SYMBOL INDEX (1328 symbols across 97 files)

FILE: app.py
  class UploadTarget (line 32) | class UploadTarget(enum.Enum):
  function concatenate_images (line 38) | def concatenate_images(images):
  function select_function (line 50) | def select_function(evt: gr.SelectData):
  function select_function_multi (line 56) | def select_function_multi(evt: gr.SelectData):
  function get_selected_image (line 61) | def get_selected_image(state_image_list, evt: gr.SelectData):
  function upload_file (line 64) | def upload_file(files, current_files):
  function update_prompt (line 69) | def update_prompt(style_model, style_choice, uuid):
  function update_pose_model (line 92) | def update_pose_model(pose_image, pose_model):
  function generate_pos_prompt (line 103) | def generate_pos_prompt(style_model, prompt_cloth):
  function launch_pipeline (line 118) | def launch_pipeline(uuid,
  function launch_pipeline_inpaint (line 190) | def launch_pipeline_inpaint(uuid,
  function update_lora_choice (line 238) | def update_lora_choice(uuid):
  function upload_lora_file (line 254) | def upload_lora_file(uuid, lora_file):
  function clear_lora_file (line 275) | def clear_lora_file(uuid, lora_file):
  function change_lora_choice (line 285) | def change_lora_choice(lora_choice):
  function change_style_choice (line 294) | def change_style_choice(uuid, style_choice):
  function select_trained_style (line 311) | def select_trained_style(trained_styles):
  function get_tag (line 314) | def get_tag(imgs):
  function modify_tag (line 326) | def modify_tag(gallery, impath, tag):
  function inference_input (line 335) | def inference_input():
  function inference_inpaint (line 450) | def inference_inpaint():
  function train_input (line 506) | def train_input():

FILE: face_adapter/face_adapter_v1.py
  function detect (line 28) | def detect(image, face_detection):
  function get_mask_head (line 37) | def get_mask_head(result):
  function align (line 73) | def align(image, points_vec):
  function face_image_preprocess (line 77) | def face_image_preprocess(image, segmentation_pipeline, face_detection):
  class Face_Transformer (line 85) | class Face_Transformer(nn.Module):
    method __init__ (line 86) | def __init__(self, name='vits', weight='./ms1mv2_model_TransFace_S.pt'):
    method forward (line 105) | def forward(self, img):
  function FeedForward (line 111) | def FeedForward(dim, mult=4):
  function reshape_tensor (line 121) | def reshape_tensor(x, heads):
  class PerceiverAttention (line 132) | class PerceiverAttention(nn.Module):
    method __init__ (line 133) | def __init__(self, *, dim, dim_head=64, heads=8):
    method forward (line 148) | def forward(self, x, latents):
  class Face_Prj_Resampler (line 180) | class Face_Prj_Resampler(nn.Module):
    method __init__ (line 181) | def __init__(
    method forward (line 212) | def forward(self, x):
  class Face_Extracter_v1 (line 228) | class Face_Extracter_v1(nn.Module):
    method __init__ (line 229) | def __init__(self, fr_weight_path, fc_weight_path):
    method forward (line 241) | def forward(self, face_img):
  class Identity (line 253) | class Identity(nn.Module):
    method __init__ (line 254) | def __init__(self):
    method forward (line 257) | def forward(self, x):
  class FaceAdapter_v1 (line 261) | class FaceAdapter_v1:
    method __init__ (line 263) | def __init__(self, sd_pipe, face_detection, segmentation_pipeline, fac...
    method set_adapter (line 284) | def set_adapter(self):
    method load_adapter (line 329) | def load_adapter(self):
    method set_scale (line 339) | def set_scale(self, scale):
    method set_num_ims (line 345) | def set_num_ims(self, num_ims):
    method generate (line 360) | def generate(

FILE: face_adapter/face_attention_processor_v1.py
  function exists (line 7) | def exists(val):
  function uniq (line 11) | def uniq(arr):
  function default (line 15) | def default(val, d):
  class GEGLU (line 20) | class GEGLU(nn.Module):
    method __init__ (line 21) | def __init__(self, dim_in, dim_out):
    method forward (line 25) | def forward(self, x):
  class FeedForward (line 29) | class FeedForward(nn.Module):
    method __init__ (line 30) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 45) | def forward(self, x):
  class SelfAttention (line 74) | class SelfAttention(nn.Module):
    method __init__ (line 75) | def __init__(self, query_dim, inner_dim, dropout=0.):
    method forward (line 87) | def forward(self, x, attn):
  class AttnProcessor (line 110) | class AttnProcessor(nn.Module):
    method __init__ (line 114) | def __init__(
    method __call__ (line 121) | def __call__(
  class AttnProcessor2_0 (line 181) | class AttnProcessor2_0(nn.Module):
    method __init__ (line 185) | def __init__(
    method __call__ (line 194) | def __call__(
  class FaceAttnProcessor (line 266) | class FaceAttnProcessor(nn.Module):
    method __init__ (line 270) | def __init__(
    method __call__ (line 299) | def __call__(
  class FaceAttnProcessor2_0 (line 398) | class FaceAttnProcessor2_0(nn.Module):
    method __init__ (line 402) | def __init__(
    method __call__ (line 433) | def __call__(
  class CNAttnProcessor2_0 (line 546) | class CNAttnProcessor2_0:
    method __init__ (line 551) | def __init__(self):
    method __call__ (line 557) | def __call__(
  class CNAttnProcessor (line 633) | class CNAttnProcessor:
    method __init__ (line 638) | def __init__(self):
    method __call__ (line 642) | def __call__(

