SYMBOL INDEX (1033 symbols across 70 files)

FILE: blender_utils/render_proxy.py
  function load_object (line 23) | def load_object(object_path: str) -> None:
  function az_el_to_points (line 34) | def az_el_to_points(azimuths, elevations):
  function points_to_az_el_dist (line 40) | def points_to_az_el_dist(location):
  function set_camera (line 48) | def set_camera(camera, az, el, dist):
  function get_camera (line 60) | def get_camera(camera_name):
  function init_global (line 90) | def init_global(context):
  function reset_scene (line 126) | def reset_scene() -> None:
  function scene_root_objects (line 142) | def scene_root_objects():
  function scene_meshes (line 147) | def scene_meshes():
  function selected_root_objects (line 152) | def selected_root_objects():
  function selected_meshes (line 157) | def selected_meshes():
  function selected_objects_bbox (line 162) | def selected_objects_bbox(single_obj=None, ignore_matrix=False):
  function scene_bbox (line 178) | def scene_bbox(single_obj=None, ignore_matrix=False):
  function normalize_scene (line 194) | def normalize_scene():

FILE: externs/pvcnn/modules/ball_query.py
  class BallQuery (line 9) | class BallQuery(nn.Module):
    method __init__ (line 10) | def __init__(self, radius, num_neighbors, include_coordinates=True):
    method forward (line 16) | def forward(self, points_coords, centers_coords, points_features=None):
    method extra_repr (line 32) | def extra_repr(self):

FILE: externs/pvcnn/modules/frustum.py
  class FrustumPointNetLoss (line 11) | class FrustumPointNetLoss(nn.Module):
    method __init__ (line 12) | def __init__(self, num_heading_angle_bins, num_size_templates, size_te...
    method forward (line 27) | def forward(self, inputs, targets):
  function get_box_corners_3d (line 92) | def get_box_corners_3d(centers, headings, sizes, with_flip=False):

FILE: externs/pvcnn/modules/functional/ball_query.py
  function ball_query (line 8) | def ball_query(centers_coords, points_coords, radius, num_neighbors):

FILE: externs/pvcnn/modules/functional/devoxelization.py
  class TrilinearDevoxelization (line 8) | class TrilinearDevoxelization(Function):
    method forward (line 10) | def forward(ctx, features, coords, resolution, is_training=True):
    method backward (line 30) | def backward(ctx, grad_output):

FILE: externs/pvcnn/modules/functional/grouping.py
  class Grouping (line 8) | class Grouping(Function):
    method forward (line 10) | def forward(ctx, features, indices):
    method backward (line 25) | def backward(ctx, grad_output):

FILE: externs/pvcnn/modules/functional/interpolatation.py
  class NeighborInterpolation (line 8) | class NeighborInterpolation(Function):
    method forward (line 10) | def forward(ctx, points_coords, centers_coords, centers_features):
    method backward (line 30) | def backward(ctx, grad_output):

FILE: externs/pvcnn/modules/functional/loss.py
  function kl_loss (line 7) | def kl_loss(x, y):
  function huber_loss (line 13) | def huber_loss(error, delta):

FILE: externs/pvcnn/modules/functional/sampling.py
  class Gather (line 10) | class Gather(Function):
    method forward (line 12) | def forward(ctx, features, indices):
    method backward (line 28) | def backward(ctx, grad_output):
  function furthest_point_sample (line 37) | def furthest_point_sample(coords, num_samples):
  function logits_mask (line 51) | def logits_mask(coords, logits, num_points_per_object):

FILE: externs/pvcnn/modules/functional/src/ball_query/ball_query.cpp
  function ball_query_forward (line 6) | at::Tensor ball_query_forward(at::Tensor centers_coords,

FILE: externs/pvcnn/modules/functional/src/bindings.cpp
  function PYBIND11_MODULE (line 10) | PYBIND11_MODULE(_pvcnn_backend, m) {

FILE: externs/pvcnn/modules/functional/src/grouping/grouping.cpp
  function grouping_forward (line 6) | at::Tensor grouping_forward(at::Tensor features, at::Tensor indices) {
  function grouping_backward (line 26) | at::Tensor grouping_backward(at::Tensor grad_y, at::Tensor indices,

FILE: externs/pvcnn/modules/functional/src/interpolate/neighbor_interpolate.cpp
  function three_nearest_neighbors_interpolate_forward (line 6) | std::vector<at::Tensor>
  function three_nearest_neighbors_interpolate_backward (line 42) | at::Tensor three_nearest_neighbors_interpolate_backward(at::Tensor grad_y,

FILE: externs/pvcnn/modules/functional/src/interpolate/trilinear_devox.cpp
  function trilinear_devoxelize_forward (line 18) | std::vector<at::Tensor>
  function trilinear_devoxelize_backward (line 67) | at::Tensor trilinear_devoxelize_backward(const at::Tensor grad_y,

FILE: externs/pvcnn/modules/functional/src/sampling/sampling.cpp
  function gather_features_forward (line 6) | at::Tensor gather_features_forward(at::Tensor features, at::Tensor indic...
  function gather_features_backward (line 25) | at::Tensor gather_features_backward(at::Tensor grad_y, at::Tensor indices,
  function furthest_point_sampling_forward (line 43) | at::Tensor furthest_point_sampling_forward(at::Tensor coords,

FILE: externs/pvcnn/modules/functional/src/voxelization/vox.cpp
  function avg_voxelize_forward (line 17) | std::vector<at::Tensor> avg_voxelize_forward(const at::Tensor features,
  function avg_voxelize_backward (line 54) | at::Tensor avg_voxelize_backward(const at::Tensor grad_y,

FILE: externs/pvcnn/modules/functional/voxelization.py
  class AvgVoxelization (line 8) | class AvgVoxelization(Function):
    method forward (line 10) | def forward(ctx, features, coords, resolution):
    method backward (line 27) | def backward(ctx, grad_output):

FILE: externs/pvcnn/modules/loss.py
  class KLLoss (line 8) | class KLLoss(nn.Module):
    method forward (line 9) | def forward(self, x, y):

FILE: externs/pvcnn/modules/pointnet.py
  class PointNetAModule (line 11) | class PointNetAModule(nn.Module):
    method __init__ (line 12) | def __init__(self, in_channels, out_channels, include_coordinates=True):
    method forward (line 32) | def forward(self, inputs):
    method extra_repr (line 45) | def extra_repr(self):
  class PointNetSAModule (line 49) | class PointNetSAModule(nn.Module):
    method __init__ (line 50) | def __init__(self, num_centers, radius, num_neighbors, in_channels, ou...
    method forward (line 80) | def forward(self, inputs):
    method extra_repr (line 91) | def extra_repr(self):
  class PointNetFPModule (line 95) | class PointNetFPModule(nn.Module):
    method __init__ (line 96) | def __init__(self, in_channels, out_channels):
    method forward (line 100) | def forward(self, inputs):

FILE: externs/pvcnn/modules/pvconv.py
  class PVConv (line 11) | class PVConv(nn.Module):
    method __init__ (line 12) | def __init__(self, in_channels, out_channels, kernel_size, resolution,...
    method forward (line 33) | def forward(self, inputs):
  class ProxyVoxelConv (line 41) | class ProxyVoxelConv(nn.Module):
    method __init__ (line 42) | def __init__(self, in_channels, out_channels, kernel_size, resolution,...
    method forward (line 51) | def forward(self, inputs):

FILE: externs/pvcnn/modules/se.py
  class SE3d (line 6) | class SE3d(nn.Module):
    method __init__ (line 7) | def __init__(self, channel, reduction=8):
    method forward (line 16) | def forward(self, inputs):

FILE: externs/pvcnn/modules/shared_mlp.py
  class SharedMLP (line 6) | class SharedMLP(nn.Module):
    method __init__ (line 7) | def __init__(self, in_channels, out_channels, dim=1):
    method forward (line 29) | def forward(self, inputs):

FILE: externs/pvcnn/modules/voxelization.py
  class Voxelization (line 9) | class Voxelization(nn.Module):
    method __init__ (line 10) | def __init__(self, resolution, normalize=True, eps=0):
    method forward (line 16) | def forward(self, features, coords):
    method extra_repr (line 28) | def extra_repr(self):

FILE: foreground_segment.py
  class BackgroundRemoval (line 9) | class BackgroundRemoval:
    method __init__ (line 10) | def __init__(self, device='cuda'):
    method __call__ (line 26) | def __call__(self, image):
  function process (line 33) | def process(image_path, mask_path):

FILE: generate.py
  function load_model (line 18) | def load_model(cfg,ckpt,strict=True):
  function main (line 27) | def main():

FILE: ldm/DPMPPScheduler.py
  class DPMPPSchedulerOutput (line 13) | class DPMPPSchedulerOutput(BaseOutput):
  class DPMPPScheduler (line 29) | class DPMPPScheduler(DPMSolverMultistepScheduler):
    method step (line 30) | def step(
    method reinit (line 105) | def reinit(self):

FILE: ldm/base_utils.py
  function save_pickle (line 7) | def save_pickle(data, pkl_path):
  function read_pickle (line 12) | def read_pickle(pkl_path):
  function draw_epipolar_line (line 16) | def draw_epipolar_line(F, img0, img1, pt0, color):
  function draw_epipolar_lines (line 29) | def draw_epipolar_lines(F, img0, img1,num=20):
  function compute_F (line 45) | def compute_F(K1, K2, Rt0, Rt1=None):
  function compute_dR_dt (line 58) | def compute_dR_dt(Rt0, Rt1):
  function concat_images (line 65) | def concat_images(img0,img1,vert=False):
  function concat_images_list (line 79) | def concat_images_list(*args,vert=False):
  function pose_inverse (line 87) | def pose_inverse(pose):
  function project_points (line 92) | def project_points(pts,RT,K):
  function draw_keypoints (line 104) | def draw_keypoints(img, kps, colors=None, radius=2):
  function output_points (line 116) | def output_points(fn,pts,colors=None):
  function mask_depth_to_pts (line 125) | def mask_depth_to_pts(mask,depth,K,rgb=None):
  function transform_points_pose (line 135) | def transform_points_pose(pts, pose):
  function pose_apply (line 141) | def pose_apply(pose,pts):
  function downsample_gaussian_blur (line 144) | def downsample_gaussian_blur(img, ratio):

