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]: 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: return np.random.randint(0, MAX_SEED) if randomize_seed else seed 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, str]: """ Convert an image to a 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, }, ) # Generate video preview 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 = str(uuid.uuid4()) video_path = os.path.join(user_dir, f"{trial_id}.mp4") imageio.mimsave(video_path, video, fps=15) # Save full quality GLB glb = postprocessing_utils.to_glb( outputs['gaussian'][0], outputs['mesh'][0], simplify=0.0, # No simplification for full quality fill_holes=True, fill_holes_max_size=0.04, texture_size=2048, # Maximum texture size verbose=False ) full_glb_path = os.path.join(user_dir, f"{trial_id}_full.glb") glb.export(full_glb_path) # Pack state for potential reduced version state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id) return state, video_path, full_glb_path def extract_reduced_glb( state: dict, mesh_simplify: float, texture_size: int, req: gr.Request, ) -> Tuple[str, str]: """ Extract a reduced quality GLB file. """ user_dir = os.path.join(TMP_DIR, str(req.session_hash)) gs, mesh, trial_id = unpack_state(state) # Create reduced quality GLB with user settings 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 ) reduced_glb_path = os.path.join(user_dir, f"{trial_id}_reduced.glb") glb.export(reduced_glb_path) return reduced_glb_path, reduced_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 (no mesh simplification, maximum texture resolution) * Or create a reduced size version with customizable settings """) 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="Reduced GLB Settings", open=False): mesh_simplify = gr.Slider(0.0, 0.98, label="Mesh Simplification", value=0.95, step=0.01, info="Higher values = more reduction") texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512) extract_reduced_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) gr.Markdown("### Download Options") 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 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, inputs=[randomize_seed, seed], outputs=[seed], ).then( image_to_3d,