SYMBOL INDEX (593 symbols across 38 files)

FILE: configs/celeba.py
  function get_celeba_configs (line 7) | def get_celeba_configs():

FILE: datasets/AFHQ_dataset.py
  class AFHQ_dataset (line 7) | class AFHQ_dataset(Dataset):
    method __init__ (line 8) | def __init__(self, image_root, transform=None, mode='train', animal_cl...
    method __getitem__ (line 14) | def __getitem__(self, index):
    method __len__ (line 22) | def __len__(self):
  function get_afhq_dataset (line 28) | def get_afhq_dataset(data_root, config):

FILE: datasets/CelebA_HQ_dataset.py
  class MultiResolutionDataset (line 8) | class MultiResolutionDataset(Dataset):
    method __init__ (line 9) | def __init__(self, path, transform, resolution=256):
    method __len__ (line 28) | def __len__(self):
    method __getitem__ (line 31) | def __getitem__(self, index):
  function get_celeba_dataset (line 45) | def get_celeba_dataset(data_root, config):

FILE: datasets/CelebA_HQ_dataset_dialog.py
  class MultiResolutionDialogDataset (line 9) | class MultiResolutionDialogDataset(Dataset):
    method __init__ (line 10) | def __init__(self, path, transform, train=True, guid="Smiling"):
    method __len__ (line 34) | def __len__(self):
    method __getitem__ (line 37) | def __getitem__(self, index):
  function get_celeba_dialog_dataset (line 55) | def get_celeba_dialog_dataset(data_root, config):

FILE: datasets/CelebA_HQ_dataset_with_attr.py
  class CustomImageDataset (line 11) | class CustomImageDataset(Dataset):
    method __init__ (line 12) | def __init__(self, img_dir, transform=None, test_nums=10000, train=True):
    method __len__ (line 69) | def __len__(self):
    method __getitem__ (line 72) | def __getitem__(self, idx):
  class MultiResolutionDataset (line 82) | class MultiResolutionDataset(Dataset):
    method __init__ (line 83) | def __init__(self, path, transform, resolution=256):
    method __len__ (line 128) | def __len__(self):
    method __getitem__ (line 131) | def __getitem__(self, index):
  function get_celeba_dataset_attr (line 146) | def get_celeba_dataset_attr(data_root, config):

FILE: datasets/IMAGENET_dataset.py
  function get_imagenet_dataset (line 10) | def get_imagenet_dataset(data_root, config, class_num=None, random_crop=...
  class IMAGENET_dataset (line 22) | class IMAGENET_dataset(Dataset):
    method __init__ (line 23) | def __init__(self, image_root, mode='val', class_num=None, img_size=51...
    method __getitem__ (line 45) | def __getitem__(self, index):
    method __len__ (line 66) | def __len__(self):
  function center_crop_arr (line 70) | def center_crop_arr(pil_image, image_size):
  function random_crop_arr (line 90) | def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_f...

FILE: datasets/LSUN_dataset.py
  class VisionDataset (line 14) | class VisionDataset(data.Dataset):
    method __init__ (line 17) | def __init__(self, root, transforms=None, transform=None, target_trans...
    method __getitem__ (line 36) | def __getitem__(self, index):
    method __len__ (line 39) | def __len__(self):
    method __repr__ (line 42) | def __repr__(self):
    method _format_transform_repr (line 57) | def _format_transform_repr(self, transform, head):
    method extra_repr (line 62) | def extra_repr(self):
  class StandardTransform (line 66) | class StandardTransform(object):
    method __init__ (line 67) | def __init__(self, transform=None, target_transform=None):
    method __call__ (line 71) | def __call__(self, input, target):
    method _format_transform_repr (line 78) | def _format_transform_repr(self, transform, head):
    method __repr__ (line 83) | def __repr__(self):
  class LSUNClass (line 96) | class LSUNClass(VisionDataset):
    method __init__ (line 97) | def __init__(self, root, transform=None, target_transform=None):
    method __getitem__ (line 123) | def __getitem__(self, index):
    method __len__ (line 142) | def __len__(self):
  class LSUN (line 147) | class LSUN(VisionDataset):
    method __init__ (line 161) | def __init__(self, root, classes="train", transform=None, target_trans...
    method _verify_classes (line 182) | def _verify_classes(self, classes):
    method __getitem__ (line 232) | def __getitem__(self, index):
    method __len__ (line 257) | def __len__(self):
    method extra_repr (line 260) | def extra_repr(self):
  function get_lsun_dataset (line 269) | def get_lsun_dataset(data_root, config):

FILE: datasets/data_utils.py
  class CustomImageDataset (line 14) | class CustomImageDataset(Dataset):
    method __init__ (line 15) | def __init__(self, img_dir, transform=None, test_nums=None, train=True):
    method __len__ (line 25) | def __len__(self):
    method __getitem__ (line 28) | def __getitem__(self, idx):
  function get_dataset (line 36) | def get_dataset(dataset_type, dataset_paths, config, target_class_num=No...
  function get_dataloader (line 67) | def get_dataloader(train_dataset, test_dataset, bs_train=1, num_workers=...