FILE: ldm/data/base.py
  class Txt2ImgIterableBaseDataset (line 7) | class Txt2ImgIterableBaseDataset(IterableDataset):
    method __init__ (line 11) | def __init__(self, num_records=0, valid_ids=None, size=256):
    method __len__ (line 20) | def __len__(self):
    method __iter__ (line 24) | def __iter__(self):
  class PRNGMixin (line 28) | class PRNGMixin(object):
    method prng (line 35) | def prng(self):

FILE: ldm/data/coco.py
  class CocoBase (line 11) | class CocoBase(Dataset):
    method __init__ (line 13) | def __init__(self, size=None, dataroot="", datajson="", onehot_segment...
    method year (line 95) | def year(self):
    method __len__ (line 98) | def __len__(self):
    method preprocess_image (line 101) | def preprocess_image(self, image_path, segmentation_path=None):
    method __getitem__ (line 145) | def __getitem__(self, i):
  class CocoImagesAndCaptionsTrain2017 (line 166) | class CocoImagesAndCaptionsTrain2017(CocoBase):
    method __init__ (line 168) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
    method get_split (line 175) | def get_split(self):
    method year (line 178) | def year(self):
  class CocoImagesAndCaptionsValidation2017 (line 182) | class CocoImagesAndCaptionsValidation2017(CocoBase):
    method __init__ (line 184) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
    method get_split (line 193) | def get_split(self):
    method year (line 196) | def year(self):
  class CocoImagesAndCaptionsTrain2014 (line 201) | class CocoImagesAndCaptionsTrain2014(CocoBase):
    method __init__ (line 203) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
    method get_split (line 212) | def get_split(self):
    method year (line 215) | def year(self):
  class CocoImagesAndCaptionsValidation2014 (line 218) | class CocoImagesAndCaptionsValidation2014(CocoBase):
    method __init__ (line 220) | def __init__(self, size, onehot_segmentation=False, use_stuffthing=Fal...
    method get_split (line 231) | def get_split(self):
    method year (line 234) | def year(self):

FILE: ldm/data/control_sync_dreamer.py
  class ControlSyncDreamerTrainData (line 23) | class ControlSyncDreamerTrainData(SyncDreamerTrainData):
    method __init__ (line 24) | def __init__(self, target_dir, input_dir, proxy_dir, uid_set_pkl, imag...
    method get_data_for_index (line 41) | def get_data_for_index(self, index):
  class ControlSyncDreamerEvalData (line 69) | class ControlSyncDreamerEvalData(Dataset):
    method __init__ (line 70) | def __init__(self, image_dir, proxy_dir, uid_set_pkl):
    method __len__ (line 81) | def __len__(self):
    method get_data_for_index (line 84) | def get_data_for_index(self, index):
    method __getitem__ (line 92) | def __getitem__(self, index):
  class ControlSyncDreamerDataset (line 95) | class ControlSyncDreamerDataset(SyncDreamerDataset):
    method __init__ (line 96) | def __init__(self, target_dir, input_dir, validation_dir, proxy_dir, b...
    method setup (line 112) | def setup(self, stage):

FILE: ldm/data/dummy.py
  class DummyData (line 6) | class DummyData(Dataset):
    method __init__ (line 7) | def __init__(self, length, size):
    method __len__ (line 11) | def __len__(self):
    method __getitem__ (line 14) | def __getitem__(self, i):
  class DummyDataWithEmbeddings (line 21) | class DummyDataWithEmbeddings(Dataset):
    method __init__ (line 22) | def __init__(self, length, size, emb_size):
    method __len__ (line 27) | def __len__(self):
    method __getitem__ (line 30) | def __getitem__(self, i):

FILE: ldm/data/imagenet.py
  function synset2idx (line 20) | def synset2idx(path_to_yaml="data/index_synset.yaml"):
  class ImageNetBase (line 26) | class ImageNetBase(Dataset):
    method __init__ (line 27) | def __init__(self, config=None):
    method __len__ (line 39) | def __len__(self):
    method __getitem__ (line 42) | def __getitem__(self, i):
    method _prepare (line 45) | def _prepare(self):
    method _filter_relpaths (line 48) | def _filter_relpaths(self, relpaths):
    method _prepare_synset_to_human (line 66) | def _prepare_synset_to_human(self):
    method _prepare_idx_to_synset (line 74) | def _prepare_idx_to_synset(self):
    method _prepare_human_to_integer_label (line 80) | def _prepare_human_to_integer_label(self):
    method _load (line 93) | def _load(self):
  class ImageNetTrain (line 134) | class ImageNetTrain(ImageNetBase):
    method __init__ (line 145) | def __init__(self, process_images=True, data_root=None, **kwargs):
    method _prepare (line 150) | def _prepare(self):
  class ImageNetValidation (line 197) | class ImageNetValidation(ImageNetBase):
    method __init__ (line 211) | def __init__(self, process_images=True, data_root=None, **kwargs):
    method _prepare (line 216) | def _prepare(self):
  class ImageNetSR (line 272) | class ImageNetSR(Dataset):
    method __init__ (line 273) | def __init__(self, size=None,
    method __len__ (line 336) | def __len__(self):
    method __getitem__ (line 339) | def __getitem__(self, i):
  class ImageNetSRTrain (line 375) | class ImageNetSRTrain(ImageNetSR):
    method __init__ (line 376) | def __init__(self, **kwargs):
    method get_base (line 379) | def get_base(self):
  class ImageNetSRValidation (line 386) | class ImageNetSRValidation(ImageNetSR):
    method __init__ (line 387) | def __init__(self, **kwargs):
    method get_base (line 390) | def get_base(self):

FILE: ldm/data/inpainting/synthetic_mask.py
  function gen_segment_mask (line 56) | def gen_segment_mask(mask, start, end, brush_width):
  function gen_box_mask (line 66) | def gen_box_mask(mask, masked):
  function gen_round_mask (line 72) | def gen_round_mask(mask, masked, radius):
  function gen_large_mask (line 85) | def gen_large_mask(prng, img_h, img_w,

FILE: ldm/data/laion.py
  class DataWithWings (line 24) | class DataWithWings(torch.utils.data.IterableDataset):
    method __init__ (line 25) | def __init__(self, min_size, transform=None, target_transform=None):
    method _compute_hash (line 50) | def _compute_hash(url, text):
    method _add_tags (line 58) | def _add_tags(self, x):
    method _punsafe_to_class (line 64) | def _punsafe_to_class(self, punsafe):
    method _filter_predicate (line 67) | def _filter_predicate(self, x):
    method __iter__ (line 73) | def __iter__(self):
  function dict_collation_fn (line 77) | def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
  class WebDataModuleFromConfig (line 108) | class WebDataModuleFromConfig(pl.LightningDataModule):
    method __init__ (line 109) | def __init__(self, tar_base, batch_size, train=None, validation=None,
    method make_loader (line 125) | def make_loader(self, dataset_config, train=True):
    method filter_size (line 188) | def filter_size(self, x):
    method filter_keys (line 205) | def filter_keys(self, x):
    method train_dataloader (line 211) | def train_dataloader(self):
    method val_dataloader (line 214) | def val_dataloader(self):
    method test_dataloader (line 217) | def test_dataloader(self):
  class AddLR (line 224) | class AddLR(object):
    method __init__ (line 225) | def __init__(self, factor, output_size, initial_size=None, image_key="...
    method pt2np (line 231) | def pt2np(self, x):
    method np2pt (line 235) | def np2pt(self, x):
    method __call__ (line 239) | def __call__(self, sample):
  class AddBW (line 250) | class AddBW(object):
    method __init__ (line 251) | def __init__(self, image_key="jpg"):
    method pt2np (line 254) | def pt2np(self, x):
    method np2pt (line 258) | def np2pt(self, x):
    method __call__ (line 262) | def __call__(self, sample):
  class AddMask (line 273) | class AddMask(PRNGMixin):
    method __init__ (line 274) | def __init__(self, mode="512train", p_drop=0.):
    method __call__ (line 280) | def __call__(self, sample):
  class AddEdge (line 294) | class AddEdge(PRNGMixin):
    method __init__ (line 295) | def __init__(self, mode="512train", mask_edges=True):
    method __call__ (line 304) | def __call__(self, sample):
  function example00 (line 365) | def example00():
  function example01 (line 397) | def example01():
  function example02 (line 438) | def example02():
  function example03 (line 457) | def example03():
  function example04 (line 505) | def example04():

