SYMBOL INDEX (502 symbols across 41 files)

FILE: docs/carousel.js
  function initCarousel (line 13) | function initCarousel(carouselId) {

FILE: main.py
  class SetupCallback (line 44) | class SetupCallback(Callback):
    method __init__ (line 45) | def __init__(self, config: DictConfig, basedir: Path, logdir: str = "l...
    method on_fit_start (line 51) | def on_fit_start(self, trainer: pl.trainer.Trainer, pl_module: pl.Ligh...
  function setup_callbacks (line 57) | def setup_callbacks(config: DictConfig) -> Tuple[List[Callback], Logger]:
  function merge_cfg (line 84) | def merge_cfg(cfg, arg_cfg):
  function get_args (line 92) | def get_args():

FILE: scripts/gradio_app.py
  function get_pipeline_cached (line 23) | def get_pipeline_cached(config_path, ckpt_path, device='cuda', low_vram=...
  function predict (line 85) | def predict(

FILE: scripts/infer_dit_refine.py
  function load_models (line 18) | def load_models(config_path, ckpt_path, device='cuda'):
  function run_inference (line 60) | def run_inference(args):

FILE: scripts/sampling.py
  function load_mesh (line 21) | def load_mesh(mesh_path: str, device: str = "cuda") -> Tuple[torch.Tenso...
  function compute_mesh_features (line 33) | def compute_mesh_features(vertices: torch.Tensor, faces: torch.Tensor) -...
  function sample_uniform_points (line 78) | def sample_uniform_points(
  function sample_surface_points (line 94) | def sample_surface_points(
  function normalize_points_and_mesh (line 195) | def normalize_points_and_mesh(vertices: torch.Tensor, points: torch.Tens...
  function add_gaussian_noise (line 210) | def add_gaussian_noise(uniform_surface_points: torch.Tensor, curvature_s...
  function compute_points_value_bvh (line 250) | def compute_points_value_bvh(
  function save_point_cloud (line 269) | def save_point_cloud(
  function sample_points_in_bbox (line 306) | def sample_points_in_bbox(
  function process_single_mesh (line 317) | def process_single_mesh(
  class MeshDataset (line 422) | class MeshDataset(Dataset):
    method __init__ (line 423) | def __init__(self, mesh_json: str):
    method __len__ (line 428) | def __len__(self) -> int:
    method __getitem__ (line 430) | def __getitem__(self, idx: int) -> dict:
  class MeshProcessor (line 439) | class MeshProcessor(pl.LightningModule):
    method __init__ (line 440) | def __init__(
    method predict_step (line 456) | def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int...
    method predict_dataloader (line 497) | def predict_dataloader(self) -> DataLoader:
  function process_mesh_directory (line 508) | def process_mesh_directory(

FILE: ultrashape/data/objaverse_dit.py
  function padding (line 44) | def padding(image, mask, center=True, padding_ratio_range=[1.15, 1.15]):
  class ObjaverseDataset (line 88) | class ObjaverseDataset(Dataset):
    method __init__ (line 89) | def __init__(
    method __len__ (line 127) | def __len__(self):
    method _load_shape (line 130) | def _load_shape(self, index: int) -> Dict[str, Any]:
    method _load_image (line 182) | def _load_image(self, index: int) -> Dict[str, Any]:
    method get_data (line 223) | def get_data(self, index):
    method __getitem__ (line 228) | def __getitem__(self, index):
    method collate (line 235) | def collate(self, batch):
  class ObjaverseDataModule (line 240) | class ObjaverseDataModule(pl.LightningDataModule):
    method __init__ (line 241) | def __init__(
    method train_dataloader (line 289) | def train_dataloader(self):
    method val_dataloader (line 311) | def val_dataloader(self):

FILE: ultrashape/data/objaverse_vae.py
  class ObjaverseDataset (line 45) | class ObjaverseDataset(Dataset):
    method __init__ (line 46) | def __init__(
    method __len__ (line 87) | def __len__(self):
    method _load_shape (line 90) | def _load_shape(self, index: int) -> Dict[str, Any]:
    method _clip_to_tsdf (line 166) | def _clip_to_tsdf(self, sdf: np.array):
    method get_data (line 172) | def get_data(self, index):
    method __getitem__ (line 176) | def __getitem__(self, index):
    method collate (line 179) | def collate(self, batch):
  class ObjaverseDataModule (line 184) | class ObjaverseDataModule(pl.LightningDataModule):
    method __init__ (line 185) | def __init__(
    method train_dataloader (line 220) | def train_dataloader(self):
    method val_dataloader (line 242) | def val_dataloader(self):

