mirror of
https://github.com/microsoft/TRELLIS.2
synced 2026-04-25 17:15:37 +02:00
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
import os
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import json
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from typing import *
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import numpy as np
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import torch
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from .. import models
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from .components import ImageConditionedMixin
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from ..modules.sparse import SparseTensor
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from .structured_latent import SLatVisMixin, SLat
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from ..utils.render_utils import get_renderer, yaw_pitch_r_fov_to_extrinsics_intrinsics
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class SLatShapeVisMixin(SLatVisMixin):
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def _loading_slat_dec(self):
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if self.slat_dec is not None:
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return
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if self.slat_dec_path is not None:
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cfg = json.load(open(os.path.join(self.slat_dec_path, 'config.json'), 'r'))
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decoder = getattr(models, cfg['models']['decoder']['name'])(**cfg['models']['decoder']['args'])
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ckpt_path = os.path.join(self.slat_dec_path, 'ckpts', f'decoder_{self.slat_dec_ckpt}.pt')
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decoder.load_state_dict(torch.load(ckpt_path, map_location='cpu', weights_only=True))
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else:
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decoder = models.from_pretrained(self.pretrained_slat_dec)
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decoder.set_resolution(self.resolution)
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self.slat_dec = decoder.cuda().eval()
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@torch.no_grad()
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def visualize_sample(self, x_0: Union[SparseTensor, dict]):
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x_0 = x_0 if isinstance(x_0, SparseTensor) else x_0['x_0']
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reps = self.decode_latent(x_0.cuda())
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# build camera
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yaw = [0, np.pi/2, np.pi, 3*np.pi/2]
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yaw_offset = -16 / 180 * np.pi
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yaw = [y + yaw_offset for y in yaw]
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pitch = [20 / 180 * np.pi for _ in range(4)]
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exts, ints = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaw, pitch, 2, 30)
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# render
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renderer = get_renderer(reps[0])
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images = []
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for representation in reps:
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image = torch.zeros(3, 1024, 1024).cuda()
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tile = [2, 2]
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for j, (ext, intr) in enumerate(zip(exts, ints)):
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res = renderer.render(representation, ext, intr)
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image[:, 512 * (j // tile[1]):512 * (j // tile[1] + 1), 512 * (j % tile[1]):512 * (j % tile[1] + 1)] = res['normal']
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images.append(image)
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images = torch.stack(images)
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return images
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class SLatShape(SLatShapeVisMixin, SLat):
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"""
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structured latent for shape generation
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Args:
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roots (str): path to the dataset
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resolution (int): resolution of the shape
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min_aesthetic_score (float): minimum aesthetic score
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max_tokens (int): maximum number of tokens
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latent_key (str): key of the latent to be used
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normalization (dict): normalization stats
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pretrained_slat_dec (str): name of the pretrained slat decoder
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slat_dec_path (str): path to the slat decoder, if given, will override the pretrained_slat_dec
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slat_dec_ckpt (str): name of the slat decoder checkpoint
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"""
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def __init__(self,
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roots: str,
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*,
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resolution: int,
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min_aesthetic_score: float = 5.0,
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max_tokens: int = 32768,
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normalization: Optional[dict] = None,
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pretrained_slat_dec: str = 'microsoft/TRELLIS.2-4B/ckpts/shape_dec_next_dc_f16c32_fp16',
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slat_dec_path: Optional[str] = None,
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slat_dec_ckpt: Optional[str] = None,
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):
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super().__init__(
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roots,
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min_aesthetic_score=min_aesthetic_score,
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max_tokens=max_tokens,
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latent_key='shape_latent',
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normalization=normalization,
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pretrained_slat_dec=pretrained_slat_dec,
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slat_dec_path=slat_dec_path,
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slat_dec_ckpt=slat_dec_ckpt,
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)
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self.resolution = resolution
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class ImageConditionedSLatShape(ImageConditionedMixin, SLatShape):
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"""
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Image conditioned structured latent for shape generation
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"""
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pass
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