FILE: ldm/data/lsun.py
  class LSUNBase (line 9) | class LSUNBase(Dataset):
    method __init__ (line 10) | def __init__(self,
    method __len__ (line 36) | def __len__(self):
    method __getitem__ (line 39) | def __getitem__(self, i):
  class LSUNChurchesTrain (line 62) | class LSUNChurchesTrain(LSUNBase):
    method __init__ (line 63) | def __init__(self, **kwargs):
  class LSUNChurchesValidation (line 67) | class LSUNChurchesValidation(LSUNBase):
    method __init__ (line 68) | def __init__(self, flip_p=0., **kwargs):
  class LSUNBedroomsTrain (line 73) | class LSUNBedroomsTrain(LSUNBase):
    method __init__ (line 74) | def __init__(self, **kwargs):
  class LSUNBedroomsValidation (line 78) | class LSUNBedroomsValidation(LSUNBase):
    method __init__ (line 79) | def __init__(self, flip_p=0.0, **kwargs):
  class LSUNCatsTrain (line 84) | class LSUNCatsTrain(LSUNBase):
    method __init__ (line 85) | def __init__(self, **kwargs):
  class LSUNCatsValidation (line 89) | class LSUNCatsValidation(LSUNBase):
    method __init__ (line 90) | def __init__(self, flip_p=0., **kwargs):

FILE: ldm/data/nerf_like.py
  function cartesian_to_spherical (line 11) | def cartesian_to_spherical(xyz):
  function get_T (line 21) | def get_T(T_target, T_cond):
  function get_spherical (line 32) | def get_spherical(T_target, T_cond):
  class RTMV (line 43) | class RTMV(Dataset):
    method __init__ (line 44) | def __init__(self, root_dir='datasets/RTMV/google_scanned',\
    method __len__ (line 52) | def __len__(self):
    method __getitem__ (line 55) | def __getitem__(self, idx):
    method blend_rgba (line 79) | def blend_rgba(self, img):
  class GSO (line 84) | class GSO(Dataset):
    method __init__ (line 85) | def __init__(self, root_dir='datasets/GoogleScannedObjects',\
    method __len__ (line 95) | def __len__(self):
    method __getitem__ (line 98) | def __getitem__(self, idx):
    method blend_rgba (line 123) | def blend_rgba(self, img):
  class WILD (line 127) | class WILD(Dataset):
    method __init__ (line 128) | def __init__(self, root_dir='data/nerf_wild',\
    method __len__ (line 136) | def __len__(self):
    method __getitem__ (line 139) | def __getitem__(self, idx):
    method blend_rgba (line 163) | def blend_rgba(self, img):

FILE: ldm/data/simple.py
  function make_transform_multi_folder_data (line 29) | def make_transform_multi_folder_data(paths, caption_files=None, **kwargs):
  function make_nfp_data (line 33) | def make_nfp_data(base_path):
  class VideoDataset (line 42) | class VideoDataset(Dataset):
    method __init__ (line 43) | def __init__(self, root_dir, image_transforms, caption_file, offset=8,...
    method __len__ (line 62) | def __len__(self):
    method __getitem__ (line 65) | def __getitem__(self, index):
    method _load_sample (line 73) | def _load_sample(self, index):
  function make_tranforms (line 103) | def make_tranforms(image_transforms):
  function make_multi_folder_data (line 113) | def make_multi_folder_data(paths, caption_files=None, **kwargs):
  class NfpDataset (line 136) | class NfpDataset(Dataset):
    method __init__ (line 137) | def __init__(self,
    method __len__ (line 151) | def __len__(self):
    method __getitem__ (line 155) | def __getitem__(self, index):
    method _load_im (line 164) | def _load_im(self, filename):
  class ObjaverseDataModuleFromConfig (line 168) | class ObjaverseDataModuleFromConfig(pl.LightningDataModule):
    method __init__ (line 169) | def __init__(self, root_dir, batch_size, total_view, train=None, valid...
    method train_dataloader (line 191) | def train_dataloader(self):
    method val_dataloader (line 197) | def val_dataloader(self):
    method test_dataloader (line 203) | def test_dataloader(self):
  class ObjaverseData (line 208) | class ObjaverseData(Dataset):
    method __init__ (line 209) | def __init__(self,
    method __len__ (line 245) | def __len__(self):
    method cartesian_to_spherical (line 248) | def cartesian_to_spherical(self, xyz):
    method get_T (line 257) | def get_T(self, target_RT, cond_RT):
    method load_im (line 274) | def load_im(self, path, color):
    method __getitem__ (line 287) | def __getitem__(self, index):
    method process_im (line 328) | def process_im(self, im):
  class FolderData (line 332) | class FolderData(Dataset):
    method __init__ (line 333) | def __init__(self,
    method __len__ (line 376) | def __len__(self):
    method __getitem__ (line 382) | def __getitem__(self, index):
    method process_im (line 410) | def process_im(self, im):
  class TransformDataset (line 415) | class TransformDataset():
    method __init__ (line 416) | def __init__(self, ds, extra_label="sksbspic"):
    method __getitem__ (line 426) | def __getitem__(self, index):
    method __len__ (line 444) | def __len__(self):
  function hf_dataset (line 447) | def hf_dataset(
  class TextOnly (line 473) | class TextOnly(Dataset):
    method __init__ (line 474) | def __init__(self, captions, output_size, image_key="image", caption_k...
    method __len__ (line 490) | def __len__(self):
    method __getitem__ (line 493) | def __getitem__(self, index):
    method _load_caption_file (line 498) | def _load_caption_file(self, filename):
  class IdRetreivalDataset (line 507) | class IdRetreivalDataset(FolderData):
    method __init__ (line 508) | def __init__(self, ret_file, *args, **kwargs):
    method __getitem__ (line 513) | def __getitem__(self, index):

FILE: ldm/data/sync_dreamer.py
  class SyncDreamerTrainData (line 21) | class SyncDreamerTrainData(Dataset):
    method __init__ (line 22) | def __init__(self, target_dir, input_dir, uid_set_pkl, image_size=256):
    method __len__ (line 36) | def __len__(self):
    method load_im (line 39) | def load_im(self, path):
    method process_im (line 47) | def process_im(self, im):
    method load_index (line 52) | def load_index(self, filename, index):
    method get_data_for_index (line 57) | def get_data_for_index(self, index):
    method __getitem__ (line 76) | def __getitem__(self, index):
  class SyncDreamerEvalData (line 80) | class SyncDreamerEvalData(Dataset):
    method __init__ (line 81) | def __init__(self, image_dir):
    method __len__ (line 92) | def __len__(self):
    method get_data_for_index (line 95) | def get_data_for_index(self, index):
    method __getitem__ (line 100) | def __getitem__(self, index):
  class SyncDreamerDataset (line 103) | class SyncDreamerDataset(pl.LightningDataModule):
    method __init__ (line 104) | def __init__(self, target_dir, input_dir, validation_dir, batch_size, ...
    method setup (line 116) | def setup(self, stage):
    method train_dataloader (line 123) | def train_dataloader(self):
    method val_dataloader (line 127) | def val_dataloader(self):
    method test_dataloader (line 131) | def test_dataloader(self):

FILE: ldm/lr_scheduler.py
  class LambdaWarmUpCosineScheduler (line 4) | class LambdaWarmUpCosineScheduler:
    method __init__ (line 8) | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_...
    method schedule (line 17) | def schedule(self, n, **kwargs):
    method __call__ (line 32) | def __call__(self, n, **kwargs):
  class LambdaWarmUpCosineScheduler2 (line 36) | class LambdaWarmUpCosineScheduler2:
    method __init__ (line 41) | def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths...
    method find_in_interval (line 52) | def find_in_interval(self, n):
    method schedule (line 59) | def schedule(self, n, **kwargs):
    method __call__ (line 77) | def __call__(self, n, **kwargs):
  class LambdaLinearScheduler (line 81) | class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
    method schedule (line 83) | def schedule(self, n, **kwargs):

FILE: ldm/models/autoencoder.py
  class VQModel (line 14) | class VQModel(pl.LightningModule):
    method __init__ (line 15) | def __init__(self,
    method ema_scope (line 64) | def ema_scope(self, context=None):
    method init_from_ckpt (line 78) | def init_from_ckpt(self, path, ignore_keys=list()):
    method on_train_batch_end (line 92) | def on_train_batch_end(self, *args, **kwargs):
    method encode (line 96) | def encode(self, x):
    method encode_to_prequant (line 102) | def encode_to_prequant(self, x):
    method decode (line 107) | def decode(self, quant):
    method decode_code (line 112) | def decode_code(self, code_b):
    method forward (line 117) | def forward(self, input, return_pred_indices=False):
    method get_input (line 124) | def get_input(self, batch, k):
    method training_step (line 142) | def training_step(self, batch, batch_idx, optimizer_idx):
    method validation_step (line 164) | def validation_step(self, batch, batch_idx):
    method _validation_step (line 170) | def _validation_step(self, batch, batch_idx, suffix=""):
    method configure_optimizers (line 197) | def configure_optimizers(self):
    method get_last_layer (line 230) | def get_last_layer(self):
    method log_images (line 233) | def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
    method to_rgb (line 255) | def to_rgb(self, x):
  class VQModelInterface (line 264) | class VQModelInterface(VQModel):
    method __init__ (line 265) | def __init__(self, embed_dim, *args, **kwargs):
    method encode (line 269) | def encode(self, x):
    method decode (line 274) | def decode(self, h, force_not_quantize=False):
  class AutoencoderKL (line 285) | class AutoencoderKL(pl.LightningModule):
    method __init__ (line 286) | def __init__(self,
    method init_from_ckpt (line 313) | def init_from_ckpt(self, path, ignore_keys=list()):
    method encode (line 324) | def encode(self, x):
    method decode (line 330) | def decode(self, z):
    method forward (line 335) | def forward(self, input, sample_posterior=True):
    method get_input (line 344) | def get_input(self, batch, k):
    method training_step (line 351) | def training_step(self, batch, batch_idx, optimizer_idx):
    method validation_step (line 372) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 386) | def configure_optimizers(self):
    method get_last_layer (line 397) | def get_last_layer(self):
    method log_images (line 401) | def log_images(self, batch, only_inputs=False, **kwargs):
    method to_rgb (line 417) | def to_rgb(self, x):
  class IdentityFirstStage (line 426) | class IdentityFirstStage(torch.nn.Module):
    method __init__ (line 427) | def __init__(self, *args, vq_interface=False, **kwargs):
    method encode (line 431) | def encode(self, x, *args, **kwargs):
    method decode (line 434) | def decode(self, x, *args, **kwargs):
    method quantize (line 437) | def quantize(self, x, *args, **kwargs):
    method forward (line 442) | def forward(self, x, *args, **kwargs):