FILE: ultrashape/data/utils.py
  function make_seed (line 27) | def make_seed(*args):
  class PipelineStage (line 34) | class PipelineStage:
    method invoke (line 35) | def invoke(self, *args, **kw):
  function identity (line 39) | def identity(x: Any) -> Any:
  function safe_eval (line 44) | def safe_eval(s: str, expr: str = "{}"):
  function lookup_sym (line 51) | def lookup_sym(sym: str, modules: list):
  function repeatedly0 (line 61) | def repeatedly0(
  function guess_batchsize (line 69) | def guess_batchsize(batch: Union[tuple, list]):
  function repeatedly (line 74) | def repeatedly(
  function pytorch_worker_info (line 100) | def pytorch_worker_info(group=None):  # sourcery skip: use-contextlib-su...
  function pytorch_worker_seed (line 136) | def pytorch_worker_seed(group=None):
  function worker_init_fn (line 141) | def worker_init_fn(_):
  function collation_fn (line 155) | def collation_fn(samples, combine_tensors=True, combine_scalars=True):

FILE: ultrashape/models/autoencoders/attention_blocks.py
  class FourierEmbedder (line 55) | class FourierEmbedder(nn.Module):
    method __init__ (line 58) | def __init__(self,
    method get_dims (line 89) | def get_dims(self, input_dim):
    method forward (line 95) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DropPath (line 116) | class DropPath(nn.Module):
    method __init__ (line 120) | def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
    method forward (line 125) | def forward(self, x):
    method extra_repr (line 144) | def extra_repr(self):
  class MLP (line 148) | class MLP(nn.Module):
    method __init__ (line 149) | def __init__(
    method forward (line 163) | def forward(self, x):
  class QKVMultiheadCrossAttention (line 167) | class QKVMultiheadCrossAttention(nn.Module):
    method __init__ (line 168) | def __init__(
    method forward (line 185) | def forward(self, q, kv):
  class MultiheadCrossAttention (line 201) | class MultiheadCrossAttention(nn.Module):
    method __init__ (line 202) | def __init__(
    method forward (line 232) | def forward(self, x, data):
  class ResidualCrossAttentionBlock (line 246) | class ResidualCrossAttentionBlock(nn.Module):
    method __init__ (line 247) | def __init__(
    method forward (line 278) | def forward(self, x: torch.Tensor, data: torch.Tensor):
  class QKVMultiheadAttention (line 284) | class QKVMultiheadAttention(nn.Module):
    method __init__ (line 285) | def __init__(
    method forward (line 300) | def forward(self, qkv):
  class MultiheadAttention (line 314) | class MultiheadAttention(nn.Module):
    method __init__ (line 315) | def __init__(
    method forward (line 341) | def forward(self, x):
  class ResidualAttentionBlock (line 348) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 349) | def __init__(
    method forward (line 374) | def forward(self, x: torch.Tensor):
  class Transformer (line 380) | class Transformer(nn.Module):
    method __init__ (line 381) | def __init__(
    method forward (line 412) | def forward(self, x: torch.Tensor):
  class CrossAttentionDecoder (line 418) | class CrossAttentionDecoder(nn.Module):
    method __init__ (line 420) | def __init__(
    method set_cross_attention_processor (line 460) | def set_cross_attention_processor(self, processor):
    method set_default_cross_attention_processor (line 463) | def set_default_cross_attention_processor(self):
    method forward (line 466) | def forward(self, queries=None, query_embeddings=None, latents=None):
  function fps (line 479) | def fps(
  class PointCrossAttentionEncoder (line 493) | class PointCrossAttentionEncoder(nn.Module):
    method __init__ (line 495) | def __init__(
    method sample_points_and_latents (line 567) | def sample_points_and_latents(self, pc: torch.FloatTensor, feats: Opti...
    method forward (line 681) | def forward(self, pc, feats):

FILE: ultrashape/models/autoencoders/attention_processors.py
  class CrossAttentionProcessor (line 36) | class CrossAttentionProcessor:
    method __call__ (line 37) | def __call__(self, attn, q, k, v):
  class FlashVDMCrossAttentionProcessor (line 42) | class FlashVDMCrossAttentionProcessor:
    method __init__ (line 43) | def __init__(self, topk=None):
    method __call__ (line 46) | def __call__(self, attn, q, k, v):
    method select_topkv (line 80) | def select_topkv(self, q_chunk, k, v, topk):
  class FlashVDMTopMCrossAttentionProcessor (line 91) | class FlashVDMTopMCrossAttentionProcessor(FlashVDMCrossAttentionProcessor):
    method select_topkv (line 92) | def select_topkv(self, q_chunk, k, v, topk):

FILE: ultrashape/models/autoencoders/model.py
  class DiagonalGaussianDistribution (line 46) | class DiagonalGaussianDistribution(object):
    method __init__ (line 47) | def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]]...
    method sample (line 75) | def sample(self):
    method kl (line 85) | def kl(self, other=None, dims=(1, 2)):
    method nll (line 110) | def nll(self, sample, dims=(1, 2, 3)):
    method mode (line 118) | def mode(self):
  class VectsetVAE (line 122) | class VectsetVAE(nn.Module):
    method from_single_file (line 126) | def from_single_file(
    method from_pretrained (line 166) | def from_pretrained(
    method init_from_ckpt (line 194) | def init_from_ckpt(self, path, ignore_keys=()):
    method __init__ (line 209) | def __init__(
    method latents2mesh (line 222) | def latents2mesh(self, latents: torch.FloatTensor, **kwargs):
    method enable_flashvdm_decoder (line 229) | def enable_flashvdm_decoder(
  class ShapeVAE (line 249) | class ShapeVAE(VectsetVAE):
    method __init__ (line 250) | def __init__(
    method forward (line 340) | def forward(self, latents):
    method encode (line 345) | def encode(self, surface, sample_posterior=True, need_kl=False, need_v...
    method decode (line 361) | def decode(self, latents, voxel_idx=None):
    method query (line 366) | def query(self, latents, queries, voxel_idx=None):

