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 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)) os.makedirs(user_dir, exist_ok=True) def end_session(req: gr.Request): user_dir = os.path.join(TMP_DIR, str(req.session_hash)) shutil.rmtree(user_dir) def preprocess_image(image: Image.Image) -> Image.Image: """ Preprocess the input image. Args: image (Image.Image): The input image. Returns: Image.Image: The preprocessed image. """ processed_image = pipeline.preprocess_image(image) return processed_image def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]: """ Preprocess a list of input images. Args: images (List[Tuple[Image.Image, str]]): The input images. Returns: List[Image.Image]: The preprocessed images. """ images = [image[0] for image in images] processed_images = [pipeline.preprocess_image(image) for image in images] return processed_images def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> 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(), }, } 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 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. Args: image (Image.Image): The input image. multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode. is_multiimage (bool): Whether is in multi-image mode. seed (int): The random 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. multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation. Returns: dict: The information of the generated 3D model. str: The path to the video of the 3D model. """ user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) 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=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))] video_path = os.path.join(user_dir, 'sample.mp4') imageio.mimsave(video_path, video, fps=15) state = pack_state(outputs['gaussian'][0], outputs['mesh'][0]) torch.cuda.empty_cache() return state, video_path @spaces.GPU(duration=90) 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. 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. """ user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) gs, mesh = 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, 'sample.glb') glb.export(glb_path) torch.cuda.empty_cache() return glb_path, glb_path def prepare_multi_example() -> List[Image.Image]: multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")])) images = [] for case in multi_case: _images = [] for i in range(1, 4): img = Image.open(f'assets/example_multi_image/{case}_{i}.png') W, H = img.size img = img.resize((int(W / H * 512), 512)) _images.append(np.array(img)) images.append(Image.fromarray(np.concatenate(_images, axis=1))) return images def split_image(image: Image.Image) -> List[Image.Image]: """ Split an image into multiple views. """ image = np.array(image) alpha = image[..., 3] alpha = np.any(alpha>0, axis=0) start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist() end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist() images = [] for s, e in zip(start_pos, end_pos): images.append(Image.fromarray(image[:, s:e+1])) return [preprocess_image(image) for image in images] with gr.Blocks(delete_cache=(600, 600)) as demo: gr.Markdown(""" ## Image to 3D Asset Generator * Upload an image and click "Generate" to create a 3D asset. * 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(): with gr.Tabs() as input_tabs: with gr.Tab(label="Single Image", id=0) as single_image_input_tab: 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, 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) with gr.Row(): 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=10.0, height=300) with gr.Row(): download_glb = gr.DownloadButton(label="Download GLB", interactive=False) output_buf = gr.State() # Example images at the bottom of the page with gr.Row() as single_image_example: 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, ) # Handlers demo.load(start_session) demo.unload(end_session) single_image_input_tab.select( lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]), outputs=[single_image_example] ) 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), outputs=[extract_glb_btn], ) video_output.clear( lambda: gr.Button(interactive=False), outputs=[extract_glb_btn], ) extract_glb_btn.click( extract_glb, inputs=[output_buf, mesh_simplify, texture_size], outputs=[model_output, download_glb], ).then( lambda: gr.Button(interactive=True), outputs=[download_glb], ) model_output.clear( lambda: gr.Button(interactive=False), outputs=[download_glb], ) # Launch the Gradio app 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(show_api=False)