| import gradio as gr
|
| from gradio_litmodel3d import LitModel3D
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|
|
| import os
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| import shutil
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| from typing import *
|
| import torch
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| import numpy as np
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| import imageio
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| from easydict import EasyDict as edict
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| from PIL import Image
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| from trellis.pipelines import TrellisImageTo3DPipeline
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| from trellis.representations import Gaussian, MeshExtractResult
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| from trellis.utils import render_utils, postprocessing_utils
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|
|
|
|
| MAX_SEED = np.iinfo(np.int32).max
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| TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
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| os.makedirs(TMP_DIR, exist_ok=True)
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|
|
|
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| def start_session(req: gr.Request):
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| user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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| os.makedirs(user_dir, exist_ok=True)
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|
|
|
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| def end_session(req: gr.Request):
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| user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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| shutil.rmtree(user_dir)
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|
|
|
|
| def preprocess_image(image: Image.Image) -> Image.Image:
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| """
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| Preprocess the input image.
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|
|
| Args:
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| image (Image.Image): The input image.
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|
|
| Returns:
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| Image.Image: The preprocessed image.
|
| """
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| processed_image = pipeline.preprocess_image(image)
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| return processed_image
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|
|
|
|
| def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
|
| """
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| Preprocess a list of input images.
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|
|
| Args:
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| images (List[Tuple[Image.Image, str]]): The input images.
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|
|
| Returns:
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| List[Image.Image]: The preprocessed images.
|
| """
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| images = [image[0] for image in images]
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| processed_images = [pipeline.preprocess_image(image) for image in images]
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| return processed_images
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|
|
|
|
| def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
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| return {
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| 'gaussian': {
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| **gs.init_params,
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| '_xyz': gs._xyz.cpu().numpy(),
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| '_features_dc': gs._features_dc.cpu().numpy(),
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| '_scaling': gs._scaling.cpu().numpy(),
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| '_rotation': gs._rotation.cpu().numpy(),
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| '_opacity': gs._opacity.cpu().numpy(),
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| },
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| 'mesh': {
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| 'vertices': mesh.vertices.cpu().numpy(),
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| 'faces': mesh.faces.cpu().numpy(),
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| },
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| }
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|
|
|
|
| def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
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| gs = Gaussian(
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| aabb=state['gaussian']['aabb'],
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| sh_degree=state['gaussian']['sh_degree'],
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| mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
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| scaling_bias=state['gaussian']['scaling_bias'],
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| opacity_bias=state['gaussian']['opacity_bias'],
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| scaling_activation=state['gaussian']['scaling_activation'],
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| )
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| gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
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| gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
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| gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
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| gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
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| gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
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|
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| mesh = edict(
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| vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
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| faces=torch.tensor(state['mesh']['faces'], device='cuda'),
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| )
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|
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| return gs, mesh
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|
|
|
|
| def get_seed(randomize_seed: bool, seed: int) -> int:
|
| """
|
| Get the random seed.
|
| """
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| return np.random.randint(0, MAX_SEED) if randomize_seed else seed
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|
|
|
|
| def image_to_3d(
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| image: Image.Image,
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| multiimages: List[Tuple[Image.Image, str]],
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| is_multiimage: bool,
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| seed: int,
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| ss_guidance_strength: float,
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| ss_sampling_steps: int,
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| slat_guidance_strength: float,
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| slat_sampling_steps: int,
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| multiimage_algo: Literal["multidiffusion", "stochastic"],
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| req: gr.Request,
|
| ) -> Tuple[dict, str]:
|
| """
|
| Convert an image to a 3D model.
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|
|
| Args:
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| image (Image.Image): The input image.
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| multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
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| is_multiimage (bool): Whether is in multi-image mode.
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| seed (int): The random seed.
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| ss_guidance_strength (float): The guidance strength for sparse structure generation.
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| ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
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| slat_guidance_strength (float): The guidance strength for structured latent generation.
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| slat_sampling_steps (int): The number of sampling steps for structured latent generation.
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| multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
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|
|
| Returns:
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| dict: The information of the generated 3D model.
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| str: The path to the video of the 3D model.
|
| """
|
| user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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| if not is_multiimage:
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| outputs = pipeline.run(
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| image,
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| seed=seed,
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| formats=["gaussian", "mesh"],
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| preprocess_image=False,
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| sparse_structure_sampler_params={
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| "steps": ss_sampling_steps,
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| "cfg_strength": ss_guidance_strength,
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| },
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| slat_sampler_params={
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| "steps": slat_sampling_steps,
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| "cfg_strength": slat_guidance_strength,
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| },
|
| )
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| else:
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| outputs = pipeline.run_multi_image(
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| [image[0] for image in multiimages],
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| seed=seed,
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| formats=["gaussian", "mesh"],
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| preprocess_image=False,
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| sparse_structure_sampler_params={
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| "steps": ss_sampling_steps,
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| "cfg_strength": ss_guidance_strength,
|
| },
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| slat_sampler_params={
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| "steps": slat_sampling_steps,
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| "cfg_strength": slat_guidance_strength,
|
| },
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| mode=multiimage_algo,
|
| )
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| video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
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| video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
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| video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
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| video_path = os.path.join(user_dir, 'sample.mp4')
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| imageio.mimsave(video_path, video, fps=15)
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| state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
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| torch.cuda.empty_cache()
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| return state, video_path
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|
|
|
|
| def extract_glb(
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| state: dict,
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| mesh_simplify: float,
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| texture_size: int,
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| 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:
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| str: The path to the extracted GLB file.