FILE: datasets/sc_loss_dataset.py
  class SemanticConsistencyDataset (line 10) | class SemanticConsistencyDataset(Dataset):
    method __init__ (line 11) | def __init__(self, path, transform= None, resolution=256):
    method __getitem__ (line 19) | def __getitem__(self, index):
    method __len__ (line 37) | def __len__(self):

FILE: diffusion_latent.py
  class Asyrp (line 31) | class Asyrp(object):
    method __init__ (line 32) | def __init__(self, args, config, device=None):
    method load_pretrained_model (line 76) | def load_pretrained_model(self):
    method run_training (line 129) | def run_training(self):
    method save_image (line 446) | def save_image(self, model, x_lat_tensor, seq_inv, seq_inv_next,
    method run_test (line 548) | def run_test(self):
    method precompute_pairs_with_h (line 878) | def precompute_pairs_with_h(self, model, img_path):
    method precompute_pairs (line 952) | def precompute_pairs(self, model, save_imgs=False):
    method random_noise_pairs (line 1088) | def random_noise_pairs(self, model, saved_noise=False, save_imgs=False):
    method compute_lpips_distance (line 1191) | def compute_lpips_distance(self):
    method set_t_edit_t_addnoise (line 1308) | def set_t_edit_t_addnoise(self, LPIPS_th=0.33, LPIPS_addnoise_th=0.1, ...

FILE: losses/clip_loss.py
  class DirectionLoss (line 11) | class DirectionLoss(torch.nn.Module):
    method __init__ (line 13) | def __init__(self, loss_type='mse'):
    method forward (line 24) | def forward(self, x, y):
  class CLIPLoss (line 30) | class CLIPLoss(torch.nn.Module):
    method __init__ (line 31) | def __init__(self, device, lambda_direction=1., lambda_patch=0., lambd...
    method tokenize (line 67) | def tokenize(self, strings: list):
    method encode_text (line 70) | def encode_text(self, tokens: list) -> torch.Tensor:
    method encode_images (line 73) | def encode_images(self, images: torch.Tensor) -> torch.Tensor:
    method encode_images_with_cnn (line 77) | def encode_images_with_cnn(self, images: torch.Tensor) -> torch.Tensor:
    method distance_with_templates (line 81) | def distance_with_templates(self, img: torch.Tensor, class_str: str, t...
    method get_text_features (line 90) | def get_text_features(self, class_str: str, templates=imagenet_templat...
    method get_image_features (line 102) | def get_image_features(self, img: torch.Tensor, norm: bool = True) -> ...
    method compute_text_direction (line 110) | def compute_text_direction(self, source_class: str, target_class: str)...
    method compute_img2img_direction (line 119) | def compute_img2img_direction(self, source_images: torch.Tensor, targe...
    method set_text_features (line 142) | def set_text_features(self, source_class: str, target_class: str) -> N...
    method clip_angle_loss (line 149) | def clip_angle_loss(self, src_img: torch.Tensor, source_class: str, ta...
    method compose_text_with_templates (line 167) | def compose_text_with_templates(self, text: str, templates=imagenet_te...
    method clip_directional_loss (line 170) | def clip_directional_loss(self, src_img: torch.Tensor, source_class: s...
    method global_clip_loss (line 182) | def global_clip_loss(self, img: torch.Tensor, text) -> torch.Tensor:
    method random_patch_centers (line 193) | def random_patch_centers(self, img_shape, num_patches, size):
    method generate_patches (line 202) | def generate_patches(self, img: torch.Tensor, patch_centers, size):
    method patch_scores (line 223) | def patch_scores(self, img: torch.Tensor, class_str: str, patch_center...
    method clip_patch_similarity (line 236) | def clip_patch_similarity(self, src_img: torch.Tensor, source_class: s...
    method patch_directional_loss (line 246) | def patch_directional_loss(self, src_img: torch.Tensor, source_class: ...
    method cnn_feature_loss (line 275) | def cnn_feature_loss(self, src_img: torch.Tensor, target_img: torch.Te...
    method forward (line 281) | def forward(self, src_img: torch.Tensor, source_class: str, target_img...

FILE: losses/id_loss.py
  class IDLoss (line 7) | class IDLoss(nn.Module):
    method __init__ (line 8) | def __init__(self, use_mobile_id=False):
    method extract_feats (line 17) | def extract_feats(self, x):
    method forward (line 23) | def forward(self, x, x_hat):

FILE: losses/resnet.py
  function conv3x3 (line 14) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 20) | class BasicBlock(nn.Module):
    method __init__ (line 21) | def __init__(self, in_chan, out_chan, stride=1):
    method forward (line 36) | def forward(self, x):
  function create_layer_basic (line 51) | def create_layer_basic(in_chan, out_chan, bnum, stride=1):
  class Resnet18 (line 58) | class Resnet18(nn.Module):
    method __init__ (line 59) | def __init__(self):
    method forward (line 71) | def forward(self, x):
    method init_weight (line 82) | def init_weight(self):
    method get_params (line 90) | def get_params(self):

FILE: main.py
  function parse_args_and_config (line 12) | def parse_args_and_config():
  function dict2namespace (line 311) | def dict2namespace(config):
  function main (line 322) | def main():