FILE: face_adapter/face_preprocess.py
  function read_image (line 5) | def read_image(img_path, **kwargs):
  function preprocess (line 20) | def preprocess(img, bbox=None, landmark=None, **kwargs):
  function preprocess_3pt (line 88) | def preprocess_3pt(img, bbox=None, landmark=None, **kwargs):

FILE: face_adapter/utils.py
  function is_torch2_available (line 3) | def is_torch2_available():

FILE: face_adapter/vit.py
  class Mlp (line 7) | class Mlp(nn.Module):
    method __init__ (line 8) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 17) | def forward(self, x):
  class VITBatchNorm (line 26) | class VITBatchNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, num_features):
    method forward (line 32) | def forward(self, x):
  class Attention (line 36) | class Attention(nn.Module):
    method __init__ (line 37) | def __init__(self,
    method forward (line 55) | def forward(self, x):
  class Block (line 74) | class Block(nn.Module):
    method __init__ (line 76) | def __init__(self,
    method forward (line 108) | def forward(self, x):
  class PatchEmbed (line 115) | class PatchEmbed(nn.Module):
    method __init__ (line 116) | def __init__(self, img_size=108, patch_size=9, in_channels=3, embed_di...
    method forward (line 128) | def forward(self, x):
  class VisionTransformer (line 137) | class VisionTransformer(nn.Module):
    method __init__ (line 141) | def __init__(self,
    method _init_weights (line 219) | def _init_weights(self, m):
    method no_weight_decay (line 229) | def no_weight_decay(self):
    method get_classifier (line 232) | def get_classifier(self):
    method random_masking (line 235) | def random_masking(self, x, mask_ratio=0.1):
    method forward_features (line 264) | def forward_features(self, x):
    method forward (line 298) | def forward(self, x):

FILE: face_module/TopoFR/GUM.py
  function gauss_unif (line 8) | def gauss_unif(x):

FILE: face_module/TopoFR/backbones/__init__.py
  function get_model (line 5) | def get_model(name, **kwargs):

FILE: face_module/TopoFR/backbones/iresnet.py
  function conv3x3 (line 9) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 21) | def conv1x1(in_planes, out_planes, stride=1):
  class IBasicBlock (line 30) | class IBasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, downsample=None,
    method forward_impl (line 48) | def forward_impl(self, x):
    method forward (line 61) | def forward(self, x):
  class IResNet (line 68) | class IResNet(nn.Module):
    method __init__ (line 70) | def __init__(self,
    method _make_layer (line 128) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 154) | def forward(self, x, phase='train'):
  function _iresnet (line 181) | def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
  function iresnet18 (line 188) | def iresnet18(pretrained=False, progress=True, **kwargs):
  function iresnet34 (line 193) | def iresnet34(pretrained=False, progress=True, **kwargs):
  function iresnet50 (line 198) | def iresnet50(pretrained=False, progress=True, **kwargs):
  function iresnet100 (line 203) | def iresnet100(pretrained=False, progress=True, **kwargs):
  function iresnet200 (line 208) | def iresnet200(pretrained=False, progress=True, **kwargs):

FILE: face_module/TopoFR/backbones/iresnet2060.py
  function conv3x3 (line 10) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 22) | def conv1x1(in_planes, out_planes, stride=1):
  class IBasicBlock (line 31) | class IBasicBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, inplanes, planes, stride=1, downsample=None,
    method forward (line 50) | def forward(self, x):
  class IResNet (line 64) | class IResNet(nn.Module):
    method __init__ (line 67) | def __init__(self,
    method _make_layer (line 119) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method checkpoint (line 145) | def checkpoint(self, func, num_seg, x):
    method forward (line 151) | def forward(self, x):
  function _iresnet (line 168) | def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
  function iresnet2060 (line 175) | def iresnet2060(pretrained=False, progress=True, **kwargs):

FILE: face_module/TopoFR/backbones/mobilefacenet.py
  class Flatten (line 11) | class Flatten(Module):
    method forward (line 12) | def forward(self, x):
  class ConvBlock (line 16) | class ConvBlock(Module):
    method __init__ (line 17) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 25) | def forward(self, x):
  class LinearBlock (line 29) | class LinearBlock(Module):
    method __init__ (line 30) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 37) | def forward(self, x):
  class DepthWise (line 41) | class DepthWise(Module):
    method __init__ (line 42) | def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=...
    method forward (line 51) | def forward(self, x):
  class Residual (line 63) | class Residual(Module):
    method __init__ (line 64) | def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1),...
    method forward (line 71) | def forward(self, x):
  class GDC (line 75) | class GDC(Module):
    method __init__ (line 76) | def __init__(self, embedding_size):
    method forward (line 84) | def forward(self, x):
  class MobileFaceNet (line 88) | class MobileFaceNet(Module):
    method __init__ (line 89) | def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), ...
    method _initialize_weights (line 120) | def _initialize_weights(self):
    method forward (line 134) | def forward(self, x):
  function get_mbf (line 143) | def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
  function get_mbf_large (line 146) | def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):