FILE: ldm/models/diffusion/ctrldemo_sync_dreamer.py
  class ControlSpatialVolumeNet (line 24) | class ControlSpatialVolumeNet(SpatialVolumeNet):
    method __init__ (line 25) | def __init__(self, time_dim, view_dim, view_num,
    method construct_spatial_volume (line 41) | def construct_spatial_volume(self, x, t_embed, v_embed, target_poses, ...
  class CtrlDemo (line 100) | class CtrlDemo(SyncMultiviewDiffusion):
    method __init__ (line 101) | def __init__(self, unet_config, scheduler_config,
    method _init_sampler (line 136) | def _init_sampler(self, latent_size, sample_steps):
    method prepare (line 139) | def prepare(self, batch):
    method inference (line 145) | def inference(self, sampler, batch, cfg_scale, batch_view_num, return_...
    method decode_latents (line 152) | def decode_latents(self, x_sample):
    method get_target_view_feats (line 156) | def get_target_view_feats(self, x_input, spatial_volume, clip_embed, t...
    method training_step (line 182) | def training_step(self, batch):
    method configure_optimizers (line 213) | def configure_optimizers(self):
  class CtrlDemoSampler (line 226) | class CtrlDemoSampler:
    method __init__ (line 227) | def __init__(self, model: CtrlDemo, scheduler_steps, scheduler_name='d...
    method parameterization (line 243) | def parameterization(self):
    method from_pkl (line 254) | def from_pkl(cls, model, pkl_dir):
    method set_ctrl3D_params (line 258) | def set_ctrl3D_params(self, ctrl3D_params_list, strength: float=1.0):
    method concat_proxy (line 262) | def concat_proxy(self, proxys, inferenc_step):
    method denoise_apply_impl (line 276) | def denoise_apply_impl(self, x_target_noisy, time_steps, noise_pred, i...
    method denoise_apply (line 285) | def denoise_apply(self, x_target_noisy, input_info, v_embed, clip_embe...
    method inference (line 328) | def inference(self, input_info, clip_embed, unconditional_scale=1.0, l...
    method get_clip_feature (line 372) | def get_clip_feature(self, x_input, clip_embed, v_embed, target_index):

FILE: ldm/models/diffusion/sync_dreamer.py
  function disabled_train (line 20) | def disabled_train(self, mode=True):
  function disable_training_module (line 25) | def disable_training_module(module: nn.Module):
  function repeat_to_batch (line 32) | def repeat_to_batch(tensor, B, VN):
  class UNetWrapper (line 38) | class UNetWrapper(nn.Module):
    method __init__ (line 39) | def __init__(self, diff_model_config, drop_conditions=False, drop_sche...
    method drop (line 47) | def drop(self, cond, mask):
    method get_trainable_parameters (line 53) | def get_trainable_parameters(self):
    method get_drop_scheme (line 56) | def get_drop_scheme(self, B, device):
    method forward (line 67) | def forward(self, x, t, clip_embed, volume_feats, x_concat, is_train=F...
    method predict_with_unconditional_scale (line 104) | def predict_with_unconditional_scale(self, x, t, clip_embed, volume_fe...
    method predict_for_threestudio (line 123) | def predict_for_threestudio(self, x, t, clip_embed, volume_feats, x_co...
    method predict_with_unconditional_scale_mv (line 138) | def predict_with_unconditional_scale_mv(self, x, t, clip_embed, volume...
  class SpatialVolumeNet (line 146) | class SpatialVolumeNet(nn.Module):
    method __init__ (line 147) | def __init__(self, time_dim, view_dim, view_num,
    method construct_spatial_volume (line 168) | def construct_spatial_volume(self, x, t_embed, v_embed, target_poses, ...
    method construct_view_frustum_volume (line 214) | def construct_view_frustum_volume(self, spatial_volume, t_embed, v_emb...
  class SyncMultiviewDiffusion (line 247) | class SyncMultiviewDiffusion(pl.LightningModule):
    method __init__ (line 248) | def __init__(self, unet_config, scheduler_config,
    method _init_clip_projection (line 287) | def _init_clip_projection(self):
    method _init_multiview (line 296) | def _init_multiview(self):
    method get_viewpoint_embedding (line 309) | def get_viewpoint_embedding(self, batch_size, elevation_ref):
    method _init_first_stage (line 329) | def _init_first_stage(self):
    method _init_clip_image_encoder (line 354) | def _init_clip_image_encoder(self):
    method _init_schedule (line 358) | def _init_schedule(self):
    method _init_time_step_embedding (line 382) | def _init_time_step_embedding(self):
    method encode_first_stage (line 390) | def encode_first_stage(self, x, sample=True):
    method decode_first_stage (line 398) | def decode_first_stage(self, z):
    method prepare (line 403) | def prepare(self, batch):
    method embed_time (line 421) | def embed_time(self, t):
    method get_target_view_feats (line 426) | def get_target_view_feats(self, x_input, spatial_volume, clip_embed, t...
    method training_step (line 451) | def training_step(self, batch):
    method add_noise (line 482) | def add_noise(self, x_start, t):
    method sample (line 498) | def sample(self, sampler, batch, cfg_scale, batch_view_num, return_int...
    method decode_latents (line 520) | def decode_latents(self, x_sample):
    method inference (line 524) | def inference(self, sampler, batch, cfg_scale, batch_view_num, return_...
    method log_image (line 530) | def log_image(self,  x_sample, batch, step, output_dir):
    method validation_step (line 543) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 554) | def configure_optimizers(self):
  class SyncDDIMSampler (line 575) | class SyncDDIMSampler:
    method __init__ (line 576) | def __init__(self, model: SyncMultiviewDiffusion, ddim_num_steps, ddim...
    method _make_schedule (line 584) | def _make_schedule(self,  ddim_num_steps, ddim_discretize="uniform", d...
    method denoise_apply_impl (line 602) | def denoise_apply_impl(self, x_target_noisy, index, noise_pred, is_ste...
    method denoise_apply (line 628) | def denoise_apply(self, x_target_noisy, input_info, clip_embed, time_s...
    method sample (line 669) | def sample(self, input_info, clip_embed, unconditional_scale=1.0, log_...

FILE: ldm/models/diffusion/sync_dreamer_attention.py
  class DepthAttention (line 8) | class DepthAttention(nn.Module):
    method __init__ (line 9) | def __init__(self, query_dim, context_dim, heads, dim_head, output_bia...
    method forward (line 26) | def forward(self, x, context):
  class DepthTransformer (line 50) | class DepthTransformer(nn.Module):
    method __init__ (line 51) | def __init__(self, dim, n_heads, d_head, context_dim=None, checkpoint=...
    method forward (line 75) | def forward(self, x, context=None):
    method _forward (line 78) | def _forward(self, x, context):
  class DepthWiseAttention (line 87) | class DepthWiseAttention(UNetModel):
    method __init__ (line 88) | def __init__(self, volume_dims=(5,16,32,64), *args, **kwargs):
    method forward (line 117) | def forward(self, x, timesteps=None, context=None, source_dict=None, *...
    method get_trainable_parameters (line 140) | def get_trainable_parameters(self):

FILE: ldm/models/diffusion/sync_dreamer_network.py
  class Image2DResBlockWithTV (line 6) | class Image2DResBlockWithTV(nn.Module):
    method __init__ (line 7) | def __init__(self, dim, tdim, vdim):
    method forward (line 21) | def forward(self, x, t, v):
  class NoisyTargetViewEncoder (line 25) | class NoisyTargetViewEncoder(nn.Module):
    method __init__ (line 26) | def __init__(self, time_embed_dim, viewpoint_dim, run_dim=16, output_d...
    method forward (line 39) | def forward(self, x, t, v):
  class SpatialUpTimeBlock (line 52) | class SpatialUpTimeBlock(nn.Module):
    method __init__ (line 53) | def __init__(self, x_in_dim, t_in_dim, out_dim):
    method forward (line 61) | def forward(self, x, t):
  class SpatialTimeBlock (line 65) | class SpatialTimeBlock(nn.Module):
    method __init__ (line 66) | def __init__(self, x_in_dim, t_in_dim, out_dim, stride):
    method forward (line 74) | def forward(self, x, t):
  class SpatialTime3DNet (line 78) | class SpatialTime3DNet(nn.Module):
    method __init__ (line 79) | def __init__(self, time_dim=256, input_dim=128, dims=(32, 64, 128, 256)):
    method forward (line 103) | def forward(self, x, t, control=None):
  class ControlSpatialTime3DNet (line 135) | class ControlSpatialTime3DNet(nn.Module):
    method __init__ (line 136) | def __init__(self, time_dim=256, input_dim=128, proxy_input_dim=3, dim...
    method forward (line 169) | def forward(self, x, t, proxy_feature):
    method make_zero_conv (line 198) | def make_zero_conv(self, dims, channels):
  class FrustumTVBlock (line 201) | class FrustumTVBlock(nn.Module):
    method __init__ (line 202) | def __init__(self, x_dim, t_dim, v_dim, out_dim, stride):
    method forward (line 211) | def forward(self, x, t, v):
  class FrustumTVUpBlock (line 215) | class FrustumTVUpBlock(nn.Module):
    method __init__ (line 216) | def __init__(self, x_dim, t_dim, v_dim, out_dim):
    method forward (line 225) | def forward(self, x, t, v):
  class FrustumTV3DNet (line 229) | class FrustumTV3DNet(nn.Module):
    method __init__ (line 230) | def __init__(self, in_dim, t_dim, v_dim, dims=(32, 64, 128, 256)):
    method forward (line 247) | def forward(self, x, t, v):

