Create app.py

app.py

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+import gradio as gr
+import spaces
+from gradio_litmodel3d import LitModel3D
+
+import os
+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
+
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# Log environment variables
+logger.info(f"ATTN_BACKEND: {os.environ.get('ATTN_BACKEND')}")
+logger.info(f"ATTN_DEBUG: {os.environ.get('ATTN_DEBUG')}")
+logger.info(f"SPARSE_BACKEND: {os.environ.get('SPARSE_BACKEND')}")
+logger.info(f"SPARSE_DEBUG: {os.environ.get('SPARSE_DEBUG')}")
+logger.info(f"SPARSE_ATTN_BACKEND: {os.environ.get('SPARSE_ATTN_BACKEND')}")
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = "/tmp/Trellis-demo"
+
+os.makedirs(TMP_DIR, exist_ok=True)
+
+
+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.
+    """
+    trial_id = str(uuid.uuid4())
+    processed_image = pipeline.preprocess_image(image)
+    processed_image.save(f"{TMP_DIR}/{trial_id}.png")
+    return trial_id, 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']
+
+
+@spaces.GPU
+def image_to_3d(trial_id: str, seed: int, randomize_seed: bool, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int) -> Tuple[dict, str]:
+    """
+    Convert an image to a 3D model.
+    Args:
+        trial_id (str): The uuid of the trial.
+        seed (int): The random seed.
+        randomize_seed (bool): Whether to randomize the 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.
+    """
+    if randomize_seed:
+        seed = np.random.randint(0, MAX_SEED)
+    outputs = pipeline.run(
+        Image.open(f"{TMP_DIR}/{trial_id}.png"),
+        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))]
+    trial_id = uuid.uuid4()
+    video_path = f"{TMP_DIR}/{trial_id}.mp4"
+    os.makedirs(os.path.dirname(video_path), exist_ok=True)
+    imageio.mimsave(video_path, video, fps=15)
+    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
+    return state, video_path
+
+
+@spaces.GPU
+def extract_glb(state: dict, mesh_simplify: float, texture_size: int) -> 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.
+    """
+    gs, mesh, trial_id = unpack_state(state)
+    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
+    glb_path = f"{TMP_DIR}/{trial_id}.glb"
+    glb.export(glb_path)
+    return glb_path, glb_path
+
+
+def activate_button() -> gr.Button:
+    return gr.Button(interactive=True)
+
+
+def deactivate_button() -> gr.Button:
+    return gr.Button(interactive=False)
+
+
+with gr.Blocks() 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. 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.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_prompt = gr.Image(label="Image Prompt", 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, 50, 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, 50, 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(0.9, 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 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", exposure=20.0, height=300)
+            download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
+            
+    trial_id = gr.Textbox(visible=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=[trial_id, image_prompt],
+            run_on_click=True,
+            examples_per_page=64,
+        )
+
+    # Handlers
+    image_prompt.upload(
+        preprocess_image,
+        inputs=[image_prompt],
+        outputs=[trial_id, image_prompt],
+    )
+    image_prompt.clear(
+        lambda: '',
+        outputs=[trial_id],
+    )
+
+    generate_btn.click(
+        image_to_3d,
+        inputs=[trial_id, seed, randomize_seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
+        outputs=[output_buf, video_output],
+    ).then(
+        activate_button,
+        outputs=[extract_glb_btn],
+    )
+
+    video_output.clear(
+        deactivate_button,
+        outputs=[extract_glb_btn],
+    )
+
+    extract_glb_btn.click(
+        extract_glb,
+        inputs=[output_buf, mesh_simplify, texture_size],
+        outputs=[model_output, download_glb],
+    ).then(
+        activate_button,
+        outputs=[download_glb],
+    )
+
+    model_output.clear(
+        deactivate_button,
+        outputs=[download_glb],
+    )
+    
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    if torch.cuda.is_available():
+        pipeline.cuda()
+        print("CUDA is available. Using GPU.")
+    else:
+        print("CUDA not available. Falling back to CPU.")
+    try:
+        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+    except:
+        pass
+    print(f"CUDA Available: {torch.cuda.is_available()}")
+    print(f"CUDA Version: {torch.version.cuda}")
+    print(f"Number of GPUs: {torch.cuda.device_count()}")
+    demo.launch(debug=True)