FILE: models/ddpm/diffusion.py
  function slerp (line 6) | def slerp(t,v0,v1):
  function get_timestep_embedding (line 42) | def get_timestep_embedding(timesteps, embedding_dim):
  function nonlinearity (line 63) | def nonlinearity(x):
  function Normalize (line 68) | def Normalize(in_channels):
  class Upsample (line 72) | class Upsample(nn.Module):
    method __init__ (line 73) | def __init__(self, in_channels, with_conv):
    method forward (line 83) | def forward(self, x):
  class Downsample (line 91) | class Downsample(nn.Module):
    method __init__ (line 92) | def __init__(self, in_channels, with_conv):
    method forward (line 103) | def forward(self, x):
  class ResnetBlock (line 113) | class ResnetBlock(nn.Module):
    method __init__ (line 114) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 151) | def forward(self, x, temb):
  class AttnBlock (line 173) | class AttnBlock(nn.Module):
    method __init__ (line 174) | def __init__(self, in_channels):
    method forward (line 200) | def forward(self, x):
  class DeltaBlock (line 228) | class DeltaBlock(nn.Module):
    method __init__ (line 229) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 251) | def forward(self, x, temb=None):
  class DeltaBlock_global (line 266) | class DeltaBlock_global(nn.Module):
    method __init__ (line 267) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 306) | def forward(self, x, temb, clip_direction):
  class DDPM (line 327) | class DDPM(nn.Module):
    method __init__ (line 328) | def __init__(self, config):
    method setattr_layers (line 433) | def setattr_layers(self, nums):
    method setattr_global_layer (line 451) | def setattr_global_layer(self, nums):
    method get_temb (line 464) | def get_temb(self, t):
    method forward (line 473) | def forward(self, x, t, index=None, t_edit=400, hs_coeff=(1.0, 1.0), d...
    method forward_layer_check (line 582) | def forward_layer_check(self, x, t, index=None, t_edit=400, hs_coeff=(...
    method multiple_attr (line 682) | def multiple_attr(self, x, t, index=None, maintain=400, rambda=(1.0,1....
    method interpolation2 (line 761) | def interpolation2(self, x, t, index=None, maintain=400, alpha=None):
    method forward_at (line 835) | def forward_at(self, x, t, index=None):
    method forward_global (line 904) | def forward_global(self, x, t, index=None, maintain=400, direction=None):

FILE: models/guided_diffusion/dist_util.py
  function setup_dist (line 21) | def setup_dist():
  function dev (line 45) | def dev():
  function load_state_dict (line 54) | def load_state_dict(path, **kwargs):
  function sync_params (line 77) | def sync_params(params):
  function _find_free_port (line 86) | def _find_free_port():

FILE: models/guided_diffusion/fp16_util.py
  function convert_module_to_f16 (line 15) | def convert_module_to_f16(l):
  function convert_module_to_f32 (line 25) | def convert_module_to_f32(l):
  function make_master_params (line 35) | def make_master_params(param_groups_and_shapes):
  function model_grads_to_master_grads (line 52) | def model_grads_to_master_grads(param_groups_and_shapes, master_params):
  function master_params_to_model_params (line 65) | def master_params_to_model_params(param_groups_and_shapes, master_params):
  function unflatten_master_params (line 78) | def unflatten_master_params(param_group, master_param):
  function get_param_groups_and_shapes (line 82) | def get_param_groups_and_shapes(named_model_params):
  function master_params_to_state_dict (line 95) | def master_params_to_state_dict(
  function state_dict_to_master_params (line 116) | def state_dict_to_master_params(model, state_dict, use_fp16):
  function zero_master_grads (line 128) | def zero_master_grads(master_params):
  function zero_grad (line 133) | def zero_grad(model_params):
  function param_grad_or_zeros (line 141) | def param_grad_or_zeros(param):
  class MixedPrecisionTrainer (line 148) | class MixedPrecisionTrainer:
    method __init__ (line 149) | def __init__(
    method zero_grad (line 173) | def zero_grad(self):
    method backward (line 176) | def backward(self, loss: th.Tensor):
    method optimize (line 183) | def optimize(self, opt: th.optim.Optimizer):
    method _optimize_fp16 (line 189) | def _optimize_fp16(self, opt: th.optim.Optimizer):
    method _optimize_normal (line 209) | def _optimize_normal(self, opt: th.optim.Optimizer):
    method _compute_norms (line 216) | def _compute_norms(self, grad_scale=1.0):
    method master_params_to_state_dict (line 226) | def master_params_to_state_dict(self, master_params):
    method state_dict_to_master_params (line 231) | def state_dict_to_master_params(self, state_dict):
  function check_overflow (line 235) | def check_overflow(value):