FILE: ldm/models/diffusion/sync_dreamer_utils.py
  function project_and_normalize (line 5) | def project_and_normalize(ref_grid, src_proj, length):
  function project_ (line 22) | def project_(ref_grid, src_proj):
  function construct_project_matrix (line 37) | def construct_project_matrix(x_ratio, y_ratio, Ks, poses):
  function get_warp_coordinates (line 54) | def get_warp_coordinates(volume_xyz, warp_size, input_size, Ks, warp_pose):
  function get_proxy_warp_coordinates (line 61) | def get_proxy_warp_coordinates(proxy_xyz, warp_size, input_size, Ks, war...
  function create_target_volume (line 68) | def create_target_volume(depth_size, volume_size, input_image_size, pose...
  function near_far_from_unit_sphere_using_camera_poses (line 106) | def near_far_from_unit_sphere_using_camera_poses(camera_poses):

FILE: ldm/modules/attention.py
  function exists (line 12) | def exists(val):
  function uniq (line 16) | def uniq(arr):
  function default (line 20) | def default(val, d):
  function max_neg_value (line 26) | def max_neg_value(t):
  function init_ (line 30) | def init_(tensor):
  class GEGLU (line 38) | class GEGLU(nn.Module):
    method __init__ (line 39) | def __init__(self, dim_in, dim_out):
    method forward (line 43) | def forward(self, x):
  class ConvGEGLU (line 47) | class ConvGEGLU(nn.Module):
    method __init__ (line 48) | def __init__(self, dim_in, dim_out):
    method forward (line 52) | def forward(self, x):
  class FeedForward (line 57) | class FeedForward(nn.Module):
    method __init__ (line 58) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 73) | def forward(self, x):
  function zero_module (line 77) | def zero_module(module):
  function Normalize (line 86) | def Normalize(in_channels):
  class LinearAttention (line 90) | class LinearAttention(nn.Module):
    method __init__ (line 91) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 98) | def forward(self, x):
  class SpatialSelfAttention (line 109) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 110) | def __init__(self, in_channels):
    method forward (line 136) | def forward(self, x):
  class CrossAttention (line 162) | class CrossAttention(nn.Module):
    method __init__ (line 163) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 180) | def forward(self, x, context=None, mask=None):
  class BasicSpatialTransformer (line 210) | class BasicSpatialTransformer(nn.Module):
    method __init__ (line 211) | def __init__(self, dim, n_heads, d_head, context_dim=None, checkpoint=...
    method forward (line 233) | def forward(self, x, context=None):
    method _forward (line 236) | def _forward(self, x, context):
  class BasicTransformerBlock (line 253) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 254) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 267) | def forward(self, x, context=None):
    method _forward (line 270) | def _forward(self, x, context=None):
  class ConvFeedForward (line 276) | class ConvFeedForward(nn.Module):
    method __init__ (line 277) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 292) | def forward(self, x):
  class SpatialTransformer (line 296) | class SpatialTransformer(nn.Module):
    method __init__ (line 304) | def __init__(self, in_channels, n_heads, d_head,
    method forward (line 330) | def forward(self, x, context=None):

FILE: ldm/modules/diffusionmodules/model.py
  function get_timestep_embedding (line 12) | def get_timestep_embedding(timesteps, embedding_dim):
  function nonlinearity (line 33) | def nonlinearity(x):
  function Normalize (line 38) | def Normalize(in_channels, num_groups=32):
  class Upsample (line 42) | class Upsample(nn.Module):
    method __init__ (line 43) | def __init__(self, in_channels, with_conv):
    method forward (line 53) | def forward(self, x):
  class Downsample (line 60) | class Downsample(nn.Module):
    method __init__ (line 61) | def __init__(self, in_channels, with_conv):
    method forward (line 72) | def forward(self, x):
  class ResnetBlock (line 82) | class ResnetBlock(nn.Module):
    method __init__ (line 83) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 121) | def forward(self, x, temb):
  class LinAttnBlock (line 144) | class LinAttnBlock(LinearAttention):
    method __init__ (line 146) | def __init__(self, in_channels):
  class AttnBlock (line 150) | class AttnBlock(nn.Module):
    method __init__ (line 151) | def __init__(self, in_channels):
    method forward (line 178) | def forward(self, x):
  function make_attn (line 205) | def make_attn(in_channels, attn_type="vanilla"):
  class Model (line 216) | class Model(nn.Module):
    method __init__ (line 217) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 316) | def forward(self, x, t=None, context=None):
    method get_last_layer (line 364) | def get_last_layer(self):
  class Encoder (line 368) | class Encoder(nn.Module):
    method __init__ (line 369) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 434) | def forward(self, x):
  class Decoder (line 462) | class Decoder(nn.Module):
    method __init__ (line 463) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 535) | def forward(self, z):
  class SimpleDecoder (line 571) | class SimpleDecoder(nn.Module):
    method __init__ (line 572) | def __init__(self, in_channels, out_channels, *args, **kwargs):
    method forward (line 594) | def forward(self, x):
  class UpsampleDecoder (line 607) | class UpsampleDecoder(nn.Module):
    method __init__ (line 608) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso...
    method forward (line 641) | def forward(self, x):
  class LatentRescaler (line 655) | class LatentRescaler(nn.Module):
    method __init__ (line 656) | def __init__(self, factor, in_channels, mid_channels, out_channels, de...
    method forward (line 680) | def forward(self, x):
  class MergedRescaleEncoder (line 692) | class MergedRescaleEncoder(nn.Module):
    method __init__ (line 693) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
    method forward (line 705) | def forward(self, x):
  class MergedRescaleDecoder (line 711) | class MergedRescaleDecoder(nn.Module):
    method __init__ (line 712) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att...
    method forward (line 722) | def forward(self, x):
  class Upsampler (line 728) | class Upsampler(nn.Module):
    method __init__ (line 729) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu...
    method forward (line 741) | def forward(self, x):
  class Resize (line 747) | class Resize(nn.Module):
    method __init__ (line 748) | def __init__(self, in_channels=None, learned=False, mode="bilinear"):
    method forward (line 763) | def forward(self, x, scale_factor=1.0):
  class FirstStagePostProcessor (line 770) | class FirstStagePostProcessor(nn.Module):
    method __init__ (line 772) | def __init__(self, ch_mult:list, in_channels,
    method instantiate_pretrained (line 807) | def instantiate_pretrained(self, config):
    method encode_with_pretrained (line 816) | def encode_with_pretrained(self,x):
    method forward (line 822) | def forward(self,x):

FILE: ldm/modules/diffusionmodules/openaimodel.py
  function convert_module_to_f16 (line 25) | def convert_module_to_f16(x):
  function convert_module_to_f32 (line 28) | def convert_module_to_f32(x):
  class AttentionPool2d (line 33) | class AttentionPool2d(nn.Module):
    method __init__ (line 38) | def __init__(
    method forward (line 52) | def forward(self, x):
  class TimestepBlock (line 63) | class TimestepBlock(nn.Module):
    method forward (line 69) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 75) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 81) | def forward(self, x, emb, context=None):
  class Upsample (line 92) | class Upsample(nn.Module):
    method __init__ (line 101) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 110) | def forward(self, x):
  class TransposedUpsample (line 122) | class TransposedUpsample(nn.Module):
    method __init__ (line 124) | def __init__(self, channels, out_channels=None, ks=5):
    method forward (line 131) | def forward(self,x):
  class Downsample (line 135) | class Downsample(nn.Module):
    method __init__ (line 144) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi...
    method forward (line 159) | def forward(self, x):
  class ResBlock (line 164) | class ResBlock(TimestepBlock):
    method __init__ (line 180) | def __init__(
    method forward (line 244) | def forward(self, x, emb):
    method _forward (line 256) | def _forward(self, x, emb):
  class AttentionBlock (line 279) | class AttentionBlock(nn.Module):
    method __init__ (line 286) | def __init__(
    method forward (line 315) | def forward(self, x):
    method _forward (line 319) | def _forward(self, x):
  function count_flops_attn (line 328) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 348) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 353) | def __init__(self, n_heads):
    method forward (line 357) | def forward(self, qkv):
    method count_flops (line 376) | def count_flops(model, _x, y):
  class QKVAttention (line 380) | class QKVAttention(nn.Module):
    method __init__ (line 385) | def __init__(self, n_heads):
    method forward (line 389) | def forward(self, qkv):
    method count_flops (line 410) | def count_flops(model, _x, y):
  class UNetModel (line 414) | class UNetModel(nn.Module):
    method __init__ (line 444) | def __init__(
    method convert_to_fp16 (line 729) | def convert_to_fp16(self):
    method convert_to_fp32 (line 737) | def convert_to_fp32(self):
    method forward (line 745) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
  class EncoderUNetModel (line 780) | class EncoderUNetModel(nn.Module):
    method __init__ (line 786) | def __init__(
    method convert_to_fp16 (line 959) | def convert_to_fp16(self):
    method convert_to_fp32 (line 966) | def convert_to_fp32(self):
    method forward (line 973) | def forward(self, x, timesteps):