FILE: face_module/TopoFR/dataset.py
  function get_dataloader (line 19) | def get_dataloader(
  class BackgroundGenerator (line 79) | class BackgroundGenerator(threading.Thread):
    method __init__ (line 80) | def __init__(self, generator, local_rank, max_prefetch=6):
    method run (line 88) | def run(self):
    method next (line 94) | def next(self):
    method __next__ (line 100) | def __next__(self):
    method __iter__ (line 103) | def __iter__(self):
  class DataLoaderX (line 107) | class DataLoaderX(DataLoader):
    method __init__ (line 109) | def __init__(self, local_rank, **kwargs):
    method __iter__ (line 114) | def __iter__(self):
    method preload (line 120) | def preload(self):
    method __next__ (line 128) | def __next__(self):
  class MXFaceDataset (line 137) | class MXFaceDataset(Dataset):         ##
    method __init__ (line 138) | def __init__(self, root_dir, local_rank):
    method __getitem__ (line 159) | def __getitem__(self, index):
    method __len__ (line 172) | def __len__(self):
  class SyntheticDataset (line 176) | class SyntheticDataset(Dataset):
    method __init__ (line 177) | def __init__(self):
    method __getitem__ (line 186) | def __getitem__(self, index):
    method __len__ (line 189) | def __len__(self):
  function dali_data_iter (line 193) | def dali_data_iter(
  class DALIWarper (line 231) | class DALIWarper(object):
    method __init__ (line 232) | def __init__(self, dali_iter):
    method __next__ (line 235) | def __next__(self):
    method __iter__ (line 242) | def __iter__(self):
    method reset (line 245) | def reset(self):

FILE: face_module/TopoFR/eval/verification.py
  class LFold (line 41) | class LFold:
    method __init__ (line 42) | def __init__(self, n_splits=2, shuffle=False):
    method split (line 47) | def split(self, indices):
  function calculate_roc (line 54) | def calculate_roc(thresholds,
  function calculate_accuracy (line 109) | def calculate_accuracy(threshold, dist, actual_issame):
  function calculate_val (line 124) | def calculate_val(thresholds,
  function calculate_val_far (line 165) | def calculate_val_far(threshold, dist, actual_issame):
  function evaluate (line 179) | def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
  function load_bin (line 200) | def load_bin(path, image_size):
  function test (line 227) | def test(data_set, backbone, batch_size, nfolds=10):
  function dumpR (line 278) | def dumpR(data_set,

FILE: face_module/TopoFR/eval_ijbc_glint360k.py
  class Embedding (line 56) | class Embedding(object):
    method __init__ (line 57) | def __init__(self, prefix, data_shape, batch_size=1):
    method get (line 79) | def get(self, rimg, landmark):
    method forward_db (line 108) | def forward_db(self, batch_data):
  function divideIntoNstrand (line 118) | def divideIntoNstrand(listTemp, n):
  function read_template_media_list (line 125) | def read_template_media_list(path):
  function read_template_pair_list (line 136) | def read_template_pair_list(path):
  function read_image_feature (line 150) | def read_image_feature(path):
  function get_image_feature (line 159) | def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
  function image2template_feature (line 217) | def image2template_feature(img_feats=None, templates=None, medias=None):
  function verification (line 257) | def verification(template_norm_feats=None,
  function verification2 (line 287) | def verification2(template_norm_feats=None,
  function read_score (line 311) | def read_score(path):

FILE: face_module/TopoFR/eval_ijbc_ms1mv2.py
  class Embedding (line 56) | class Embedding(object):
    method __init__ (line 57) | def __init__(self, prefix, data_shape, batch_size=1):
    method get (line 78) | def get(self, rimg, landmark):
    method forward_db (line 107) | def forward_db(self, batch_data):
  function divideIntoNstrand (line 117) | def divideIntoNstrand(listTemp, n):
  function read_template_media_list (line 124) | def read_template_media_list(path):
  function read_template_pair_list (line 135) | def read_template_pair_list(path):
  function read_image_feature (line 149) | def read_image_feature(path):
  function get_image_feature (line 158) | def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
  function image2template_feature (line 216) | def image2template_feature(img_feats=None, templates=None, medias=None):
  function verification (line 256) | def verification(template_norm_feats=None,
  function verification2 (line 286) | def verification2(template_norm_feats=None,
  function read_score (line 310) | def read_score(path):

FILE: face_module/TopoFR/inference.py
  function inference (line 11) | def inference(weight, name, img):

FILE: face_module/TopoFR/losses.py
  class CombinedMarginLoss (line 5) | class CombinedMarginLoss(torch.nn.Module):
    method __init__ (line 6) | def __init__(self,
    method forward (line 27) | def forward(self, logits, labels):
  class ArcFace (line 60) | class ArcFace(torch.nn.Module):
    method __init__ (line 63) | def __init__(self, s=64.0, margin=0.5):
    method forward (line 73) | def forward(self, logits: torch.Tensor, labels: torch.Tensor):
  class CosFace (line 98) | class CosFace(torch.nn.Module):
    method __init__ (line 99) | def __init__(self, s=64.0, m=0.40):
    method forward (line 104) | def forward(self, logits: torch.Tensor, labels: torch.Tensor):