FILE: ultrashape/models/autoencoders/surface_extractors.py
  class Latent2MeshOutput (line 30) | class Latent2MeshOutput:
    method __init__ (line 31) | def __init__(self, mesh_v=None, mesh_f=None):
  function center_vertices (line 36) | def center_vertices(vertices):
  class SurfaceExtractor (line 44) | class SurfaceExtractor:
    method _compute_box_stat (line 45) | def _compute_box_stat(self, bounds: Union[Tuple[float], List[float], f...
    method run (line 70) | def run(self, *args, **kwargs):
    method __call__ (line 81) | def __call__(self, grid_logits, **kwargs):
  function get_sparse_valid_voxels (line 109) | def get_sparse_valid_voxels(grid_logit: torch.Tensor):
  class MCSurfaceExtractor (line 196) | class MCSurfaceExtractor(SurfaceExtractor):
    method run (line 197) | def run(self, grid_logit, *, mc_level, bounds, octree_resolution, **kw...
  class DMCSurfaceExtractor (line 229) | class DMCSurfaceExtractor(SurfaceExtractor):
    method run (line 230) | def run(self, grid_logit, *, octree_resolution, **kwargs):

FILE: ultrashape/models/autoencoders/vae_trainer.py
  function export_to_trimesh (line 16) | def export_to_trimesh(mesh_output):
  class VAETrainer (line 32) | class VAETrainer(pl.LightningModule):
    method __init__ (line 33) | def __init__(
    method init_from_ckpt (line 68) | def init_from_ckpt(self, path, ignore_keys=()):
    method configure_optimizers (line 125) | def configure_optimizers(self) -> Tuple[List, List]:
    method on_train_epoch_start (line 151) | def on_train_epoch_start(self) -> None:
    method forward (line 154) | def forward(self, batch):
    method training_step (line 183) | def training_step(self, batch, batch_idx, optimizer_idx=0):
    method validation_step (line 196) | def validation_step(self, batch, batch_idx, optimizer_idx=0):

FILE: ultrashape/models/autoencoders/volume_decoders.py
  function extract_near_surface_volume_fn (line 36) | def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: fl...
  function generate_dense_grid_points (line 93) | def generate_dense_grid_points(
  class VanillaVolumeDecoder (line 112) | class VanillaVolumeDecoder:
    method __call__ (line 114) | def __call__(
  class HierarchicalVolumeDecoding (line 156) | class HierarchicalVolumeDecoding:
    method __call__ (line 158) | def __call__(
  class FlashVDMVolumeDecoding (line 259) | class FlashVDMVolumeDecoding:
    method __init__ (line 260) | def __init__(self, topk_mode='mean'):
    method __call__ (line 270) | def __call__(

FILE: ultrashape/models/conditioner_mask.py
  function get_1d_sincos_pos_embed_from_grid (line 45) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  class ImageEncoder (line 65) | class ImageEncoder(nn.Module):
    method __init__ (line 66) | def __init__(
    method forward (line 107) | def forward(self, image, mask=None, value_range=(-1, 1), **kwargs):
    method unconditional_embedding (line 168) | def unconditional_embedding(self, batch_size, **kwargs):
  class CLIPImageEncoder (line 185) | class CLIPImageEncoder(ImageEncoder):
  class DinoImageEncoder (line 192) | class DinoImageEncoder(ImageEncoder):
  class DinoImageEncoderMV (line 198) | class DinoImageEncoderMV(DinoImageEncoder):
    method __init__ (line 199) | def __init__(
    method forward (line 218) | def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None):
    method unconditional_embedding (line 254) | def unconditional_embedding(self, batch_size, view_idxs=None, **kwargs):
  function build_image_encoder (line 267) | def build_image_encoder(config):
  class DualImageEncoder (line 278) | class DualImageEncoder(nn.Module):
    method __init__ (line 279) | def __init__(
    method forward (line 288) | def forward(self, image, mask=None, **kwargs):
    method unconditional_embedding (line 295) | def unconditional_embedding(self, batch_size, **kwargs):
  class SingleImageEncoder (line 303) | class SingleImageEncoder(nn.Module):
    method __init__ (line 304) | def __init__(
    method forward (line 314) | def forward(self, image, disable_drop=True, mask=None, **kwargs):
    method unconditional_embedding (line 333) | def unconditional_embedding(self, batch_size, **kwargs):