FILE: models/guided_diffusion/gaussian_diffusion.py
  function get_named_beta_schedule (line 18) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
  function betas_for_alpha_bar (line 45) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  class ModelMeanType (line 65) | class ModelMeanType(enum.Enum):
  class ModelVarType (line 75) | class ModelVarType(enum.Enum):
  class LossType (line 89) | class LossType(enum.Enum):
    method is_vb (line 97) | def is_vb(self):
  class GaussianDiffusion (line 101) | class GaussianDiffusion:
    method __init__ (line 118) | def __init__(
    method q_mean_variance (line 178) | def q_mean_variance(self, x_start, t):
    method q_sample (line 195) | def q_sample(self, x_start, t, noise=None):
    method q_posterior_mean_variance (line 215) | def q_posterior_mean_variance(self, x_start, x_t, t):
    method p_mean_variance (line 239) | def p_mean_variance(
    method _predict_xstart_from_eps (line 335) | def _predict_xstart_from_eps(self, x_t, t, eps):
    method _predict_xstart_from_xprev (line 342) | def _predict_xstart_from_xprev(self, x_t, t, xprev):
    method _predict_eps_from_xstart (line 352) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method _scale_timesteps (line 358) | def _scale_timesteps(self, t):
    method condition_mean (line 363) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
    method condition_score (line 378) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
    method p_sample (line 402) | def p_sample(
    method p_sample_loop (line 448) | def p_sample_loop(
    method p_sample_loop_progressive (line 494) | def p_sample_loop_progressive(
    method ddim_sample (line 544) | def ddim_sample(
    method ddim_reverse_sample (line 594) | def ddim_reverse_sample(
    method ddim_sample_loop (line 632) | def ddim_sample_loop(
    method ddim_sample_loop_progressive (line 666) | def ddim_sample_loop_progressive(
    method _vb_terms_bpd (line 716) | def _vb_terms_bpd(
    method training_losses (line 751) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
    method _prior_bpd (line 830) | def _prior_bpd(self, x_start):
    method calc_bpd_loop (line 848) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
  function _extract_into_tensor (line 906) | def _extract_into_tensor(arr, timesteps, broadcast_shape):

FILE: models/guided_diffusion/image_datasets.py
  function load_data (line 11) | def load_data(
  function _list_image_files_recursively (line 70) | def _list_image_files_recursively(data_dir):
  class ImageDataset (line 82) | class ImageDataset(Dataset):
    method __init__ (line 83) | def __init__(
    method __len__ (line 100) | def __len__(self):
    method __getitem__ (line 103) | def __getitem__(self, idx):
  function center_crop_arr (line 126) | def center_crop_arr(pil_image, image_size):
  function random_crop_arr (line 146) | def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_f...

FILE: models/guided_diffusion/logger.py
  class KVWriter (line 26) | class KVWriter(object):
    method writekvs (line 27) | def writekvs(self, kvs):
  class SeqWriter (line 31) | class SeqWriter(object):
    method writeseq (line 32) | def writeseq(self, seq):
  class HumanOutputFormat (line 36) | class HumanOutputFormat(KVWriter, SeqWriter):
    method __init__ (line 37) | def __init__(self, filename_or_file):
    method writekvs (line 48) | def writekvs(self, kvs):
    method _truncate (line 80) | def _truncate(self, s):
    method writeseq (line 84) | def writeseq(self, seq):
    method close (line 93) | def close(self):
  class JSONOutputFormat (line 98) | class JSONOutputFormat(KVWriter):
    method __init__ (line 99) | def __init__(self, filename):
    method writekvs (line 102) | def writekvs(self, kvs):
    method close (line 109) | def close(self):
  class CSVOutputFormat (line 113) | class CSVOutputFormat(KVWriter):
    method __init__ (line 114) | def __init__(self, filename):
    method writekvs (line 119) | def writekvs(self, kvs):
    method close (line 146) | def close(self):
  class TensorBoardOutputFormat (line 150) | class TensorBoardOutputFormat(KVWriter):
    method __init__ (line 155) | def __init__(self, dir):
    method writekvs (line 171) | def writekvs(self, kvs):
    method close (line 185) | def close(self):
  function make_output_format (line 191) | def make_output_format(format, ev_dir, log_suffix=""):
  function logkv (line 212) | def logkv(key, val):
  function logkv_mean (line 221) | def logkv_mean(key, val):
  function logkvs (line 228) | def logkvs(d):
  function dumpkvs (line 236) | def dumpkvs():
  function getkvs (line 243) | def getkvs():
  function log (line 247) | def log(*args, level=INFO):
  function debug (line 254) | def debug(*args):
  function info (line 258) | def info(*args):
  function warn (line 262) | def warn(*args):
  function error (line 266) | def error(*args):
  function set_level (line 270) | def set_level(level):
  function set_comm (line 277) | def set_comm(comm):
  function get_dir (line 281) | def get_dir():
  function profile_kv (line 294) | def profile_kv(scopename):
  function profile (line 303) | def profile(n):
  function get_current (line 325) | def get_current():
  class Logger (line 332) | class Logger(object):
    method __init__ (line 337) | def __init__(self, dir, output_formats, comm=None):
    method logkv (line 347) | def logkv(self, key, val):
    method logkv_mean (line 350) | def logkv_mean(self, key, val):
    method dumpkvs (line 355) | def dumpkvs(self):
    method log (line 376) | def log(self, *args, level=INFO):
    method set_level (line 382) | def set_level(self, level):
    method set_comm (line 385) | def set_comm(self, comm):
    method get_dir (line 388) | def get_dir(self):
    method close (line 391) | def close(self):
    method _do_log (line 397) | def _do_log(self, args):
  function get_rank_without_mpi_import (line 403) | def get_rank_without_mpi_import():
  function mpi_weighted_mean (line 412) | def mpi_weighted_mean(comm, local_name2valcount):
  function configure (line 442) | def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
  function _configure_default_logger (line 474) | def _configure_default_logger():
  function reset (line 479) | def reset():
  function scoped_configure (line 487) | def scoped_configure(dir=None, format_strs=None, comm=None):

FILE: models/guided_diffusion/losses.py
  function normal_kl (line 12) | def normal_kl(mean1, logvar1, mean2, logvar2):
  function approx_standard_normal_cdf (line 42) | def approx_standard_normal_cdf(x):
  function discretized_gaussian_log_likelihood (line 50) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):