FILE: face_module/TopoFR/lr_scheduler.py
  class PolyScheduler (line 4) | class PolyScheduler(_LRScheduler):
    method __init__ (line 5) | def __init__(self, optimizer, base_lr, max_steps, warmup_steps, last_e...
    method get_warmup_lr (line 14) | def get_warmup_lr(self):
    method get_lr (line 18) | def get_lr(self):

FILE: face_module/TopoFR/onnx_helper.py
  class ArcFaceORT (line 15) | class ArcFaceORT:
    method __init__ (line 16) | def __init__(self, model_path, cpu=False):
    method check (line 22) | def check(self, track='cfat', test_img = None):
    method check_batch (line 184) | def check_batch(self, img):
    method meta_info (line 202) | def meta_info(self):
    method forward (line 206) | def forward(self, imgs):
    method benchmark (line 222) | def benchmark(self, img):

FILE: face_module/TopoFR/onnx_ijbc.py
  class AlignedDataSet (line 30) | class AlignedDataSet(mx.gluon.data.Dataset):
    method __init__ (line 31) | def __init__(self, root, lines, align=True):
    method __len__ (line 36) | def __len__(self):
    method __getitem__ (line 39) | def __getitem__(self, idx):
  function extract (line 56) | def extract(model_root, dataset):
  function read_template_media_list (line 79) | def read_template_media_list(path):
  function read_template_pair_list (line 86) | def read_template_pair_list(path):
  function read_image_feature (line 94) | def read_image_feature(path):
  function image2template_feature (line 100) | def image2template_feature(img_feats=None,
  function verification (line 126) | def verification(template_norm_feats=None,
  function verification2 (line 148) | def verification2(template_norm_feats=None,
  function main (line 170) | def main(args):

FILE: face_module/TopoFR/partial_fc.py
  class PartialFC (line 12) | class PartialFC(torch.nn.Module):
    method __init__ (line 35) | def __init__(
    method sample (line 100) | def sample(self,
    method update (line 142) | def update(self):
    method forward (line 154) | def forward(
    method state_dict (line 220) | def state_dict(self, destination=None, prefix="", keep_vars=False):
    method load_state_dict (line 234) | def load_state_dict(self, state_dict, strict: bool = True):
  class PartialFCAdamW (line 245) | class PartialFCAdamW(torch.nn.Module):     # Adam
    method __init__ (line 246) | def __init__(self,
    method sample (line 316) | def sample(self, labels, index_positive, optimizer):   #
    method update (line 343) | def update(self):
    method forward (line 355) | def forward(
    method state_dict (line 421) | def state_dict(self, destination=None, prefix="", keep_vars=False):
    method load_state_dict (line 435) | def load_state_dict(self, state_dict, strict: bool = True):
  class DistCrossEntropyFunc (line 447) | class DistCrossEntropyFunc(torch.autograd.Function):
    method forward (line 454) | def forward(ctx, logits: torch.Tensor, label: torch.Tensor):
    method backward (line 476) | def backward(ctx, loss_gradient):
  class DistCrossEntropy (line 499) | class DistCrossEntropy(torch.nn.Module):
    method __init__ (line 500) | def __init__(self):
    method forward (line 503) | def forward(self, logit_part, label_part):
  class AllGatherFunc (line 506) | class AllGatherFunc(torch.autograd.Function):
    method forward (line 510) | def forward(ctx, tensor, *gather_list):
    method backward (line 516) | def backward(ctx, *grads):
  function Entropy (line 538) | def Entropy(input_):

FILE: face_module/TopoFR/persistent_homology.py
  function compute_distance_matrix (line 13) | def compute_distance_matrix(x, p=2):
  function compute_topological_loss (line 18) | def compute_topological_loss(input_space, feature_space, use_grad=False):
  class TopologicalSignatureDistance (line 46) | class TopologicalSignatureDistance(nn.Module):
    method __init__ (line 49) | def __init__(self, sort_selected=False, use_cycles=False,
    method _get_pairings (line 64) | def _get_pairings(self, distances):
    method _select_distances_from_pairs (line 70) | def _select_distances_from_pairs(self, distance_matrix, pairs):
    method sig_error (line 86) | def sig_error(signature1, signature2):
    method _count_matching_pairs (line 91) | def _count_matching_pairs(pairs1, pairs2):
    method _get_nonzero_cycles (line 97) | def _get_nonzero_cycles(pairs):
    method forward (line 102) | def forward(self, distances1, distances2):

FILE: face_module/TopoFR/topology.py
  class UnionFind (line 8) | class UnionFind:
    method __init__ (line 15) | def __init__(self, n_vertices):
    method find (line 23) | def find(self, u):
    method merge (line 35) | def merge(self, u, v):
    method roots (line 44) | def roots(self):
  class PersistentHomologyCalculation (line 55) | class PersistentHomologyCalculation:
    method __call__ (line 56) | def __call__(self, matrix):
  class AlephPersistenHomologyCalculation (line 95) | class AlephPersistenHomologyCalculation():
    method __init__ (line 96) | def __init__(self, compute_cycles, sort_selected):
    method __call__ (line 108) | def __call__(self, distance_matrix):