FILE: ultrashape/models/denoisers/dit_mask.py
  function modulate (line 48) | def modulate(x, shift, scale):
  class Timesteps (line 52) | class Timesteps(nn.Module):
    method __init__ (line 53) | def __init__(self,
    method forward (line 65) | def forward(self, timesteps):
  class TimestepEmbedder (line 81) | class TimestepEmbedder(nn.Module):
    method __init__ (line 86) | def __init__(self, hidden_size, frequency_embedding_size=256, cond_pro...
    method forward (line 102) | def forward(self, t, condition):
  class MLP (line 115) | class MLP(nn.Module):
    method __init__ (line 116) | def __init__(self, *, width: int):
    method forward (line 123) | def forward(self, x):
  class CrossAttention (line 127) | class CrossAttention(nn.Module):
    method __init__ (line 128) | def __init__(
    method forward (line 157) | def forward(self, x, y):
  class Attention (line 246) | class Attention(nn.Module):
    method __init__ (line 251) | def __init__(
    method forward (line 276) | def forward(self, x, rotary_cos=None, rotary_sin=None):
  class DiTBlock (line 313) | class DiTBlock(nn.Module):
    method __init__ (line 314) | def __init__(
    method forward (line 380) | def forward(self, x, c=None, text_states=None, skip_value=None, rotary...
  class AttentionPool (line 410) | class AttentionPool(nn.Module):
    method __init__ (line 411) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 420) | def forward(self, x, attention_mask=None):
  class FinalLayer (line 452) | class FinalLayer(nn.Module):
    method __init__ (line 457) | def __init__(self, final_hidden_size, out_channels):
    method forward (line 463) | def forward(self, x):
  class RefineDiT (line 470) | class RefineDiT(nn.Module):
    method from_single_file (line 474) | def from_single_file(
    method from_pretrained (line 514) | def from_pretrained(
    method __init__ (line 540) | def __init__(
    method forward (line 602) | def forward(self, x, t, contexts, **kwargs):
  function apply_rotary_emb (line 639) | def apply_rotary_emb(x, cos, sin):
  function precompute_freqs_cis_3d (line 655) | def precompute_freqs_cis_3d(dim: int, grid_indices: torch.Tensor, theta:...
  function precompute_freqs_cis_3d_interpolated (line 682) | def precompute_freqs_cis_3d_interpolated(

FILE: ultrashape/models/denoisers/moe_layers.py
  class AddAuxiliaryLoss (line 24) | class AddAuxiliaryLoss(torch.autograd.Function):
    method forward (line 30) | def forward(ctx, x, loss):
    method backward (line 37) | def backward(ctx, grad_output):
  class MoEGate (line 43) | class MoEGate(nn.Module):
    method __init__ (line 44) | def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, a...
    method reset_parameters (line 59) | def reset_parameters(self) -> None:
    method forward (line 63) | def forward(self, hidden_states):
  class MoEBlock (line 112) | class MoEBlock(nn.Module):
    method __init__ (line 113) | def __init__(self, dim, num_experts=8, moe_top_k=2,
    method initialize_weight (line 131) | def initialize_weight(self):
    method forward (line 134) | def forward(self, hidden_states):
    method moe_infer (line 157) | def moe_infer(self, x, flat_expert_indices, flat_expert_weights):

FILE: ultrashape/models/diffusion/flow_matching_dit_trainer.py
  class Diffuser (line 18) | class Diffuser(pl.LightningModule):
    method __init__ (line 19) | def __init__(
    method ema_scope (line 122) | def ema_scope(self, context=None):
    method init_from_ckpt (line 136) | def init_from_ckpt(self, path, ignore_keys=()):
    method on_load_checkpoint (line 160) | def on_load_checkpoint(self, checkpoint):
    method configure_optimizers (line 176) | def configure_optimizers(self) -> Tuple[List, List]:
    method on_train_batch_end (line 231) | def on_train_batch_end(self, *args, **kwargs):
    method on_train_epoch_start (line 235) | def on_train_epoch_start(self) -> None:
    method forward (line 238) | def forward(self, batch, disable_drop):
    method training_step (line 254) | def training_step(self, batch, batch_idx, optimizer_idx=0):
    method validation_step (line 265) | def validation_step(self, batch, batch_idx, optimizer_idx=0):
    method sample (line 277) | def sample(self, batch, output_type='trimesh', **kwargs):

FILE: ultrashape/models/diffusion/transport/__init__.py
  function create_transport (line 29) | def create_transport(

FILE: ultrashape/models/diffusion/transport/integrators.py
  class sde (line 33) | class sde:
    method __init__ (line 35) | def __init__(
    method __Euler_Maruyama_step (line 54) | def __Euler_Maruyama_step(self, x, mean_x, t, model, **model_kwargs):
    method __Heun_step (line 64) | def __Heun_step(self, x, _, t, model, **model_kwargs):
    method __forward_fn (line 75) | def __forward_fn(self):
    method sample (line 89) | def sample(self, init, model, **model_kwargs):
  class ode (line 102) | class ode:
    method __init__ (line 104) | def __init__(
    method sample (line 123) | def sample(self, x, model, **model_kwargs):