FILE: ldm/modules/diffusionmodules/util.py
  function make_beta_schedule (line 21) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
  function make_ddim_timesteps (line 46) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
  function make_ddim_sampling_parameters (line 63) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
  function betas_for_alpha_bar (line 77) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  function extract_into_tensor (line 96) | def extract_into_tensor(a, t, x_shape):
  function checkpoint (line 102) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 119) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 121) | def forward(ctx, run_function, length, *args):
    method backward (line 131) | def backward(ctx, *output_grads):
  function timestep_embedding (line 151) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
  function zero_module (line 174) | def zero_module(module):
  function scale_module (line 183) | def scale_module(module, scale):
  function mean_flat (line 192) | def mean_flat(tensor):
  function normalization (line 199) | def normalization(channels):
  class SiLU (line 209) | class SiLU(nn.Module):
    method forward (line 210) | def forward(self, x):
  class GroupNorm32 (line 214) | class GroupNorm32(nn.GroupNorm):
    method forward (line 215) | def forward(self, x):
  function conv_nd (line 218) | def conv_nd(dims, *args, **kwargs):
  function linear (line 231) | def linear(*args, **kwargs):
  function avg_pool_nd (line 238) | def avg_pool_nd(dims, *args, **kwargs):
  class HybridConditioner (line 251) | class HybridConditioner(nn.Module):
    method __init__ (line 253) | def __init__(self, c_concat_config, c_crossattn_config):
    method forward (line 258) | def forward(self, c_concat, c_crossattn):
  function noise_like (line 264) | def noise_like(shape, device, repeat=False):

FILE: ldm/modules/distributions/distributions.py
  class AbstractDistribution (line 5) | class AbstractDistribution:
    method sample (line 6) | def sample(self):
    method mode (line 9) | def mode(self):
  class DiracDistribution (line 13) | class DiracDistribution(AbstractDistribution):
    method __init__ (line 14) | def __init__(self, value):
    method sample (line 17) | def sample(self):
    method mode (line 20) | def mode(self):
  class DiagonalGaussianDistribution (line 24) | class DiagonalGaussianDistribution(object):
    method __init__ (line 25) | def __init__(self, parameters, deterministic=False):
    method sample (line 35) | def sample(self):
    method kl (line 39) | def kl(self, other=None):
    method nll (line 53) | def nll(self, sample, dims=[1,2,3]):
    method mode (line 61) | def mode(self):
  function normal_kl (line 65) | def normal_kl(mean1, logvar1, mean2, logvar2):

FILE: ldm/modules/encoders/modules.py
  class AbstractEncoder (line 12) | class AbstractEncoder(nn.Module):
    method __init__ (line 13) | def __init__(self):
    method encode (line 16) | def encode(self, *args, **kwargs):
  class IdentityEncoder (line 19) | class IdentityEncoder(AbstractEncoder):
    method encode (line 21) | def encode(self, x):
  class FaceClipEncoder (line 24) | class FaceClipEncoder(AbstractEncoder):
    method __init__ (line 25) | def __init__(self, augment=True, retreival_key=None):
    method forward (line 31) | def forward(self, img):
    method encode (line 54) | def encode(self, img):
  class FaceIdClipEncoder (line 61) | class FaceIdClipEncoder(AbstractEncoder):
    method __init__ (line 62) | def __init__(self):
    method forward (line 69) | def forward(self, img):
    method encode (line 84) | def encode(self, img):
  class ClassEmbedder (line 91) | class ClassEmbedder(nn.Module):
    method __init__ (line 92) | def __init__(self, embed_dim, n_classes=1000, key='class'):
    method forward (line 97) | def forward(self, batch, key=None):
  class TransformerEmbedder (line 106) | class TransformerEmbedder(AbstractEncoder):
    method __init__ (line 108) | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, devic...
    method forward (line 114) | def forward(self, tokens):
    method encode (line 119) | def encode(self, x):
  class BERTTokenizer (line 123) | class BERTTokenizer(AbstractEncoder):
    method __init__ (line 125) | def __init__(self, device="cuda", vq_interface=True, max_length=77):
    method forward (line 133) | def forward(self, text):
    method encode (line 140) | def encode(self, text):
    method decode (line 146) | def decode(self, text):
  class BERTEmbedder (line 150) | class BERTEmbedder(AbstractEncoder):
    method __init__ (line 152) | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
    method forward (line 163) | def forward(self, text):
    method encode (line 171) | def encode(self, text):
  function disabled_train (line 178) | def disabled_train(self, mode=True):
  class FrozenT5Embedder (line 184) | class FrozenT5Embedder(AbstractEncoder):
    method __init__ (line 186) | def __init__(self, version="google/t5-v1_1-large", device="cuda", max_...
    method freeze (line 194) | def freeze(self):
    method forward (line 200) | def forward(self, text):
    method encode (line 209) | def encode(self, text):
  class FrozenFaceEncoder (line 215) | class FrozenFaceEncoder(AbstractEncoder):
    method __init__ (line 216) | def __init__(self, model_path, augment=False):
    method forward (line 237) | def forward(self, img):
    method encode (line 251) | def encode(self, img):
  class FrozenCLIPEmbedder (line 254) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 256) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
    method freeze (line 264) | def freeze(self):
    method forward (line 270) | def forward(self, text):
    method encode (line 279) | def encode(self, text):
  class ClipImageProjector (line 284) | class ClipImageProjector(AbstractEncoder):
    method __init__ (line 288) | def __init__(self, version="openai/clip-vit-large-patch14", max_length...
    method get_null_cond (line 301) | def get_null_cond(self, version, max_length):
    method preprocess (line 307) | def preprocess(self, x):
    method forward (line 317) | def forward(self, x):
    method encode (line 327) | def encode(self, im):
  class ProjectedFrozenCLIPEmbedder (line 330) | class ProjectedFrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 331) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
    method forward (line 336) | def forward(self, text):
    method encode (line 340) | def encode(self, text):
  class FrozenCLIPImageEmbedder (line 343) | class FrozenCLIPImageEmbedder(AbstractEncoder):
    method __init__ (line 348) | def __init__(
    method preprocess (line 363) | def preprocess(self, x):
    method forward (line 373) | def forward(self, x):
    method encode (line 381) | def encode(self, im):
  class FrozenCLIPImageMutliEmbedder (line 387) | class FrozenCLIPImageMutliEmbedder(AbstractEncoder):
    method __init__ (line 392) | def __init__(
    method preprocess (line 409) | def preprocess(self, x):
    method forward (line 423) | def forward(self, x):
    method encode (line 440) | def encode(self, im):
  class SpatialRescaler (line 443) | class SpatialRescaler(nn.Module):
    method __init__ (line 444) | def __init__(self,
    method forward (line 462) | def forward(self,x):
    method encode (line 471) | def encode(self, x):
  class LowScaleEncoder (line 479) | class LowScaleEncoder(nn.Module):
    method __init__ (line 480) | def __init__(self, model_config, linear_start, linear_end, timesteps=1...
    method register_schedule (line 490) | def register_schedule(self, beta_schedule="linear", timesteps=1000,
    method q_sample (line 517) | def q_sample(self, x_start, t, noise=None):
    method forward (line 522) | def forward(self, x):
    method decode (line 532) | def decode(self, z):

FILE: ldm/modules/x_transformer.py
  class AbsolutePositionalEmbedding (line 25) | class AbsolutePositionalEmbedding(nn.Module):
    method __init__ (line 26) | def __init__(self, dim, max_seq_len):
    method init_ (line 31) | def init_(self):
    method forward (line 34) | def forward(self, x):
  class FixedPositionalEmbedding (line 39) | class FixedPositionalEmbedding(nn.Module):
    method __init__ (line 40) | def __init__(self, dim):
    method forward (line 45) | def forward(self, x, seq_dim=1, offset=0):
  function exists (line 54) | def exists(val):
  function default (line 58) | def default(val, d):
  function always (line 64) | def always(val):
  function not_equals (line 70) | def not_equals(val):
  function equals (line 76) | def equals(val):
  function max_neg_value (line 82) | def max_neg_value(tensor):
  function pick_and_pop (line 88) | def pick_and_pop(keys, d):
  function group_dict_by_key (line 93) | def group_dict_by_key(cond, d):
  function string_begins_with (line 102) | def string_begins_with(prefix, str):
  function group_by_key_prefix (line 106) | def group_by_key_prefix(prefix, d):
  function groupby_prefix_and_trim (line 110) | def groupby_prefix_and_trim(prefix, d):
  class Scale (line 117) | class Scale(nn.Module):
    method __init__ (line 118) | def __init__(self, value, fn):
    method forward (line 123) | def forward(self, x, **kwargs):
  class Rezero (line 128) | class Rezero(nn.Module):
    method __init__ (line 129) | def __init__(self, fn):
    method forward (line 134) | def forward(self, x, **kwargs):
  class ScaleNorm (line 139) | class ScaleNorm(nn.Module):
    method __init__ (line 140) | def __init__(self, dim, eps=1e-5):
    method forward (line 146) | def forward(self, x):
  class RMSNorm (line 151) | class RMSNorm(nn.Module):
    method __init__ (line 152) | def __init__(self, dim, eps=1e-8):
    method forward (line 158) | def forward(self, x):
  class Residual (line 163) | class Residual(nn.Module):
    method forward (line 164) | def forward(self, x, residual):
  class GRUGating (line 168) | class GRUGating(nn.Module):
    method __init__ (line 169) | def __init__(self, dim):
    method forward (line 173) | def forward(self, x, residual):
  class GEGLU (line 184) | class GEGLU(nn.Module):
    method __init__ (line 185) | def __init__(self, dim_in, dim_out):
    method forward (line 189) | def forward(self, x):
  class FeedForward (line 194) | class FeedForward(nn.Module):
    method __init__ (line 195) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 210) | def forward(self, x):
  class Attention (line 215) | class Attention(nn.Module):
    method __init__ (line 216) | def __init__(
    method forward (line 268) | def forward(
  class AttentionLayers (line 370) | class AttentionLayers(nn.Module):
    method __init__ (line 371) | def __init__(
    method forward (line 481) | def forward(
  class Encoder (line 541) | class Encoder(AttentionLayers):
    method __init__ (line 542) | def __init__(self, **kwargs):
  class TransformerWrapper (line 548) | class TransformerWrapper(nn.Module):
    method __init__ (line 549) | def __init__(
    method init_ (line 595) | def init_(self):
    method forward (line 598) | def forward(