FILE: face_module/TopoFR/torch2onnx.py
  function convert_onnx (line 6) | def convert_onnx(net, path_module, output, opset=11, simplify=False):

FILE: face_module/TopoFR/train.py
  function Entropy (line 48) | def Entropy(input_):
  function main (line 55) | def main(args):

FILE: face_module/TopoFR/utils/plot.py
  function read_template_pair_list (line 18) | def read_template_pair_list(path):

FILE: face_module/TopoFR/utils/utils_callbacks.py
  class CallBackVerification (line 14) | class CallBackVerification(object):
    method __init__ (line 16) | def __init__(self, val_targets, rec_prefix, summary_writer=None, image...
    method ver_test (line 27) | def ver_test(self, backbone: torch.nn.Module, global_step: int):
    method init_dataset (line 44) | def init_dataset(self, val_targets, data_dir, image_size):
    method __call__ (line 52) | def __call__(self, num_update, backbone: torch.nn.Module):
  class CallBackLogging (line 59) | class CallBackLogging(object):
    method __init__ (line 60) | def __init__(self, frequent, total_step, batch_size, start_step=0,writ...
    method __call__ (line 73) | def __call__(self,

FILE: face_module/TopoFR/utils/utils_config.py
  function get_config (line 5) | def get_config(config_file):

FILE: face_module/TopoFR/utils/utils_distributed_sampler.py
  function setup_seed (line 11) | def setup_seed(seed, cuda_deterministic=True):
  function worker_init_fn (line 25) | def worker_init_fn(worker_id, num_workers, rank, seed):
  function get_dist_info (line 34) | def get_dist_info():
  function sync_random_seed (line 45) | def sync_random_seed(seed=None, device="cuda"):
  class DistributedSampler (line 82) | class DistributedSampler(_DistributedSampler):
    method __init__ (line 83) | def __init__(
    method __iter__ (line 102) | def __iter__(self):

FILE: face_module/TopoFR/utils/utils_logging.py
  class AverageMeter (line 6) | class AverageMeter(object):
    method __init__ (line 10) | def __init__(self):
    method reset (line 17) | def reset(self):
    method update (line 23) | def update(self, val, n=1):
  function init_logging (line 30) | def init_logging(rank, models_root):

FILE: face_module/TransFace/FFT.py
  function amplitude_spectrum_mix (line 5) | def amplitude_spectrum_mix(img1, img2, alpha, ratio=1.0):   #img_src, im...

FILE: face_module/TransFace/backbones/__init__.py
  function get_model (line 5) | def get_model(name, **kwargs):

FILE: face_module/TransFace/backbones/iresnet.py
  function conv3x3 (line 8) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 20) | def conv1x1(in_planes, out_planes, stride=1):
  class IBasicBlock (line 29) | class IBasicBlock(nn.Module):
    method __init__ (line 31) | def __init__(self, inplanes, planes, stride=1, downsample=None,
    method forward_impl (line 47) | def forward_impl(self, x):
    method forward (line 60) | def forward(self, x):
  class IResNet (line 67) | class IResNet(nn.Module):
    method __init__ (line 69) | def __init__(self,
    method _make_layer (line 122) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method forward (line 148) | def forward(self, x):
  function _iresnet (line 165) | def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
  function iresnet18 (line 172) | def iresnet18(pretrained=False, progress=True, **kwargs):
  function iresnet34 (line 177) | def iresnet34(pretrained=False, progress=True, **kwargs):
  function iresnet50 (line 182) | def iresnet50(pretrained=False, progress=True, **kwargs):
  function iresnet100 (line 187) | def iresnet100(pretrained=False, progress=True, **kwargs):
  function iresnet200 (line 192) | def iresnet200(pretrained=False, progress=True, **kwargs):

FILE: face_module/TransFace/backbones/iresnet2060.py
  function conv3x3 (line 10) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 22) | def conv1x1(in_planes, out_planes, stride=1):
  class IBasicBlock (line 31) | class IBasicBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, inplanes, planes, stride=1, downsample=None,
    method forward (line 50) | def forward(self, x):
  class IResNet (line 64) | class IResNet(nn.Module):
    method __init__ (line 67) | def __init__(self,
    method _make_layer (line 119) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method checkpoint (line 145) | def checkpoint(self, func, num_seg, x):
    method forward (line 151) | def forward(self, x):
  function _iresnet (line 168) | def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
  function iresnet2060 (line 175) | def iresnet2060(pretrained=False, progress=True, **kwargs):

