import gradio as gr import spaces from gradio_litmodel3d import LitModel3D 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 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 @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. """ 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=10)['color'] video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=10)['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) state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id) return state, video_path @spaces.GPU def extract_full_glb( state: dict, req: gr.Request, ) -> Tuple[str, str]: """ Extract a full-quality 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=0.0, # No simplification fill_holes=True, fill_holes_max_size=0.04, texture_size=2048, # Maximum texture resolution verbose=False ) glb_path = os.path.join(user_dir, f"{trial_id}_full.glb") glb.export(glb_path) return glb_path, glb_path @spaces.GPU def extract_glb( state: dict, mesh_simplify: float, texture_size: int, req: gr.Request, ) -> Tuple[str, str]: """ Extract a reduced-quality 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, texture_size=texture_size, verbose=False) glb_path = os.path.join(user_dir, f"{trial_id}.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. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background. * After generation: * Click "Extract Full GLB" for maximum quality (no mesh reduction) * Or use the GLB Extraction Settings for a reduced size version """) 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") extract_full_btn = gr.Button("Extract Full GLB", interactive=False) 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, 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: [gr.Button(interactive=True), gr.Button(interactive=True)], outputs=[extract_full_btn, extract_glb_btn], ) extract_full_btn.click( extract_full_glb, inputs=[output_buf], outputs=[model_output, download_full], ).then( lambda: gr.Button(interactive=True), outputs=[download_full], ) 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))) # Preload rembg except: pass demo.launch()