FILE: models/guided_diffusion/nn.py
  class SiLU (line 12) | class SiLU(nn.Module):
    method forward (line 13) | def forward(self, x):
  class GroupNorm32 (line 17) | class GroupNorm32(nn.GroupNorm):
    method forward (line 18) | def forward(self, x):
  function conv_nd (line 22) | def conv_nd(dims, *args, **kwargs):
  function linear (line 35) | def linear(*args, **kwargs):
  function avg_pool_nd (line 42) | def avg_pool_nd(dims, *args, **kwargs):
  function update_ema (line 55) | def update_ema(target_params, source_params, rate=0.99):
  function zero_module (line 68) | def zero_module(module):
  function scale_module (line 77) | def scale_module(module, scale):
  function mean_flat (line 86) | def mean_flat(tensor):
  function normalization (line 93) | def normalization(channels):
  function timestep_embedding (line 103) | def timestep_embedding(timesteps, dim, max_period=10000):
  function checkpoint (line 124) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 142) | class CheckpointFunction(th.autograd.Function):
    method forward (line 144) | def forward(ctx, run_function, length, *args):
    method backward (line 153) | def backward(ctx, *output_grads):

FILE: models/guided_diffusion/resample.py
  function create_named_schedule_sampler (line 8) | def create_named_schedule_sampler(name, diffusion):
  class ScheduleSampler (line 23) | class ScheduleSampler(ABC):
    method weights (line 35) | def weights(self):
    method sample (line 42) | def sample(self, batch_size, device):
  class UniformSampler (line 61) | class UniformSampler(ScheduleSampler):
    method __init__ (line 62) | def __init__(self, diffusion):
    method weights (line 66) | def weights(self):
  class LossAwareSampler (line 70) | class LossAwareSampler(ScheduleSampler):
    method update_with_local_losses (line 71) | def update_with_local_losses(self, local_ts, local_losses):
    method update_with_all_losses (line 107) | def update_with_all_losses(self, ts, losses):
  class LossSecondMomentResampler (line 124) | class LossSecondMomentResampler(LossAwareSampler):
    method __init__ (line 125) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
    method weights (line 134) | def weights(self):
    method update_with_all_losses (line 143) | def update_with_all_losses(self, ts, losses):
    method _warmed_up (line 153) | def _warmed_up(self):

FILE: models/guided_diffusion/respace.py
  function space_timesteps (line 7) | def space_timesteps(num_timesteps, section_counts):
  class SpacedDiffusion (line 63) | class SpacedDiffusion(GaussianDiffusion):
    method __init__ (line 72) | def __init__(self, use_timesteps, **kwargs):
    method p_mean_variance (line 88) | def p_mean_variance(
    method training_losses (line 93) | def training_losses(
    method condition_mean (line 98) | def condition_mean(self, cond_fn, *args, **kwargs):
    method condition_score (line 101) | def condition_score(self, cond_fn, *args, **kwargs):
    method _wrap_model (line 104) | def _wrap_model(self, model):
    method _scale_timesteps (line 111) | def _scale_timesteps(self, t):
  class _WrappedModel (line 116) | class _WrappedModel:
    method __init__ (line 117) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu...
    method __call__ (line 123) | def __call__(self, x, ts, **kwargs):

FILE: models/guided_diffusion/script_util.py
  function diffusion_defaults (line 48) | def diffusion_defaults():
  function classifier_defaults (line 64) | def classifier_defaults():
  function model_and_diffusion_defaults (line 80) | def model_and_diffusion_defaults():
  function classifier_and_diffusion_defaults (line 107) | def classifier_and_diffusion_defaults():
  function create_model_and_diffusion (line 113) | def create_model_and_diffusion(
  function guided_Diffusion (line 173) | def guided_Diffusion(dataset_name = 'MetFACE'):
  function create_model (line 180) | def create_model(
  function create_classifier_and_diffusion (line 237) | def create_classifier_and_diffusion(
  function create_classifier (line 278) | def create_classifier(
  function sr_model_and_diffusion_defaults (line 319) | def sr_model_and_diffusion_defaults():
  function sr_create_model_and_diffusion (line 330) | def sr_create_model_and_diffusion(
  function sr_create_model (line 388) | def sr_create_model(
  function create_gaussian_diffusion (line 440) | def create_gaussian_diffusion(
  function add_dict_to_argparser (line 485) | def add_dict_to_argparser(parser, default_dict):
  function args_to_dict (line 495) | def args_to_dict(args, keys):
  function str2bool (line 499) | def str2bool(v):

FILE: models/guided_diffusion/train_util.py
  class TrainLoop (line 22) | class TrainLoop:
    method __init__ (line 23) | def __init__(
    method _load_and_sync_parameters (line 110) | def _load_and_sync_parameters(self):
    method _load_ema_parameters (line 125) | def _load_ema_parameters(self, rate):
    method _load_optimizer_state (line 141) | def _load_optimizer_state(self):
    method run_loop (line 153) | def run_loop(self):
    method run_step (line 172) | def run_step(self, batch, cond):
    method forward_backward (line 180) | def forward_backward(self, batch, cond):
    method _update_ema (line 216) | def _update_ema(self):
    method _anneal_lr (line 220) | def _anneal_lr(self):
    method log_step (line 228) | def log_step(self):
    method save (line 232) | def save(self):
  function parse_resume_step_from_filename (line 258) | def parse_resume_step_from_filename(filename):
  function get_blob_logdir (line 273) | def get_blob_logdir():
  function find_resume_checkpoint (line 279) | def find_resume_checkpoint():
  function find_ema_checkpoint (line 285) | def find_ema_checkpoint(main_checkpoint, step, rate):
  function log_loss_dict (line 295) | def log_loss_dict(diffusion, ts, losses):