FILE: face_module/TransFace/backbones/mobilefacenet.py
  class Flatten (line 11) | class Flatten(Module):
    method forward (line 12) | def forward(self, x):
  class ConvBlock (line 16) | class ConvBlock(Module):
    method __init__ (line 17) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 25) | def forward(self, x):
  class LinearBlock (line 29) | class LinearBlock(Module):
    method __init__ (line 30) | def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=...
    method forward (line 37) | def forward(self, x):
  class DepthWise (line 41) | class DepthWise(Module):
    method __init__ (line 42) | def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=...
    method forward (line 51) | def forward(self, x):
  class Residual (line 63) | class Residual(Module):
    method __init__ (line 64) | def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1),...
    method forward (line 71) | def forward(self, x):
  class GDC (line 75) | class GDC(Module):
    method __init__ (line 76) | def __init__(self, embedding_size):
    method forward (line 84) | def forward(self, x):
  class MobileFaceNet (line 88) | class MobileFaceNet(Module):
    method __init__ (line 89) | def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), ...
    method _initialize_weights (line 120) | def _initialize_weights(self):
    method forward (line 134) | def forward(self, x):
  function get_mbf (line 143) | def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
  function get_mbf_large (line 146) | def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):

FILE: face_module/TransFace/backbones/vit.py
  class Mlp (line 7) | class Mlp(nn.Module):
    method __init__ (line 8) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 17) | def forward(self, x):
  class VITBatchNorm (line 26) | class VITBatchNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, num_features):
    method forward (line 32) | def forward(self, x):
  class Attention (line 36) | class Attention(nn.Module):
    method __init__ (line 37) | def __init__(self,
    method forward (line 55) | def forward(self, x):
  class Block (line 74) | class Block(nn.Module):
    method __init__ (line 76) | def __init__(self,
    method forward (line 108) | def forward(self, x):
  class PatchEmbed (line 115) | class PatchEmbed(nn.Module):
    method __init__ (line 116) | def __init__(self, img_size=108, patch_size=9, in_channels=3, embed_di...
    method forward (line 128) | def forward(self, x):
  class VisionTransformer (line 137) | class VisionTransformer(nn.Module):
    method __init__ (line 141) | def __init__(self,
    method _init_weights (line 219) | def _init_weights(self, m):
    method no_weight_decay (line 229) | def no_weight_decay(self):
    method get_classifier (line 232) | def get_classifier(self):
    method random_masking (line 235) | def random_masking(self, x, mask_ratio=0.1):
    method forward_features (line 264) | def forward_features(self, x):
    method forward (line 298) | def forward(self, x):

FILE: face_module/TransFace/dataset.py
  function get_dataloader (line 19) | def get_dataloader(
  class BackgroundGenerator (line 78) | class BackgroundGenerator(threading.Thread):
    method __init__ (line 79) | def __init__(self, generator, local_rank, max_prefetch=6):
    method run (line 87) | def run(self):
    method next (line 93) | def next(self):
    method __next__ (line 99) | def __next__(self):
    method __iter__ (line 102) | def __iter__(self):
  class DataLoaderX (line 106) | class DataLoaderX(DataLoader):
    method __init__ (line 108) | def __init__(self, local_rank, **kwargs):
    method __iter__ (line 113) | def __iter__(self):
    method preload (line 119) | def preload(self):
    method __next__ (line 127) | def __next__(self):
  class MXFaceDataset (line 136) | class MXFaceDataset(Dataset):
    method __init__ (line 137) | def __init__(self, root_dir, local_rank):
    method __getitem__ (line 158) | def __getitem__(self, index):
    method __len__ (line 171) | def __len__(self):
  class SyntheticDataset (line 175) | class SyntheticDataset(Dataset):
    method __init__ (line 176) | def __init__(self):
    method __getitem__ (line 185) | def __getitem__(self, index):
    method __len__ (line 188) | def __len__(self):
  function dali_data_iter (line 192) | def dali_data_iter(
  class DALIWarper (line 230) | class DALIWarper(object):
    method __init__ (line 231) | def __init__(self, dali_iter):
    method __next__ (line 234) | def __next__(self):
    method __iter__ (line 241) | def __iter__(self):
    method reset (line 244) | def reset(self):

FILE: face_module/TransFace/eval/verification.py
  class LFold (line 41) | class LFold:
    method __init__ (line 42) | def __init__(self, n_splits=2, shuffle=False):
    method split (line 47) | def split(self, indices):
  function calculate_roc (line 54) | def calculate_roc(thresholds,
  function calculate_accuracy (line 109) | def calculate_accuracy(threshold, dist, actual_issame):
  function calculate_val (line 124) | def calculate_val(thresholds,
  function calculate_val_far (line 165) | def calculate_val_far(threshold, dist, actual_issame):
  function evaluate (line 179) | def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
  function load_bin (line 200) | def load_bin(path, image_size):
  function test (line 227) | def test(data_set, backbone, batch_size, nfolds=10):
  function dumpR (line 277) | def dumpR(data_set,

FILE: face_module/TransFace/eval_ijbc.py
  class Embedding (line 54) | class Embedding(object):
    method __init__ (line 55) | def __init__(self, prefix, data_shape, batch_size=1):
    method get (line 75) | def get(self, rimg, landmark):
    method forward_db (line 104) | def forward_db(self, batch_data):
  function divideIntoNstrand (line 112) | def divideIntoNstrand(listTemp, n):
  function read_template_media_list (line 119) | def read_template_media_list(path):
  function read_template_pair_list (line 130) | def read_template_pair_list(path):
  function read_image_feature (line 144) | def read_image_feature(path):
  function get_image_feature (line 153) | def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
  function image2template_feature (line 211) | def image2template_feature(img_feats=None, templates=None, medias=None):
  function verification (line 251) | def verification(template_norm_feats=None,
  function verification2 (line 281) | def verification2(template_norm_feats=None,
  function read_score (line 305) | def read_score(path):

