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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 | |
| MAX_SEED = np.iinfo(np.int32).max | |
| def preprocess_image(image: Image.Image) -> Tuple[np.array, Image.Image]: | |
| """ | |
| Preprocess the input image. | |
| Args: | |
| image (Image.Image): The input image. | |
| Returns: | |
| np.array: The preprocessed image. | |
| Image.Image: The preprocessed image. | |
| """ | |
| processed_image = pipeline.preprocess_image(image) | |
| return np.array(processed_image), processed_image | |
| def pack_state(gs: Gaussian, mesh: MeshExtractResult, model_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(), | |
| }, | |
| 'model_id': model_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['model_id'] | |
| def image_to_3d(image: np.array, 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: | |
| image (np.array): The input image. | |
| 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.fromarray(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=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))] | |
| model_id = uuid.uuid4() | |
| video_path = f"/tmp/Trellis-demo/{model_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], model_id) | |
| return state, video_path | |
| 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, model_id = unpack_state(state) | |
| glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False) | |
| glb_path = f"/tmp/Trellis-demo/{model_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) | |
| image = gr.State() | |
| model = 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=lambda image: preprocess_image(image), | |
| outputs=[image, image_prompt], | |
| run_on_click=True, | |
| examples_per_page=64, | |
| ) | |
| # Handlers | |
| image_prompt.upload( | |
| preprocess_image, | |
| inputs=[image_prompt], | |
| outputs=[image, image_prompt], | |
| ) | |
| generate_btn.click( | |
| image_to_3d, | |
| inputs=[image, seed, randomize_seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps], | |
| outputs=[model, 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=[model, 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") | |
| pipeline.cuda() | |
| demo.launch() | |