FILE: ldm/thirdp/psp/helpers.py
  class Flatten (line 12) | class Flatten(Module):
    method forward (line 13) | def forward(self, input):
  function l2_norm (line 17) | def l2_norm(input, axis=1):
  class Bottleneck (line 23) | class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
  function get_block (line 27) | def get_block(in_channel, depth, num_units, stride=2):
  function get_blocks (line 31) | def get_blocks(num_layers):
  class SEModule (line 58) | class SEModule(Module):
    method __init__ (line 59) | def __init__(self, channels, reduction):
    method forward (line 67) | def forward(self, x):
  class bottleneck_IR (line 77) | class bottleneck_IR(Module):
    method __init__ (line 78) | def __init__(self, in_channel, depth, stride):
    method forward (line 93) | def forward(self, x):
  class bottleneck_IR_SE (line 99) | class bottleneck_IR_SE(Module):
    method __init__ (line 100) | def __init__(self, in_channel, depth, stride):
    method forward (line 118) | def forward(self, x):

FILE: ldm/thirdp/psp/id_loss.py
  class IDFeatures (line 7) | class IDFeatures(nn.Module):
    method __init__ (line 8) | def __init__(self, model_path):
    method forward (line 16) | def forward(self, x, crop=False):

FILE: ldm/thirdp/psp/model_irse.py
  class Backbone (line 11) | class Backbone(Module):
    method __init__ (line 12) | def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, ...
    method forward (line 46) | def forward(self, x):
  function IR_50 (line 53) | def IR_50(input_size):
  function IR_101 (line 59) | def IR_101(input_size):
  function IR_152 (line 65) | def IR_152(input_size):
  function IR_SE_50 (line 71) | def IR_SE_50(input_size):
  function IR_SE_101 (line 77) | def IR_SE_101(input_size):
  function IR_SE_152 (line 83) | def IR_SE_152(input_size):

FILE: ldm/util.py
  function normalize (line 26) | def normalize(vec):
  function look_at (line 31) | def look_at(cam_location, point):
  function az_el_to_points (line 54) | def az_el_to_points(azimuths, elevations):
  function get_3x4_RT_matrix_from_az_el (line 60) | def get_3x4_RT_matrix_from_az_el(az, el, distance):
  function pil_rectangle_crop (line 78) | def pil_rectangle_crop(im):
  function add_margin (line 97) | def add_margin(pil_img, color=0, size=256):
  function create_carvekit_interface (line 104) | def create_carvekit_interface():
  function load_and_preprocess (line 121) | def load_and_preprocess(interface, input_im):
  function log_txt_as_img (line 147) | def log_txt_as_img(wh, xc, size=10):
  function ismap (line 171) | def ismap(x):
  function isimage (line 177) | def isimage(x):
  function exists (line 183) | def exists(x):
  function default (line 187) | def default(val, d):
  function mean_flat (line 193) | def mean_flat(tensor):
  function count_params (line 201) | def count_params(model, verbose=False):
  function instantiate_from_config (line 208) | def instantiate_from_config(config):
  function get_obj_from_str (line 218) | def get_obj_from_str(string, reload=False):
  class AdamWwithEMAandWings (line 226) | class AdamWwithEMAandWings(optim.Optimizer):
    method __init__ (line 228) | def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  #...
    method __setstate__ (line 249) | def __setstate__(self, state):
    method step (line 255) | def step(self, closure=None):
  function prepare_inputs (line 335) | def prepare_inputs(image_input, elevation_input, crop_size=-1, image_siz...
  function prepare_proxy (line 364) | def prepare_proxy(proxy_path, start_view_index=0):
  function save_pickle (line 376) | def save_pickle(data, pkl_path):
  function read_pickle (line 381) | def read_pickle(pkl_path):
  class Ctrl3DParams (line 387) | class Ctrl3DParams:
  function sample_proxy (line 393) | def sample_proxy(object_dir, num_proxy=256, overwrite=False):

FILE: raymarching/backend.py
  function find_cl_path (line 17) | def find_cl_path():

FILE: raymarching/raymarching.py
  class _near_far_from_aabb (line 19) | class _near_far_from_aabb(Function):
    method forward (line 22) | def forward(ctx, rays_o, rays_d, aabb, min_near=0.2):
  class _sph_from_ray (line 52) | class _sph_from_ray(Function):
    method forward (line 55) | def forward(ctx, rays_o, rays_d, radius):
  class _morton3D (line 83) | class _morton3D(Function):
    method forward (line 85) | def forward(ctx, coords):
  class _morton3D_invert (line 106) | class _morton3D_invert(Function):
    method forward (line 108) | def forward(ctx, indices):
  class _packbits (line 129) | class _packbits(Function):
    method forward (line 132) | def forward(ctx, grid, thresh, bitfield=None):
  class _march_rays_train (line 161) | class _march_rays_train(Function):
    method forward (line 164) | def forward(ctx, rays_o, rays_d, bound, density_bitfield, C, H, nears,...
  class _composite_rays_train (line 238) | class _composite_rays_train(Function):
    method forward (line 241) | def forward(ctx, sigmas, rgbs, deltas, rays, T_thresh=1e-4):
    method backward (line 273) | def backward(ctx, grad_weights_sum, grad_depth, grad_image):
  class _march_rays (line 297) | class _march_rays(Function):
    method forward (line 300) | def forward(ctx, n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, ...
  class _composite_rays (line 351) | class _composite_rays(Function):
    method forward (line 354) | def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, de...

FILE: raymarching/setup.py
  function find_cl_path (line 18) | def find_cl_path():

FILE: raymarching/src/bindings.cpp
  function PYBIND11_MODULE (line 5) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: renderer/agg_net.py
  function weights_init (line 5) | def weights_init(m):
  class NeRF (line 11) | class NeRF(nn.Module):
    method __init__ (line 12) | def __init__(self, vol_n=8+8, feat_ch=8+16+32+3, hid_n=64):
    method forward (line 27) | def forward(self, vox_feat, img_feat_rgb_dir, source_img_mask):
  class Agg (line 44) | class Agg(nn.Module):
    method __init__ (line 45) | def __init__(self, feat_ch):
    method masked_mean_var (line 58) | def masked_mean_var(self, img_feat_rgb, source_img_mask):
    method forward (line 66) | def forward(self, img_feat_rgb_dir, source_img_mask):

FILE: renderer/cost_reg_net.py
  class ConvBnReLU3D (line 3) | class ConvBnReLU3D(nn.Module):
    method __init__ (line 4) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
    method forward (line 10) | def forward(self, x):
  class CostRegNet (line 13) | class CostRegNet(nn.Module):
    method __init__ (line 14) | def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
    method forward (line 44) | def forward(self, x):
  class MinCostRegNet (line 60) | class MinCostRegNet(nn.Module):
    method __init__ (line 61) | def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
    method forward (line 84) | def forward(self, x):

FILE: renderer/dummy_dataset.py
  class DummyDataset (line 5) | class DummyDataset(pl.LightningDataModule):
    method __init__ (line 6) | def __init__(self,seed):
    method setup (line 9) | def setup(self, stage):
    method train_dataloader (line 16) | def train_dataloader(self):
    method val_dataloader (line 19) | def val_dataloader(self):
    method test_dataloader (line 22) | def test_dataloader(self):
  class DummyData (line 25) | class DummyData(Dataset):
    method __init__ (line 26) | def __init__(self,is_train):
    method __len__ (line 29) | def __len__(self):
    method __getitem__ (line 35) | def __getitem__(self, index):

FILE: renderer/feature_net.py
  class ConvBnReLU (line 4) | class ConvBnReLU(nn.Module):
    method __init__ (line 5) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
    method forward (line 11) | def forward(self, x):
  class FeatureNet (line 14) | class FeatureNet(nn.Module):
    method __init__ (line 15) | def __init__(self, norm_act=nn.BatchNorm2d):
    method _upsample_add (line 28) | def _upsample_add(self, x, y):
    method forward (line 31) | def forward(self, x):