FILE: ultrashape/models/diffusion/transport/path.py
  function expand_t_like_x (line 30) | def expand_t_like_x(t, x):
  class ICPlan (line 43) | class ICPlan:
    method __init__ (line 45) | def __init__(self, sigma=0.0):
    method compute_alpha_t (line 48) | def compute_alpha_t(self, t):
    method compute_sigma_t (line 52) | def compute_sigma_t(self, t):
    method compute_d_alpha_alpha_ratio_t (line 56) | def compute_d_alpha_alpha_ratio_t(self, t):
    method compute_drift (line 60) | def compute_drift(self, x, t):
    method compute_diffusion (line 70) | def compute_diffusion(self, x, t, form="constant", norm=1.0):
    method get_score_from_velocity (line 95) | def get_score_from_velocity(self, velocity, x, t):
    method get_noise_from_velocity (line 111) | def get_noise_from_velocity(self, velocity, x, t):
    method get_velocity_from_score (line 127) | def get_velocity_from_score(self, score, x, t):
    method compute_mu_t (line 139) | def compute_mu_t(self, t, x0, x1):
    method compute_xt (line 147) | def compute_xt(self, t, x0, x1):
    method compute_ut (line 152) | def compute_ut(self, t, x0, x1, xt):
    method plan (line 159) | def plan(self, t, x0, x1):
  class VPCPlan (line 165) | class VPCPlan(ICPlan):
    method __init__ (line 168) | def __init__(self, sigma_min=0.1, sigma_max=20.0):
    method compute_alpha_t (line 177) | def compute_alpha_t(self, t):
    method compute_sigma_t (line 184) | def compute_sigma_t(self, t):
    method compute_d_alpha_alpha_ratio_t (line 191) | def compute_d_alpha_alpha_ratio_t(self, t):
    method compute_drift (line 195) | def compute_drift(self, x, t):
  class GVPCPlan (line 202) | class GVPCPlan(ICPlan):
    method __init__ (line 203) | def __init__(self, sigma=0.0):
    method compute_alpha_t (line 206) | def compute_alpha_t(self, t):
    method compute_sigma_t (line 212) | def compute_sigma_t(self, t):
    method compute_d_alpha_alpha_ratio_t (line 218) | def compute_d_alpha_alpha_ratio_t(self, t):

FILE: ultrashape/models/diffusion/transport/transport.py
  class ModelType (line 37) | class ModelType(enum.Enum):
  class PathType (line 47) | class PathType(enum.Enum):
  class WeightType (line 57) | class WeightType(enum.Enum):
  class Transport (line 67) | class Transport:
    method __init__ (line 69) | def __init__(
    method prior_logp (line 98) | def prior_logp(self, z):
    method check_interval (line 108) | def check_interval(
    method sample (line 138) | def sample(self, x1):
    method training_losses (line 158) | def training_losses(
    method get_drift (line 202) | def get_drift(
    method get_score (line 237) | def get_score(
  class Sampler (line 256) | class Sampler:
    method __init__ (line 259) | def __init__(
    method __get_sde_diffusion_and_drift (line 272) | def __get_sde_diffusion_and_drift(
    method __get_last_step (line 291) | def __get_last_step(
    method sample_sde (line 324) | def sample_sde(
    method sample_ode (line 385) | def sample_ode(
    method sample_ode_intermediate (line 430) | def sample_ode_intermediate(
    method sample_ode_likelihood (line 476) | def sample_ode_likelihood(

FILE: ultrashape/models/diffusion/transport/utils.py
  class EasyDict (line 28) | class EasyDict:
    method __init__ (line 30) | def __init__(self, sub_dict):
    method __getitem__ (line 34) | def __getitem__(self, key):
  function mean_flat (line 37) | def mean_flat(x):
  function log_state (line 43) | def log_state(state):

FILE: ultrashape/pipelines.py
  function retrieve_timesteps (line 42) | def retrieve_timesteps(
  function export_to_trimesh (line 102) | def export_to_trimesh(mesh_output):
  function get_obj_from_str (line 119) | def get_obj_from_str(string, reload=False):
  function instantiate_from_config (line 127) | def instantiate_from_config(config, **kwargs):
  class DiTPipeline (line 137) | class DiTPipeline:
    method from_single_file (line 143) | def from_single_file(
    method from_pretrained (line 203) | def from_pretrained(
    method __init__ (line 236) | def __init__(
    method compile (line 268) | def compile(self):
    method enable_flashvdm (line 273) | def enable_flashvdm(
    method to (line 291) | def to(self, device=None, dtype=None):
    method _execution_device (line 304) | def _execution_device(self):
    method enable_model_cpu_offload (line 325) | def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, devic...
    method maybe_free_model_hooks (line 399) | def maybe_free_model_hooks(self):
    method encode_cond (line 419) | def encode_cond(self, image, additional_cond_inputs, do_classifier_fre...
    method prepare_extra_step_kwargs (line 453) | def prepare_extra_step_kwargs(self, generator, eta):
    method prepare_latents (line 470) | def prepare_latents(self, batch_size, dtype, device, generator, latent...
    method prepare_image (line 490) | def prepare_image(self, image, mask=None) -> dict:
    method get_guidance_scale_embedding (line 519) | def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=tor...
    method set_surface_extractor (line 547) | def set_surface_extractor(self, mc_algo):
    method __call__ (line 559) | def __call__(
    method _export (line 659) | def _export(
  class UltraShapePipeline (line 691) | class UltraShapePipeline(DiTPipeline):
    method __call__ (line 694) | def __call__(

