Update app.py

app.py

@@ -1,4 +1,93 @@
-# [Previous imports and utility functions remain exactly the same until image_to_3d]
+import gradio as gr
+import os
+import shutil
+os.environ['SPCONV_ALGO'] = 'native'
+from typing import *
+import torch
+import numpy as np
+import imageio
+import uuid
+from easydict import EasyDict as edict
+from PIL import Image
+from trellis.pipelines import TrellisImageTo3DPipeline
+from trellis.representations import Gaussian, MeshExtractResult
+from trellis.utils import render_utils, postprocessing_utils
+from gradio_litmodel3d import LitModel3D
+
+
+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)
+
+
+def start_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    print(f'Creating user directory: {user_dir}')
+    os.makedirs(user_dir, exist_ok=True)
+    
+def end_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    print(f'Removing user directory: {user_dir}')
+    shutil.rmtree(user_dir)
+
+def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
+    """
+    Preprocess the input image.
+
+    Args:
+        image (Image.Image): The input image.
+
+    Returns:
+        str: uuid of the trial.
+        Image.Image: The preprocessed image.
+    """
+    processed_image = pipeline.preprocess_image(image)
+    return processed_image
+
+def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> 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(),
+        },
+        'trial_id': trial_id,
+    }
+    
+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, state['trial_id']
+
+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
 
 def image_to_3d(
     image: Image.Image,
@@ -27,7 +116,6 @@ def image_to_3d(
             "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))]
@@ -35,7 +123,7 @@ def image_to_3d(
     video_path = os.path.join(user_dir, f"{trial_id}.mp4")
     imageio.mimsave(video_path, video, fps=15)
     
-    # Generate full quality GLB
+    # Save full quality GLB
     glb = postprocessing_utils.to_glb(
         outputs['gaussian'][0], 
         outputs['mesh'][0],
@@ -51,7 +139,94 @@ def image_to_3d(
     state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
     return state, video_path, full_glb_path
 
-# [Rest of the code remains exactly the same, except for the event handler which needs to be updated]
+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.
+    """
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    gs, mesh, trial_id = unpack_state(state)
+    glb = postprocessing_utils.to_glb(
+        gs, mesh,
+        simplify=mesh_simplify,
+        fill_holes=True,
+        fill_holes_max_size=0.04,
+        texture_size=texture_size,
+        verbose=False
+    )
+    glb_path = os.path.join(user_dir, f"{trial_id}_reduced.glb")
+    glb.export(glb_path)
+    return glb_path, glb_path
+
+with gr.Blocks(delete_cache=(600, 600)) as demo:
+    gr.Markdown("""
+    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
+    * Upload an image and click "Generate" to create a 3D asset
+    * After generation:
+        * Download the full quality GLB immediately
+        * Or create a reduced size version with the extraction settings below
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
+            
+            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)
+                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, 500, label="Sampling Steps", value=12, 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, 500, label="Sampling Steps", value=12, step=1)
+
+            generate_btn = gr.Button("Generate")
+            
+            with gr.Accordion(label="GLB Extraction Settings", open=False):
+                mesh_simplify = gr.Slider(0.0, 0.98, label="Simplify", value=0.95, step=0.01)
+                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
+            
+            extract_glb_btn = gr.Button("Extract Reduced GLB", interactive=False)
+
+        with gr.Column():
+            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
+            model_output = LitModel3D(label="3D Model Preview", exposure=20.0, height=300)
+            with gr.Row():
+                download_full = gr.DownloadButton(label="Download Full-Quality GLB", interactive=False)
+                download_reduced = gr.DownloadButton(label="Download Reduced GLB", interactive=False)
+            
+    output_buf = gr.State()
+
+    # Example images at the bottom of the page
+    with gr.Row():
+        examples = gr.Examples(
+            examples=[
+                f'assets/example_image/{image}'
+                for image in os.listdir("assets/example_image")
+            ],
+            inputs=[image_prompt],
+            fn=preprocess_image,
+            outputs=[image_prompt],
+            run_on_click=True,
+            examples_per_page=64,
+        )
+
+    # Event handlers
+    demo.load(start_session)
+    demo.unload(end_session)
+    
+    image_prompt.upload(
+        preprocess_image,
+        inputs=[image_prompt],
+        outputs=[image_prompt],
+    )
 
     generate_btn.click(
         get_seed,
@@ -66,4 +241,20 @@ def image_to_3d(
         outputs=[download_full, extract_glb_btn, download_reduced],
     )
 
-# [Rest of the code remains exactly the same]
+    extract_glb_btn.click(
+        extract_glb,
+        inputs=[output_buf, mesh_simplify, texture_size],
+        outputs=[model_output, download_reduced],
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_reduced],
+    )
+
+if __name__ == "__main__":
+    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    pipeline.cuda()
+    try:
+        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
+    except:
+        pass
+    demo.launch()