+@spaces.GPU(duration=120)
+def generate_and_extract_glb(
+    multiimages: List[Tuple[Image.Image, str]],
+    seed: int,
+    ss_guidance_strength: float,
+    ss_sampling_steps: int,
+    slat_guidance_strength: float,
+    slat_sampling_steps: int,
+    multiimage_algo: Literal["multidiffusion", "stochastic"],
+    mesh_simplify: float,
+    texture_size: int,
+    req: gr.Request,
+) -> Tuple[dict, str, str, str]:
+    """
+    Convert an image to a 3D model and extract GLB file.
+    Args:
+        image (Image.Image): The input image.
+        multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
+        is_multiimage (bool): Whether is in multi-image mode.
+        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.
+        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
+        mesh_simplify (float): The mesh simplification factor.
+        texture_size (int): The texture resolution.
+    Returns:
+        dict: The information of the generated 3D model.
+        str: The path to the video of the 3D model.
+        str: The path to the extracted GLB file.
+        str: The path to the extracted GLB file (for download).
+    """
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    image_files = [image[0] for image in multiimages]
+
+    # Generate 3D model
+    outputs, _, _ = pipeline.run(
+        image=image_files,
+        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,
     )
+
+    # Render video
+    # import uuid
+    # output_id = str(uuid.uuid4())
+    # os.makedirs(f"{TMP_DIR}/{output_id}", exist_ok=True)
+    # video_path = f"{TMP_DIR}/{output_id}/preview.mp4"
+    # glb_path = f"{TMP_DIR}/{output_id}/mesh.glb"
+
+    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)
+    
+    # Extract GLB
+    gs = outputs['gaussian'][0]
+    mesh = outputs['mesh'][0]
+    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)
+    
+    # Pack state for optional Gaussian extraction
+    state = pack_state(gs, mesh)
+    
+    torch.cuda.empty_cache()
+    return state, video_path, glb_path, glb_path
+
+
+@spaces.GPU
+def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
+    """
+    Extract a Gaussian splatting file from the generated 3D model.
     
-    gr.Markdown("# 🚀 Enhanced Gradio 6 App")
-    gr.Markdown("Enter your name and choose enthusiasm level for a personalized greeting.")
+    This function is called when the user clicks "Extract Gaussian" button.
+    It converts the 3D model state into a .ply file format containing
+    Gaussian splatting data for advanced 3D applications.
+    Args:
+        state (dict): The state of the generated 3D model containing Gaussian data
+        req (gr.Request): Gradio request object for session management
+    Returns:
+        Tuple[str, str]: Paths to the extracted Gaussian file (for display and download)
+    """
+    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[Image.Image]:
+    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
+    images = []
+    for case in multi_case:
+        _images = []
+        for i in range(1, 9):
+            if os.path.exists(f'assets/example_multi_image/{case}_{i}.png'):
+                img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
+                W, H = img.size
+                img = img.resize((int(W / H * 512), 512))
+                _images.append(np.array(img))
+        if len(_images) > 0:
+            images.append(Image.fromarray(np.concatenate(_images, axis=1)))
+    return images
+
+
+def split_image(image: Image.Image) -> List[Image.Image]:
+    """
+    Split a multi-view image into separate view images.
+    
+    This function is called when users select multi-image examples that contain
+    multiple views in a single concatenated image. It automatically splits them
+    based on alpha channel boundaries and preprocesses each view.
+    
+    Args:
+        image (Image.Image): A concatenated image containing multiple views
+        
+    Returns:
+        List[Image.Image]: List of individual preprocessed view images
+    """
+    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]
+
+# Create interface
+demo = gr.Blocks(
+    title="ReconViaGen",
+    css="""
+        .slider .inner { width: 5px; background: #FFF; }
+        .viewport { aspect-ratio: 4/3; }
+        .tabs button.selected { font-size: 20px !important; color: crimson !important; }
+        h1, h2, h3 { text-align: center; display: block; }
+        .md_feedback li { margin-bottom: 0px !important; }
+    """
+)
+with demo:
+    gr.Markdown("""
+    # 💻 ReconViaGen
+    <p align="center">
+    <a title="Github" href="https://github.com/GAP-LAB-CUHK-SZ/ReconViaGen" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://img.shields.io/github/stars/GAP-LAB-CUHK-SZ/ReconViaGen?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
+    </a>
+    <a title="Website" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
+    </a>
+    <a title="arXiv" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
+    </a>
+    </p>
+    
+    ✨This demo is partial. We will release the whole model later. Stay tuned!✨
+    """)
     