FILE: ultrashape/postprocessors.py
  function load_mesh (line 35) | def load_mesh(path):
  function reduce_face (line 44) | def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000):
  function remove_floater (line 61) | def remove_floater(mesh: pymeshlab.MeshSet):
  function pymeshlab2trimesh (line 69) | def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
  function trimesh2pymeshlab (line 83) | def trimesh2pymeshlab(mesh: trimesh.Trimesh):
  function export_mesh (line 98) | def export_mesh(input, output):
  function import_mesh (line 111) | def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2M...
  class FaceReducer (line 125) | class FaceReducer:
    method __call__ (line 127) | def __call__(
  class FloaterRemover (line 138) | class FloaterRemover:
    method __call__ (line 140) | def __call__(
  class DegenerateFaceRemover (line 150) | class DegenerateFaceRemover:
    method __call__ (line 152) | def __call__(
  function mesh_normalize (line 167) | def mesh_normalize(mesh):
  class MeshSimplifier (line 186) | class MeshSimplifier:
    method __init__ (line 187) | def __init__(self, executable: str = None):
    method __call__ (line 194) | def __call__(

FILE: ultrashape/preprocessors.py
  function array_to_tensor (line 22) | def array_to_tensor(np_array):
  class ImageProcessorV2 (line 30) | class ImageProcessorV2:
    method __init__ (line 31) | def __init__(self, size=512, border_ratio=None):
    method recenter (line 36) | def recenter(image, border_ratio: float = 0.2):
    method load_image (line 90) | def load_image(self, image, border_ratio=0.15, to_tensor=True):
    method __call__ (line 109) | def __call__(self, image, border_ratio=0.15, to_tensor=True, **kwargs):
  class MVImageProcessorV2 (line 120) | class MVImageProcessorV2(ImageProcessorV2):
    method __init__ (line 126) | def __init__(self, size=512, border_ratio=None):
    method __call__ (line 135) | def __call__(self, image_dict, border_ratio=0.15, to_tensor=True, **kw...

FILE: ultrashape/rembg.py
  class BackgroundRemover (line 26) | class BackgroundRemover():
    method __init__ (line 27) | def __init__(self):
    method __call__ (line 30) | def __call__(self, image: Image.Image):

FILE: ultrashape/schedulers.py
  class FlowMatchEulerDiscreteSchedulerOutput (line 43) | class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
  class FlowMatchEulerDiscreteScheduler (line 56) | class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
    method __init__ (line 79) | def __init__(
    method step_index (line 103) | def step_index(self):
    method begin_index (line 110) | def begin_index(self):
    method set_begin_index (line 117) | def set_begin_index(self, begin_index: int = 0):
    method scale_noise (line 127) | def scale_noise(
    method _sigma_to_t (line 175) | def _sigma_to_t(self, sigma):
    method time_shift (line 178) | def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
    method set_timesteps (line 181) | def set_timesteps(
    method index_for_timestep (line 223) | def index_for_timestep(self, timestep, schedule_timesteps=None):
    method _init_step_index (line 237) | def _init_step_index(self, timestep):
    method step (line 245) | def step(
    method __len__ (line 320) | def __len__(self):
  class ConsistencyFlowMatchEulerDiscreteSchedulerOutput (line 325) | class ConsistencyFlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
  class ConsistencyFlowMatchEulerDiscreteScheduler (line 330) | class ConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigM...
    method __init__ (line 335) | def __init__(
    method step_index (line 355) | def step_index(self):
    method begin_index (line 362) | def begin_index(self):
    method set_begin_index (line 369) | def set_begin_index(self, begin_index: int = 0):
    method _sigma_to_t (line 379) | def _sigma_to_t(self, sigma):
    method set_timesteps (line 382) | def set_timesteps(
    method index_for_timestep (line 414) | def index_for_timestep(self, timestep, schedule_timesteps=None):
    method _init_step_index (line 428) | def _init_step_index(self, timestep):
    method step (line 436) | def step(
    method __len__ (line 479) | def __len__(self):

FILE: ultrashape/surface_loaders.py
  function normalize_mesh (line 20) | def normalize_mesh(mesh, scale=0.9999):
  function sample_pointcloud (line 44) | def sample_pointcloud(mesh, num=200000):
  function load_surface (line 64) | def load_surface(mesh, num_points=8192):
  function sharp_sample_pointcloud (line 92) | def sharp_sample_pointcloud(mesh, num=16384):
  function load_surface_sharpegde (line 141) | def load_surface_sharpegde(mesh, num_points=4096, num_sharp_points=4096,...
  class SurfaceLoader (line 186) | class SurfaceLoader:
    method __init__ (line 187) | def __init__(self, num_points=8192):
    method __call__ (line 190) | def __call__(self, mesh_or_mesh_path, num_points=None):
  class SharpEdgeSurfaceLoader (line 208) | class SharpEdgeSurfaceLoader:
    method __init__ (line 209) | def __init__(self, num_uniform_points=8192, num_sharp_points=8192, **k...
    method __call__ (line 214) | def __call__(self, mesh_or_mesh_path, num_uniform_points=None,