FILE: renderer/neus_networks.py
  class Embedder (line 10) | class Embedder:
    method __init__ (line 11) | def __init__(self, **kwargs):
    method create_embedding_fn (line 15) | def create_embedding_fn(self):
    method embed (line 39) | def embed(self, inputs):
  function get_embedder (line 43) | def get_embedder(multires, input_dims=3):
  class SDFNetwork (line 60) | class SDFNetwork(nn.Module):
    method __init__ (line 61) | def __init__(self, d_in, d_out, d_hidden, n_layers, skip_in=(4,), mult...
    method forward (line 113) | def forward(self, inputs):
    method sdf (line 132) | def sdf(self, x):
    method sdf_hidden_appearance (line 135) | def sdf_hidden_appearance(self, x):
    method gradient (line 138) | def gradient(self, x):
    method sdf_normal (line 152) | def sdf_normal(self, x):
  class SDFNetworkWithFeature (line 166) | class SDFNetworkWithFeature(nn.Module):
    method __init__ (line 167) | def __init__(self, cube, dp_in, df_in, d_out, d_hidden, n_layers, skip...
    method forward (line 221) | def forward(self, points):
    method sdf (line 247) | def sdf(self, x):
    method sdf_hidden_appearance (line 250) | def sdf_hidden_appearance(self, x):
    method gradient (line 253) | def gradient(self, x):
    method sdf_normal (line 267) | def sdf_normal(self, x):
  class VanillaMLP (line 282) | class VanillaMLP(nn.Module):
    method __init__ (line 283) | def __init__(self, dim_in, dim_out, n_neurons, n_hidden_layers):
    method forward (line 295) | def forward(self, x):
    method make_linear (line 299) | def make_linear(self, dim_in, dim_out, is_first, is_last):
    method make_activation (line 320) | def make_activation(self):
  class SDFHashGridNetwork (line 327) | class SDFHashGridNetwork(nn.Module):
    method __init__ (line 328) | def __init__(self, bound=0.5, feats_dim=13):
    method forward (line 358) | def forward(self, x):
    method sdf (line 369) | def sdf(self, x):
    method gradient (line 372) | def gradient(self, x):
    method sdf_normal (line 386) | def sdf_normal(self, x):
  class RenderingFFNetwork (line 400) | class RenderingFFNetwork(nn.Module):
    method __init__ (line 401) | def __init__(self, in_feats_dim=12):
    method forward (line 422) | def forward(self, points, normals, view_dirs, feature_vectors):
  class RenderingNetwork (line 433) | class RenderingNetwork(nn.Module):
    method __init__ (line 434) | def __init__(self, d_feature, d_in, d_out, d_hidden,
    method forward (line 463) | def forward(self, points, normals, view_dirs, feature_vectors):
  class SingleVarianceNetwork (line 488) | class SingleVarianceNetwork(nn.Module):
    method __init__ (line 489) | def __init__(self, init_val, activation='exp'):
    method forward (line 494) | def forward(self, x):
    method warp (line 501) | def warp(self, x, inv_s):

FILE: renderer/ngp_renderer.py
  function custom_meshgrid (line 17) | def custom_meshgrid(*args):
  function sample_pdf (line 24) | def sample_pdf(bins, weights, n_samples, det=False):
  function plot_pointcloud (line 61) | def plot_pointcloud(pc, color=None):
  class NGPRenderer (line 73) | class NGPRenderer(nn.Module):
    method __init__ (line 74) | def __init__(self,
    method forward (line 115) | def forward(self, x, d):
    method density (line 119) | def density(self, x):
    method color (line 122) | def color(self, x, d, mask=None, **kwargs):
    method reset_extra_state (line 125) | def reset_extra_state(self):
    method run (line 137) | def run(self, rays_o, rays_d, num_steps=128, upsample_steps=128, bg_co...
    method run_cuda (line 268) | def run_cuda(self, rays_o, rays_d, dt_gamma=0, bg_color=None, perturb=...
    method mark_untrained_grid (line 369) | def mark_untrained_grid(self, poses, intrinsic, S=64):
    method update_extra_state (line 434) | def update_extra_state(self, decay=0.95, S=128):
    method render (line 529) | def render(self, rays_o, rays_d, staged=False, max_ray_batch=4096, **k...
  class _trunc_exp (line 543) | class _trunc_exp(Function):
    method forward (line 546) | def forward(ctx, x):
    method backward (line 552) | def backward(ctx, g):
  class NGPNetwork (line 558) | class NGPNetwork(NGPRenderer):
    method __init__ (line 559) | def __init__(self,
    method forward (line 636) | def forward(self, x, d):
    method density (line 669) | def density(self, x):
    method color (line 691) | def color(self, x, d, mask=None, geo_feat=None, **kwargs):

FILE: renderer/renderer.py
  function sample_pdf (line 25) | def sample_pdf(bins, weights, n_samples, det=True):
  function near_far_from_sphere (line 59) | def near_far_from_sphere(rays_o, rays_d, radius=DEFAULT_RADIUS):
  class BackgroundRemoval (line 67) | class BackgroundRemoval:
    method __init__ (line 68) | def __init__(self, device='cuda'):
    method __call__ (line 84) | def __call__(self, image):
  class BaseRenderer (line 92) | class BaseRenderer(nn.Module):
    method __init__ (line 93) | def __init__(self, train_batch_num, test_batch_num):
    method render_impl (line 99) | def render_impl(self, ray_batch, is_train, step):
    method render_with_loss (line 103) | def render_with_loss(self, ray_batch, is_train, step):
    method render (line 106) | def render(self, ray_batch, is_train, step):
  class NeuSRenderer (line 124) | class NeuSRenderer(BaseRenderer):
    method __init__ (line 125) | def __init__(self, train_batch_num, test_batch_num, lambda_eikonal_los...
    method get_vertex_colors (line 144) | def get_vertex_colors(self, vertices):
    method upsample (line 165) | def upsample(self, rays_o, rays_d, z_vals, sdf, n_importance, inv_s):
    method cat_z_vals (line 198) | def cat_z_vals(self, rays_o, rays_d, z_vals, new_z_vals, sdf, last=Fal...
    method sample_depth (line 215) | def sample_depth(self, rays_o, rays_d, near, far, perturb):
    method compute_sdf_alpha (line 243) | def compute_sdf_alpha(self, points, dists, dirs, cos_anneal_ratio, step):
    method get_anneal_val (line 270) | def get_anneal_val(self, step):
    method get_inner_mask (line 276) | def get_inner_mask(self, points):
    method render_impl (line 279) | def render_impl(self, ray_batch, is_train, step):
    method render_with_loss (line 323) | def render_with_loss(self, ray_batch, is_train, step):
  class NeRFRenderer (line 351) | class NeRFRenderer(BaseRenderer):
    method __init__ (line 352) | def __init__(self, train_batch_num, test_batch_num, bound=0.5, use_mas...
    method render_impl (line 364) | def render_impl(self, ray_batch, is_train, step):
    method render_with_loss (line 379) | def render_with_loss(self, ray_batch, is_train, step):
  class RendererTrainer (line 398) | class RendererTrainer(pl.LightningModule):
    method __init__ (line 399) | def __init__(self, image_path, total_steps, warm_up_steps, log_dir, tr...
    method _construct_ray_batch (line 438) | def _construct_ray_batch(self, images_info):
    method load_model (line 464) | def load_model(cfg, ckpt):
    method _init_dataset (line 473) | def _init_dataset(self):
    method _shuffle_train_batch (line 512) | def _shuffle_train_batch(self):
    method _shuffle_train_fg_batch (line 518) | def _shuffle_train_fg_batch(self):
    method training_step (line 525) | def training_step(self, batch, batch_idx):
    method _slice_images_info (line 545) | def _slice_images_info(self, index):
    method validation_step (line 549) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 576) | def configure_optimizers(self):

FILE: train_diffusion.py
  function rank_zero_print (line 22) | def rank_zero_print(*args):
  function get_parser (line 25) | def get_parser(**parser_kwargs):
  function trainer_args (line 46) | def trainer_args(opt):
  class SetupCallback (line 52) | class SetupCallback(Callback):
    method __init__ (line 53) | def __init__(self, resume, logdir, ckptdir, cfgdir, config):
    method on_fit_start (line 61) | def on_fit_start(self, trainer, pl_module):
  class ImageLogger (line 74) | class ImageLogger(Callback):
    method __init__ (line 75) | def __init__(self, batch_frequency, max_images, log_images_kwargs=None):
    method log_to_logger (line 82) | def log_to_logger(self, pl_module, images, split):
    method log_to_file (line 91) | def log_to_file(self, save_dir, split, images, global_step, current_ep...
    method log_img (line 105) | def log_img(self, pl_module, batch, split="train"):
    method check_frequency (line 128) | def check_frequency(self, check_idx):
    method on_train_batch_end (line 134) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...
    method on_validation_batch_end (line 138) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ...
  class CUDACallback (line 144) | class CUDACallback(Callback):
    method on_train_epoch_start (line 146) | def on_train_epoch_start(self, trainer, pl_module):
    method on_train_epoch_end (line 152) | def on_train_epoch_end(self, trainer, pl_module):
  function get_node_name (line 166) | def get_node_name(name, parent_name):
  class ResumeCallBacks (line 174) | class ResumeCallBacks(Callback):
    method on_train_start (line 175) | def on_train_start(self, trainer, pl_module):
  function load_pretrain_stable_diffusion (line 178) | def load_pretrain_stable_diffusion(new_model, finetune_from):
  function get_optional_dict (line 206) | def get_optional_dict(name, config):

FILE: train_renderer.py
  class ResumeCallBacks (line 23) | class ResumeCallBacks(Callback):
    method __init__ (line 24) | def __init__(self):
    method on_train_start (line 27) | def on_train_start(self, trainer, pl_module):
  function render_images (line 30) | def render_images(model, output,):
  function extract_fields (line 78) | def extract_fields(bound_min, bound_max, resolution, query_func, batch_s...
  function extract_geometry (line 98) | def extract_geometry(bound_min, bound_max, resolution, threshold, query_...
  function extract_mesh (line 108) | def extract_mesh(model, output, resolution=512):
  function main (line 119) | def main():

FILE: workflow/inference_comfyui_api.py
  function queue_prompt (line 16) | def queue_prompt(prompt):
  function get_image (line 22) | def get_image(filename, subfolder, folder_type):
  function get_history (line 28) | def get_history(prompt_id):
  function get_images (line 32) | def get_images(ws, prompt):