FILE: face_module/TransFace/inference.py
  function inference (line 11) | def inference(weight, name, img):

FILE: face_module/TransFace/losses.py
  class CombinedMarginLoss (line 5) | class CombinedMarginLoss(torch.nn.Module):
    method __init__ (line 6) | def __init__(self,
    method forward (line 27) | def forward(self, logits, labels):
  class ArcFace (line 60) | class ArcFace(torch.nn.Module):
    method __init__ (line 63) | def __init__(self, s=64.0, margin=0.5):
    method forward (line 73) | def forward(self, logits: torch.Tensor, labels: torch.Tensor):
  class CosFace (line 91) | class CosFace(torch.nn.Module):
    method __init__ (line 92) | def __init__(self, s=64.0, m=0.40):
    method forward (line 97) | def forward(self, logits: torch.Tensor, labels: torch.Tensor):

FILE: face_module/TransFace/lr_scheduler.py
  class PolyScheduler (line 4) | class PolyScheduler(_LRScheduler):
    method __init__ (line 5) | def __init__(self, optimizer, base_lr, max_steps, warmup_steps, last_e...
    method get_warmup_lr (line 14) | def get_warmup_lr(self):
    method get_lr (line 18) | def get_lr(self):

FILE: face_module/TransFace/onnx_helper.py
  class ArcFaceORT (line 15) | class ArcFaceORT:
    method __init__ (line 16) | def __init__(self, model_path, cpu=False):
    method check (line 22) | def check(self, track='cfat', test_img = None):
    method check_batch (line 184) | def check_batch(self, img):
    method meta_info (line 202) | def meta_info(self):
    method forward (line 206) | def forward(self, imgs):
    method benchmark (line 222) | def benchmark(self, img):

FILE: face_module/TransFace/onnx_ijbc.py
  class AlignedDataSet (line 30) | class AlignedDataSet(mx.gluon.data.Dataset):
    method __init__ (line 31) | def __init__(self, root, lines, align=True):
    method __len__ (line 36) | def __len__(self):
    method __getitem__ (line 39) | def __getitem__(self, idx):
  function extract (line 56) | def extract(model_root, dataset):
  function read_template_media_list (line 79) | def read_template_media_list(path):
  function read_template_pair_list (line 86) | def read_template_pair_list(path):
  function read_image_feature (line 94) | def read_image_feature(path):
  function image2template_feature (line 100) | def image2template_feature(img_feats=None,
  function verification (line 126) | def verification(template_norm_feats=None,
  function verification2 (line 148) | def verification2(template_norm_feats=None,
  function main (line 170) | def main(args):

FILE: face_module/TransFace/partial_fc_exp.py
  class PartialFC (line 10) | class PartialFC(torch.nn.Module):
    method __init__ (line 33) | def __init__(
    method sample (line 98) | def sample(self,
    method update (line 140) | def update(self):
    method forward (line 152) | def forward(
    method state_dict (line 217) | def state_dict(self, destination=None, prefix="", keep_vars=False):
    method load_state_dict (line 231) | def load_state_dict(self, state_dict, strict: bool = True):
  class PartialFCAdamW (line 242) | class PartialFCAdamW(torch.nn.Module):     # Adam Optimization
    method __init__ (line 243) | def __init__(self,
    method sample (line 313) | def sample(self, labels, index_positive, optimizer):
    method update (line 340) | def update(self):
    method forward (line 352) | def forward(
    method state_dict (line 434) | def state_dict(self, destination=None, prefix="", keep_vars=False):
    method load_state_dict (line 448) | def load_state_dict(self, state_dict, strict: bool = True):
  class DistCrossEntropyFunc (line 460) | class DistCrossEntropyFunc(torch.autograd.Function):
    method forward (line 467) | def forward(ctx, logits: torch.Tensor, label: torch.Tensor):
    method backward (line 494) | def backward(ctx, loss_gradient):
  class DistCrossEntropy (line 519) | class DistCrossEntropy(torch.nn.Module):
    method __init__ (line 520) | def __init__(self):
    method forward (line 523) | def forward(self, logit_part, label_part):
  class AllGatherFunc (line 526) | class AllGatherFunc(torch.autograd.Function):
    method forward (line 530) | def forward(ctx, tensor, *gather_list):
    method backward (line 536) | def backward(ctx, *grads):

FILE: face_module/TransFace/torch2onnx.py
  function convert_onnx (line 6) | def convert_onnx(net, path_module, output, opset=11, simplify=False):

FILE: face_module/TransFace/train.py
  function main (line 43) | def main(args):

FILE: face_module/TransFace/utils/plot.py
  function read_template_pair_list (line 18) | def read_template_pair_list(path):