FILE: ultrashape/utils/ema.py
  class LitEma (line 5) | class LitEma(nn.Module):
    method __init__ (line 6) | def __init__(self, model, decay=0.9999, use_num_updates=True):
    method forward (line 25) | def forward(self, model):
    method copy_to (line 46) | def copy_to(self, model):
    method store (line 55) | def store(self, model):
    method restore (line 64) | def restore(self, model):

FILE: ultrashape/utils/misc.py
  function get_config_from_file (line 13) | def get_config_from_file(config_file: str) -> Union[DictConfig, ListConf...
  function get_obj_from_str (line 32) | def get_obj_from_str(string, reload=False):
  function get_obj_from_config (line 40) | def get_obj_from_config(config):
  function instantiate_from_config (line 47) | def instantiate_from_config(config, **kwargs):
  function instantiate_vae_from_config (line 68) | def instantiate_vae_from_config(config, **kwargs):
  function instantiate_vae_from_config_local (line 87) | def instantiate_vae_from_config_local(config, **kwargs):
  function disabled_train (line 123) | def disabled_train(self, mode=True):
  function instantiate_non_trainable_model (line 129) | def instantiate_non_trainable_model(config):
  function instantiate_vae_model (line 139) | def instantiate_vae_model(config, requires_grad=False):
  function instantiate_vae_model_local (line 148) | def instantiate_vae_model_local(config, requires_grad=False):
  function is_dist_avail_and_initialized (line 157) | def is_dist_avail_and_initialized():
  function get_rank (line 165) | def get_rank():
  function get_world_size (line 171) | def get_world_size():
  function all_gather_batch (line 177) | def all_gather_batch(tensors):

FILE: ultrashape/utils/trainings/callback.py
  function node_zero_only (line 27) | def node_zero_only(fn: Callable) -> Callable:
  function node_zero_experiment (line 37) | def node_zero_experiment(fn: Callable) -> Callable:
  class MyWandbLogger (line 48) | class MyWandbLogger(WandbLogger):
    method experiment (line 51) | def experiment(self):
  class SetupCallback (line 54) | class SetupCallback(Callback):
    method __init__ (line 55) | def __init__(self, config: DictConfig, exp_config: DictConfig,
    method on_fit_start (line 75) | def on_fit_start(self, trainer: pl.trainer.Trainer, pl_module: pl.Ligh...
  class ImageLogger (line 88) | class ImageLogger(Callback):
    method __init__ (line 89) | def __init__(self, batch_frequency: int, max_images: int, clamp: bool ...
    method _wandb (line 105) | def _wandb(self, pl_module, images, batch_idx, split):
    method _testtube (line 114) | def _testtube(self, pl_module, images, batch_idx, split):
    method log_local (line 125) | def log_local(self, save_dir: str, split: str, images: Dict,
    method log_img (line 144) | def log_img(self, pl_module: pl.LightningModule, batch: Tuple[torch.Lo...
    method check_frequency (line 174) | def check_frequency(self, batch_idx: int) -> bool:
    method on_train_batch_end (line 183) | def on_train_batch_end(self, trainer: pl.trainer.Trainer, pl_module: p...
    method on_validation_batch_end (line 187) | def on_validation_batch_end(self, trainer: pl.trainer.Trainer, pl_modu...
  class CUDACallback (line 193) | class CUDACallback(Callback):
    method on_train_epoch_start (line 195) | def on_train_epoch_start(self, trainer, pl_module):
    method on_train_epoch_end (line 201) | def on_train_epoch_end(self, trainer, pl_module, outputs):

FILE: ultrashape/utils/trainings/lr_scheduler.py
  class BaseScheduler (line 18) | class BaseScheduler(object):
    method schedule (line 20) | def schedule(self, n, **kwargs):
  class LambdaWarmUpCosineFactorScheduler (line 24) | class LambdaWarmUpCosineFactorScheduler(BaseScheduler):
    method __init__ (line 28) | def __init__(self, warm_up_steps, f_min, f_max, f_start, max_decay_ste...
    method schedule (line 37) | def schedule(self, n, **kwargs):
    method __call__ (line 52) | def __call__(self, n, **kwargs):

FILE: ultrashape/utils/trainings/mesh.py
  function save_obj (line 26) | def save_obj(pointnp_px3, facenp_fx3, fname):
  function savemeshtes2 (line 41) | def savemeshtes2(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, tex_...
  class MeshOutput (line 81) | class MeshOutput(object):
    method __init__ (line 83) | def __init__(self,
    method contain_uv_texture (line 98) | def contain_uv_texture(self):
    method contain_vertex_colors (line 101) | def contain_vertex_colors(self):
    method export (line 104) | def export(self, fname):