|
| """
|
| user_dir = os.path.join(TMP_DIR, str(req.session_hash))
|
| gs, mesh = unpack_state(state)
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| glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
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| glb_path = os.path.join(user_dir, 'sample.glb')
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| glb.export(glb_path)
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| torch.cuda.empty_cache()
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| return glb_path, glb_path
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|
|
|
|
| def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
|
| """
|
| Extract a Gaussian file from the 3D model.
|
|
|
| Args:
|
| state (dict): The state of the generated 3D model.
|
|
|
| Returns:
|
| str: The path to the extracted Gaussian file.
|
| """
|
| user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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| gs, _ = unpack_state(state)
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| gaussian_path = os.path.join(user_dir, 'sample.ply')
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| gs.save_ply(gaussian_path)
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| torch.cuda.empty_cache()
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| return gaussian_path, gaussian_path
|
|
|
|
|
| def prepare_multi_example() -> List[Image.Image]:
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| multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
|
| images = []
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| for case in multi_case:
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| _images = []
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| for i in range(1, 4):
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| img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
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| W, H = img.size
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| img = img.resize((int(W / H * 512), 512))
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| _images.append(np.array(img))
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| images.append(Image.fromarray(np.concatenate(_images, axis=1)))
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| return images
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|
|
|
|
| def split_image(image: Image.Image) -> List[Image.Image]:
|
| """
|
| Split an image into multiple views.
|
| """
|
| image = np.array(image)
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| alpha = image[..., 3]
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| alpha = np.any(alpha>0, axis=0)
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| start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
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| end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
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| images = []
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| for s, e in zip(start_pos, end_pos):
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| images.append(Image.fromarray(image[:, s:e+1]))
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| return [preprocess_image(image) for image in images]
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|
|
|
|
| with gr.Blocks(delete_cache=(600, 600)) as demo:
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| gr.Markdown("""
|
| ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
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| * 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.
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| * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
|
| """)
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|
|
| with gr.Row():
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| with gr.Column():
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| with gr.Tabs() as input_tabs:
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| with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
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| image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
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| with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
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| multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
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| gr.Markdown("""
|
| Input different views of the object in separate images.
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|
|
| *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
|
| """)
|
|
|
| with gr.Accordion(label="Generation Settings", open=False):
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| seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
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| randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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| gr.Markdown("Stage 1: Sparse Structure Generation")
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| with gr.Row():
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| ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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| ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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| gr.Markdown("Stage 2: Structured Latent Generation")
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| with gr.Row():
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| slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
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| slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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| multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
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|
|
| generate_btn = gr.Button("Generate")
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|
|
| with gr.Accordion(label="GLB Extraction Settings", open=False):
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| mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
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| texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
|
|
|
| with gr.Row():
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| extract_glb_btn = gr.Button("Extract GLB", interactive=False)
|
| extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
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| gr.Markdown("""
|
| *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
|
| """)
|
|
|
| with gr.Column():
|
| video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
|
| model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
|
|
|
| with gr.Row():
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| download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
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| download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
|
|
|
| is_multiimage = gr.State(False)
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| output_buf = gr.State()
|
|
|
|
|
| with gr.Row() as single_image_example:
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| examples = gr.Examples(
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| examples=[
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| f'assets/example_image/{image}'
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| for image in os.listdir("assets/example_image")
|
| ],
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| inputs=[image_prompt],
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| fn=preprocess_image,
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| outputs=[image_prompt],
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| run_on_click=True,
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| examples_per_page=64,
|
| )
|
| with gr.Row(visible=False) as multiimage_example:
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| examples_multi = gr.Examples(
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| examples=prepare_multi_example(),
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| inputs=[image_prompt],
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| fn=split_image,
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| outputs=[multiimage_prompt],
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| run_on_click=True,
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| examples_per_page=8,
|
| )
|
|
|
|
|
| demo.load(start_session)
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| demo.unload(end_session)
|
|
|
| single_image_input_tab.select(
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| lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
|
| outputs=[is_multiimage, single_image_example, multiimage_example]
|
| )
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| multiimage_input_tab.select(
|
| lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
|
| outputs=[is_multiimage, single_image_example, multiimage_example]
|
| )
|
|
|
| image_prompt.upload(
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| preprocess_image,
|
| inputs=[image_prompt],
|
| outputs=[image_prompt],
|
| )
|
| multiimage_prompt.upload(
|
| preprocess_images,
|
| inputs=[multiimage_prompt],
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| outputs=[multiimage_prompt],
|
| )
|
|
|
| generate_btn.click(
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| get_seed,
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| inputs=[randomize_seed, seed],
|
| outputs=[seed],
|
| ).then(
|
| image_to_3d,
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| inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
|
| outputs=[output_buf, video_output],
|
| ).then(
|
| lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
|
| outputs=[extract_glb_btn, extract_gs_btn],
|
| )
|
|
|
| video_output.clear(
|
| lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
|
| outputs=[extract_glb_btn, extract_gs_btn],
|
| )
|
|
|
| extract_glb_btn.click(
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| extract_glb,
|
| inputs=[output_buf, mesh_simplify, texture_size],
|
| outputs=[model_output, download_glb],
|
| ).then(
|
| lambda: gr.Button(interactive=True),
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| outputs=[download_glb],
|
| )
|
|
|
| extract_gs_btn.click(
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| extract_gaussian,
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| inputs=[output_buf],
|
| outputs=[model_output, download_gs],
|
| ).then(
|
| lambda: gr.Button(interactive=True),
|
| outputs=[download_gs],
|
| )
|
|
|
| model_output.clear(
|
| lambda: gr.Button(interactive=False),
|
| outputs=[download_glb],
|
| )
|
|
|
|
|
|
|
| if __name__ == "__main__":
|
| pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
|
| pipeline.cuda()
|
| demo.launch()
|
|
|