FILE: models/guided_diffusion/unet.py
  function slerp (line 26) | def slerp(t,v0,v1):
  class AttentionPool2d (line 63) | class AttentionPool2d(nn.Module):
    method __init__ (line 68) | def __init__(
    method forward (line 84) | def forward(self, x):
  class TimestepBlock (line 95) | class TimestepBlock(nn.Module):
    method forward (line 101) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 107) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 113) | def forward(self, x, emb):
  class Upsample (line 122) | class Upsample(nn.Module):
    method __init__ (line 132) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 141) | def forward(self, x):
  class Downsample (line 154) | class Downsample(nn.Module):
    method __init__ (line 164) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 179) | def forward(self, x):
  class ResBlock (line 184) | class ResBlock(TimestepBlock):
    method __init__ (line 201) | def __init__(
    method forward (line 265) | def forward(self, x, emb):
    method _forward (line 277) | def _forward(self, x, emb):
  class AttentionBlock (line 300) | class AttentionBlock(nn.Module):
    method __init__ (line 308) | def __init__(
    method forward (line 337) | def forward(self, x):
    method _forward (line 340) | def _forward(self, x):
  function count_flops_attn (line 349) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 369) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 374) | def __init__(self, n_heads):
    method forward (line 378) | def forward(self, qkv):
    method count_flops (line 398) | def count_flops(model, _x, y):
  class QKVAttention (line 402) | class QKVAttention(nn.Module):
    method __init__ (line 407) | def __init__(self, n_heads):
    method forward (line 411) | def forward(self, qkv):
    method count_flops (line 433) | def count_flops(model, _x, y):
  class UNetModel (line 437) | class UNetModel(nn.Module):
    method __init__ (line 468) | def __init__(
    method convert_to_fp16 (line 659) | def convert_to_fp16(self):
    method convert_to_fp32 (line 667) | def convert_to_fp32(self):
    method forward (line 675) | def forward(self, x, timesteps, y=None, index=None, t_edit=400, hs_coe...
    method setattr_layers (line 759) | def setattr_layers(self, nums):
  class DeltaBlock (line 779) | class DeltaBlock(TimestepBlock):
    method __init__ (line 796) | def __init__(
    method forward (line 840) | def forward(self, x, emb=None):
  class SuperResModel (line 860) | class SuperResModel(UNetModel):
    method __init__ (line 867) | def __init__(self, image_size, in_channels, *args, **kwargs):
    method forward (line 870) | def forward(self, x, timesteps, low_res=None, **kwargs):
  class EncoderUNetModel (line 877) | class EncoderUNetModel(nn.Module):
    method __init__ (line 884) | def __init__(
    method convert_to_fp16 (line 1051) | def convert_to_fp16(self):
    method convert_to_fp32 (line 1058) | def convert_to_fp32(self):
    method forward (line 1065) | def forward(self, x, timesteps):

FILE: models/improved_ddpm/fp16_util.py
  function convert_module_to_f16 (line 15) | def convert_module_to_f16(l):
  function convert_module_to_f32 (line 25) | def convert_module_to_f32(l):
  function make_master_params (line 35) | def make_master_params(param_groups_and_shapes):
  function model_grads_to_master_grads (line 52) | def model_grads_to_master_grads(param_groups_and_shapes, master_params):
  function master_params_to_model_params (line 65) | def master_params_to_model_params(param_groups_and_shapes, master_params):
  function unflatten_master_params (line 78) | def unflatten_master_params(param_group, master_param):
  function get_param_groups_and_shapes (line 82) | def get_param_groups_and_shapes(named_model_params):
  function master_params_to_state_dict (line 95) | def master_params_to_state_dict(
  function state_dict_to_master_params (line 116) | def state_dict_to_master_params(model, state_dict, use_fp16):
  function zero_master_grads (line 128) | def zero_master_grads(master_params):
  function zero_grad (line 133) | def zero_grad(model_params):
  function param_grad_or_zeros (line 141) | def param_grad_or_zeros(param):
  class MixedPrecisionTrainer (line 148) | class MixedPrecisionTrainer:
    method __init__ (line 149) | def __init__(
    method zero_grad (line 173) | def zero_grad(self):
    method backward (line 176) | def backward(self, loss: th.Tensor):
    method optimize (line 183) | def optimize(self, opt: th.optim.Optimizer):
    method _optimize_fp16 (line 189) | def _optimize_fp16(self, opt: th.optim.Optimizer):
    method _optimize_normal (line 209) | def _optimize_normal(self, opt: th.optim.Optimizer):
    method _compute_norms (line 216) | def _compute_norms(self, grad_scale=1.0):
    method master_params_to_state_dict (line 226) | def master_params_to_state_dict(self, master_params):
    method state_dict_to_master_params (line 231) | def state_dict_to_master_params(self, state_dict):
  function check_overflow (line 235) | def check_overflow(value):