FILE: ultrashape/utils/trainings/mesh_log_callback.py
  class ImageConditionalASLDiffuserLogger (line 37) | class ImageConditionalASLDiffuserLogger(Callback):
    method __init__ (line 38) | def __init__(self,
    method _wandb (line 68) | def _wandb(self, pl_module, images, batch_idx, split):
    method log_local (line 76) | def log_local(self,
    method log_sample (line 144) | def log_sample(self,
    method make_grid (line 211) | def make_grid(self, images):  # return (3,h,w) in (0,1) ...
    method check_frequency (line 224) | def check_frequency(self, step: int) -> bool:
    method on_train_batch_end (line 229) | def on_train_batch_end(self, trainer: pl.trainer.Trainer, pl_module: p...
    method on_validation_batch_end (line 239) | def on_validation_batch_end(self, trainer: pl.trainer.Trainer, pl_modu...
    method denormalize_image (line 247) | def denormalize_image(self, image):
  class ImageConditionalFixASLDiffuserLogger (line 270) | class ImageConditionalFixASLDiffuserLogger(Callback):
    method __init__ (line 271) | def __init__(
    method on_train_batch_end (line 293) | def on_train_batch_end(

FILE: ultrashape/utils/trainings/peft.py
  class PeftSaveCallback (line 21) | class PeftSaveCallback(Callback):
    method __init__ (line 22) | def __init__(self, peft_model, save_dir: str, save_every_n_steps: int ...
    method recursive_convert (line 29) | def recursive_convert(self, obj):
    method _convert_peft_config (line 61) | def _convert_peft_config(self):
    method on_train_epoch_end (line 65) | def on_train_epoch_end(self, trainer, pl_module):
    method on_train_batch_end (line 71) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...

FILE: ultrashape/utils/utils.py
  function get_logger (line 22) | def get_logger(name):
  class synchronize_timer (line 38) | class synchronize_timer:
    method __init__ (line 57) | def __init__(self, name=None):
    method __enter__ (line 60) | def __enter__(self):
    method __exit__ (line 68) | def __exit__(self, exc_type, exc_value, exc_tb):
    method __call__ (line 77) | def __call__(self, func):
  function smart_load_model (line 89) | def smart_load_model(

FILE: ultrashape/utils/visualizers/color_util.py
  function get_colors (line 20) | def get_colors(inp, colormap="viridis", normalize=True, vmin=None, vmax=...
  function gen_checkers (line 30) | def gen_checkers(n_checkers_x, n_checkers_y, width=256, height=256):
  function gen_circle (line 48) | def gen_circle(width=256, height=256):

FILE: ultrashape/utils/visualizers/html_util.py
  function to_html_frame (line 23) | def to_html_frame(content):
  function to_single_row_table (line 36) | def to_single_row_table(caption: str, content: str):
  function to_image_embed_tag (line 50) | def to_image_embed_tag(image: np.ndarray):

FILE: ultrashape/utils/visualizers/pythreejs_viewer.py
  class PyThreeJSViewer (line 27) | class PyThreeJSViewer(object):
    method __init__ (line 29) | def __init__(self, settings, render_mode="WEBSITE"):
    method jupyter_mode (line 45) | def jupyter_mode(self):
    method offline (line 48) | def offline(self):
    method website (line 51) | def website(self):
    method __get_shading (line 54) | def __get_shading(self, shading):
    method __update_settings (line 66) | def __update_settings(self, settings={}):
    method __add_object (line 73) | def __add_object(self, obj, parent=None):
    method __add_line_geometry (line 88) | def __add_line_geometry(self, lines, shading, obj=None):
    method __update_view (line 105) | def __update_view(self):
    method __get_bbox (line 126) | def __get_bbox(self, v):
    method __get_colors (line 138) | def __get_colors(self, v, f, c, sh):
    method __get_point_colors (line 186) | def __get_point_colors(self, v, c, sh):
    method add_mesh (line 222) | def add_mesh(self, v, f, c=None, uv=None, n=None, shading={}, texture_...
    method add_lines (line 322) | def add_lines(self, beginning, ending, shading={}, obj=None, **kwargs):
    method add_edges (line 344) | def add_edges(self, vertices, edges, shading={}, obj=None, **kwargs):
    method add_points (line 358) | def add_points(self, points, c=None, shading={}, obj=None, **kwargs):
    method remove_object (line 402) | def remove_object(self, obj_id):
    method reset (line 410) | def reset(self):
    method update_object (line 416) | def update_object(self, oid=0, vertices=None, colors=None, faces=None):
    method add_text (line 462) | def add_text(self, text, shading={}, **kwargs):
    method to_html (line 486) | def to_html(self, imports=True, html_frame=True):
    method save (line 541) | def save(self, filename=""):

FILE: ultrashape/utils/voxelize.py
  function voxelize_from_point (line 3) | def voxelize_from_point(pc, num_latents, resolution=128):