     with gr.Row():
         with gr.Column():
-            name_input = gr.Textbox(
-                label="Your Name",
-                placeholder="Type your name here...",
-                autofocus=True,
-                max_length=50
-            )
-            enthusiasm_slider = gr.Slider(
-                label="Enthusiasm Level",
-                minimum=1,
-                maximum=5,
-                value=1,
-                step=1,
-                info="How excited should the greeting be?"
-            )
-            submit_btn = gr.Button("Generate Greeting", variant="primary", size="lg")
-            
-            # Add examples
-            examples = gr.Examples(
-                examples=[
-                    ["Alice", 3],
-                    ["Bob", 1],
-                    ["Charlie", 5]
-                ],
-                inputs=[name_input, enthusiasm_slider],
-                label="Quick Examples"
-            )
+            with gr.Tabs() as input_tabs:
+                with gr.Tab(label="Input Video or Images", id=0) as multiimage_input_tab:
+                    input_video = gr.Video(label="Upload Video", interactive=True, height=300)
+                    image_prompt = gr.Image(label="Image Prompt", format="png", visible=False, image_mode="RGBA", type="pil", height=300)
+                    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.
+                                """)
+        
+            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=False)
+                gr.Markdown("Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=30, step=1)
+                gr.Markdown("Stage 2: Structured Latent Generation")
+                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="multidiffusion")
             
+            with gr.Accordion(label="GLB Extraction Settings", open=False):
+                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)
+
+            generate_btn = gr.Button("Generate & Extract GLB", variant="primary")
+            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.*
+                        """)
+
         with gr.Column():
-            greeting_output = gr.Textbox(
-                label="Output",
-                interactive=False,
-                lines=2,
-                show_copy_button=True
-            )
-            # Add a stats component
-            char_count = gr.Number(label="Character Count", interactive=False)
-    
-    # Event listeners with Gradio 6 syntax
-    def process_greeting(name, enthusiasm):
-        result = greet(name, enthusiasm)
-        return result, len(result)
-    
-    submit_btn.click(
-        fn=process_greeting,
-        inputs=[name_input, enthusiasm_slider],
-        outputs=[greeting_output, char_count],
-        api_visibility="public"
-    )
+            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)  
     
-    # Real-time updates
-    name_input.change(
-        fn=process_greeting,
-        inputs=[name_input, enthusiasm_slider],
-        outputs=[greeting_output, char_count],
-        api_visibility="private"
+    output_buf = gr.State()
+
+    # Example images at the bottom of the page
+    with gr.Row() 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)
+    demo.unload(end_session)
+
+    input_video.upload(
+        preprocess_videos,
+        inputs=[input_video],
+        outputs=[multiimage_prompt],
+    )
+    input_video.clear(
+        lambda: tuple([None, None]),
+        outputs=[input_video, multiimage_prompt],
+    )
+    multiimage_prompt.upload(
+        preprocess_images,
+        inputs=[multiimage_prompt],
+        outputs=[multiimage_prompt],
+    )
+
+    generate_btn.click(
+        get_seed,
+        inputs=[randomize_seed, seed],
+        outputs=[seed],
+    ).then(
+        generate_and_extract_glb,
+        inputs=[multiimage_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo, mesh_simplify, texture_size],
+        outputs=[output_buf, video_output, model_output, download_glb],
+    ).then(
+        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
+        outputs=[extract_gs_btn, download_glb],
+    )
+
+    video_output.clear(
+        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False), gr.Button(interactive=False)]),
+        outputs=[extract_gs_btn, download_glb, download_gs],
     )
     
-    enthusiasm_slider.change(
-        fn=process_greeting,
-        inputs=[name_input, enthusiasm_slider],
-        outputs=[greeting_output, char_count],
-        api_visibility="private"
+    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: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
+        outputs=[download_glb, download_gs],
     )
+    
 
-demo.launch(
-    theme=gr.themes.Soft(
-        primary_hue="blue",
-        secondary_hue="cyan",
-        neutral_hue="slate",
-        font=gr.themes.GoogleFont("Inter"),
-        text_size="lg",
-        spacing_size="lg",
-        radius_size="md"
-    ).set(
-        button_primary_background_fill="*primary_600",
-        button_primary_background_fill_hover="*primary_700"
-    ),
-    footer_links=[
-        {"label": "Gradio", "url": "https://gradio.app"},
-        {"label": "GitHub", "url": "https://github.com/gradio-app/gradio"}
-    ],
-    show_error=True
-)
+# Launch the Gradio app
+if __name__ == "__main__":
+    pipeline = TrellisVGGTTo3DPipeline.from_pretrained("Stable-X/trellis-vggt-v0-2")
+    pipeline.cuda()
+    pipeline.VGGT_model.cuda()
+    pipeline.birefnet_model.cuda()
+    demo.launch()