FILE: models/improved_ddpm/logger.py
  class KVWriter (line 25) | class KVWriter(object):
    method writekvs (line 26) | def writekvs(self, kvs):
  class SeqWriter (line 30) | class SeqWriter(object):
    method writeseq (line 31) | def writeseq(self, seq):
  class HumanOutputFormat (line 35) | class HumanOutputFormat(KVWriter, SeqWriter):
    method __init__ (line 36) | def __init__(self, filename_or_file):
    method writekvs (line 47) | def writekvs(self, kvs):
    method _truncate (line 79) | def _truncate(self, s):
    method writeseq (line 83) | def writeseq(self, seq):
    method close (line 92) | def close(self):
  class JSONOutputFormat (line 97) | class JSONOutputFormat(KVWriter):
    method __init__ (line 98) | def __init__(self, filename):
    method writekvs (line 101) | def writekvs(self, kvs):
    method close (line 108) | def close(self):
  class CSVOutputFormat (line 112) | class CSVOutputFormat(KVWriter):
    method __init__ (line 113) | def __init__(self, filename):
    method writekvs (line 118) | def writekvs(self, kvs):
    method close (line 145) | def close(self):
  function make_output_format (line 149) | def make_output_format(format, ev_dir, log_suffix=""):
  function logkv (line 168) | def logkv(key, val):
  function logkv_mean (line 177) | def logkv_mean(key, val):
  function logkvs (line 184) | def logkvs(d):
  function dumpkvs (line 192) | def dumpkvs():
  function getkvs (line 199) | def getkvs():
  function log (line 203) | def log(*args, level=INFO):
  function debug (line 210) | def debug(*args):
  function info (line 214) | def info(*args):
  function warn (line 218) | def warn(*args):
  function error (line 222) | def error(*args):
  function set_level (line 226) | def set_level(level):
  function set_comm (line 233) | def set_comm(comm):
  function get_dir (line 237) | def get_dir():
  function profile_kv (line 250) | def profile_kv(scopename):
  function profile (line 259) | def profile(n):
  function get_current (line 281) | def get_current():
  class Logger (line 288) | class Logger(object):
    method __init__ (line 293) | def __init__(self, dir, output_formats, comm=None):
    method logkv (line 303) | def logkv(self, key, val):
    method logkv_mean (line 306) | def logkv_mean(self, key, val):
    method dumpkvs (line 311) | def dumpkvs(self):
    method log (line 332) | def log(self, *args, level=INFO):
    method set_level (line 338) | def set_level(self, level):
    method set_comm (line 341) | def set_comm(self, comm):
    method get_dir (line 344) | def get_dir(self):
    method close (line 347) | def close(self):
    method _do_log (line 353) | def _do_log(self, args):
  function get_rank_without_mpi_import (line 359) | def get_rank_without_mpi_import():
  function mpi_weighted_mean (line 368) | def mpi_weighted_mean(comm, local_name2valcount):
  function configure (line 398) | def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
  function _configure_default_logger (line 430) | def _configure_default_logger():
  function reset (line 435) | def reset():
  function scoped_configure (line 443) | def scoped_configure(dir=None, format_strs=None, comm=None):

FILE: models/improved_ddpm/nn.py
  class SiLU (line 12) | class SiLU(nn.Module):
    method forward (line 13) | def forward(self, x):
  class GroupNorm32 (line 17) | class GroupNorm32(nn.GroupNorm):
    method forward (line 18) | def forward(self, x):
  function conv_nd (line 22) | def conv_nd(dims, *args, **kwargs):
  function linear (line 35) | def linear(*args, **kwargs):
  function avg_pool_nd (line 42) | def avg_pool_nd(dims, *args, **kwargs):
  function update_ema (line 55) | def update_ema(target_params, source_params, rate=0.99):
  function zero_module (line 68) | def zero_module(module):
  function scale_module (line 77) | def scale_module(module, scale):
  function mean_flat (line 86) | def mean_flat(tensor):
  function normalization (line 93) | def normalization(channels):
  function timestep_embedding (line 103) | def timestep_embedding(timesteps, dim, max_period=10000):
  function checkpoint (line 124) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 142) | class CheckpointFunction(th.autograd.Function):
    method forward (line 144) | def forward(ctx, run_function, length, *args):
    method backward (line 153) | def backward(ctx, *output_grads):

FILE: models/improved_ddpm/script_util.py
  function create_model (line 45) | def create_model(
  function i_DDPM (line 102) | def i_DDPM(dataset_name = 'AFHQ'):

FILE: models/improved_ddpm/unet.py
  function slerp (line 26) | def slerp(t,v0,v1):
  class AttentionPool2d (line 64) | class AttentionPool2d(nn.Module):
    method __init__ (line 69) | def __init__(
    method forward (line 85) | def forward(self, x):
  class TimestepBlock (line 96) | class TimestepBlock(nn.Module):
    method forward (line 102) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 108) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 114) | def forward(self, x, emb):
  class Upsample (line 123) | class Upsample(nn.Module):
    method __init__ (line 133) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 142) | def forward(self, x):
  class Downsample (line 155) | class Downsample(nn.Module):
    method __init__ (line 165) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
    method forward (line 180) | def forward(self, x):
  class ResBlock (line 185) | class ResBlock(TimestepBlock):
    method __init__ (line 202) | def __init__(
    method forward (line 266) | def forward(self, x, emb):
    method _forward (line 278) | def _forward(self, x, emb):
  class AttentionBlock (line 301) | class AttentionBlock(nn.Module):
    method __init__ (line 309) | def __init__(
    method forward (line 338) | def forward(self, x):
    method _forward (line 341) | def _forward(self, x):
  function count_flops_attn (line 350) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 370) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 375) | def __init__(self, n_heads):
    method forward (line 379) | def forward(self, qkv):
    method count_flops (line 399) | def count_flops(model, _x, y):
  class QKVAttention (line 403) | class QKVAttention(nn.Module):
    method __init__ (line 408) | def __init__(self, n_heads):
    method forward (line 412) | def forward(self, qkv):
    method count_flops (line 434) | def count_flops(model, _x, y):
  class UNetModel (line 438) | class UNetModel(nn.Module):
    method __init__ (line 469) | def __init__(
    method convert_to_fp16 (line 660) | def convert_to_fp16(self):
    method convert_to_fp32 (line 668) | def convert_to_fp32(self):
    method forward (line 676) | def forward(self, x, timesteps, y=None, index=None, t_edit=400, hs_coe...
    method setattr_layers (line 756) | def setattr_layers(self, nums):
  class DeltaBlock (line 776) | class DeltaBlock(TimestepBlock):
    method __init__ (line 793) | def __init__(
    method forward (line 837) | def forward(self, x, emb=None):

FILE: models/insight_face/helpers.py
  class Conv_block (line 12) | class Conv_block(Module):
    method __init__ (line 13) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 18) | def forward(self, x):
  class Linear_block (line 24) | class Linear_block(Module):
    method __init__ (line 25) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 29) | def forward(self, x):
  class Depth_Wise (line 34) | class Depth_Wise(Module):
    method __init__ (line 35) | def __init__(self, in_c, out_c, residual = False, kernel=(3, 3), strid...
    method forward (line 41) | def forward(self, x):
  class Residual (line 53) | class Residual(Module):
    method __init__ (line 54) | def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1),...
    method forward (line 60) | def forward(self, x):
  class Flatten (line 69) | class Flatten(Module):
    method forward (line 70) | def forward(self, input):
  function l2_norm (line 74) | def l2_norm(input, axis=1):
  class Bottleneck (line 80) | class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
  function get_block (line 84) | def get_block(in_channel, depth, num_units, stride=2):
  function get_blocks (line 88) | def get_blocks(num_layers):
  class SEModule (line 115) | class SEModule(Module):
    method __init__ (line 116) | def __init__(self, channels, reduction):
    method forward (line 124) | def forward(self, x):
  class bottleneck_IR (line 134) | class bottleneck_IR(Module):
    method __init__ (line 135) | def __init__(self, in_channel, depth, stride):
    method forward (line 150) | def forward(self, x):
  class bottleneck_IR_SE (line 156) | class bottleneck_IR_SE(Module):
    method __init__ (line 157) | def __init__(self, in_channel, depth, stride):
    method forward (line 175) | def forward(self, x):

FILE: models/insight_face/model_irse.py
  class MobileFaceNet (line 9) | class MobileFaceNet(Module):
    method __init__ (line 10) | def __init__(self, embedding_size):
    method forward (line 26) | def forward(self, x):
  class Backbone (line 49) | class Backbone(Module):
    method __init__ (line 50) | def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, ...
    method forward (line 84) | def forward(self, x):
  function IR_50 (line 91) | def IR_50(input_size):
  function IR_101 (line 97) | def IR_101(input_size):
  function IR_152 (line 103) | def IR_152(input_size):
  function IR_SE_50 (line 109) | def IR_SE_50(input_size):
  function IR_SE_101 (line 115) | def IR_SE_101(input_size):
  function IR_SE_152 (line 121) | def IR_SE_152(input_size):

FILE: utils/align_utils.py
  function run_alignment (line 33) | def run_alignment(image_path, output_size):
  function get_landmark (line 45) | def get_landmark(filepath, predictor):
  function align_face (line 65) | def align_face(filepath, predictor, output_size=256, transform_size=256):
  function chunks (line 151) | def chunks(lst, n):
  function extract_on_paths (line 157) | def extract_on_paths(file_paths):
  function parse_args (line 177) | def parse_args():
  function run (line 185) | def run(args):

FILE: utils/colab_utils.py
  class GoogleDrive_Dowonloader (line 8) | class GoogleDrive_Dowonloader(object):
    method __init__ (line 9) | def __init__(self, use_pydrive):
    method authenticate (line 15) | def authenticate(self):
    method ensure_file_exists (line 21) | def ensure_file_exists(self, file_id, file_dst):

FILE: utils/diffusion_utils.py
  function get_beta_schedule (line 5) | def get_beta_schedule(*, beta_start, beta_end, num_diffusion_timesteps):
  function extract (line 12) | def extract(a, t, x_shape):
  function denoising_step (line 24) | def denoising_step(xt, t, t_next, *,

FILE: utils/prepare_lmdb_data.py
  function resize_and_convert (line 17) | def resize_and_convert(img, size, resample, quality=100):
  function resize_multiple (line 27) | def resize_multiple(
  function resize_worker (line 38) | def resize_worker(img_file, sizes, resample):
  function prepare (line 47) | def prepare(