FILE: face_module/TransFace/utils/utils_callbacks.py
  class CallBackVerification (line 14) | class CallBackVerification(object):
    method __init__ (line 16) | def __init__(self, val_targets, rec_prefix, summary_writer=None, image...
    method ver_test (line 27) | def ver_test(self, backbone: torch.nn.Module, global_step: int):
    method init_dataset (line 44) | def init_dataset(self, val_targets, data_dir, image_size):
    method __call__ (line 52) | def __call__(self, num_update, backbone: torch.nn.Module):
  class CallBackLogging (line 59) | class CallBackLogging(object):
    method __init__ (line 60) | def __init__(self, frequent, total_step, batch_size, start_step=0,writ...
    method __call__ (line 73) | def __call__(self,

FILE: face_module/TransFace/utils/utils_config.py
  function get_config (line 5) | def get_config(config_file):

FILE: face_module/TransFace/utils/utils_distributed_sampler.py
  function setup_seed (line 11) | def setup_seed(seed, cuda_deterministic=True):
  function worker_init_fn (line 25) | def worker_init_fn(worker_id, num_workers, rank, seed):
  function get_dist_info (line 34) | def get_dist_info():
  function sync_random_seed (line 45) | def sync_random_seed(seed=None, device="cuda"):
  class DistributedSampler (line 82) | class DistributedSampler(_DistributedSampler):
    method __init__ (line 83) | def __init__(
    method __iter__ (line 102) | def __iter__(self):

FILE: face_module/TransFace/utils/utils_logging.py
  class AverageMeter (line 6) | class AverageMeter(object):
    method __init__ (line 10) | def __init__(self):
    method reset (line 17) | def reset(self):
    method update (line 23) | def update(self, val, n=1):
  function init_logging (line 30) | def init_logging(rank, models_root):

FILE: facechain/inference_fact.py
  function txt2img (line 26) | def txt2img(pipe, face_image, pos_prompt, neg_prompt, num_images=10):
  function img_pad (line 43) | def img_pad(pil_file, fixed_height=512, fixed_width=512):
  function txt2img_multi (line 70) | def txt2img_multi(pipe,
  function get_mask (line 97) | def get_mask(result):
  function main_diffusion_inference_multi (line 125) | def main_diffusion_inference_multi(num_gen_images,
  function stylization_fn (line 174) | def stylization_fn(use_stylization, rank_results):
  function main_model_inference (line 182) | def main_model_inference(num_gen_images,
  function face_swap_fn (line 219) | def face_swap_fn(use_face_swap, gen_results, template_face, image_face_f...
  function post_process_fn (line 237) | def post_process_fn(use_post_process, swap_results_ori, selected_face,
  class GenPortrait (line 278) | class GenPortrait:
    method __init__ (line 280) | def __init__(self):
    method __call__ (line 373) | def __call__(self,
  function compress_image (line 512) | def compress_image(input_path, target_size):
  function change_extension_to_jpg (line 530) | def change_extension_to_jpg(image_path):

FILE: facechain/inference_inpaint_fact.py
  function concatenate_images (line 34) | def concatenate_images(images):
  function call_face_crop (line 47) | def call_face_crop(det_pipeline, image, crop_ratio):
  function crop_and_paste (line 81) | def crop_and_paste(Source_image,
  function segment (line 123) | def segment(segmentation_pipeline,
  function crop_bottom (line 196) | def crop_bottom(pil_file, width):
  function img2img_multicontrol (line 212) | def img2img_multicontrol(img,
  function get_mask (line 246) | def get_mask(result):
  function main_diffusion_inference_inpaint (line 271) | def main_diffusion_inference_inpaint(num_gen_images,
  function stylization_fn (line 470) | def stylization_fn(use_stylization, rank_results):
  function main_model_inference (line 478) | def main_model_inference(num_gen_images,
  function select_high_quality_face (line 515) | def select_high_quality_face(input_img_dir, face_quality_func):
  function face_swap_fn (line 541) | def face_swap_fn(use_face_swap, gen_results, template_face, image_face_f...
  function post_process_fn (line 562) | def post_process_fn(use_post_process, swap_results_ori, selected_face,
  function process_inpaint_img (line 595) | def process_inpaint_img(inpaint_img, resize_size=(1024, 1024)):
  function postprocess_inpaint_img (line 613) | def postprocess_inpaint_img(img2img_res, output_size=(768, 1024)):
  class GenPortrait_inpaint (line 622) | class GenPortrait_inpaint:
    method __init__ (line 624) | def __init__(self):
    method __call__ (line 711) | def __call__(self,
  function compress_image (line 916) | def compress_image(input_path, target_size):
  function change_extension_to_jpg (line 934) | def change_extension_to_jpg(image_path):

FILE: facechain/merge_lora.py
  function merge_lora (line 10) | def merge_lora(pipeline,
  function restore_lora (line 94) | def restore_lora(pipeline,

FILE: facechain/utils.py
  function max_retries (line 11) | def max_retries(max_attempts):
  function snapshot_download (line 29) | def snapshot_download(*args, **kwargs):
  function pre_download_models (line 33) | def pre_download_models():
  function set_spawn_method (line 44) | def set_spawn_method():
  function check_install (line 50) | def check_install(*args):
  function check_ffmpeg (line 57) | def check_ffmpeg():
  function get_worker_data_dir (line 64) | def get_worker_data_dir() -> str:
  function join_worker_data_dir (line 71) | def join_worker_data_dir(*kwargs) -> str:

FILE: install.py
  function get_pytorch_version (line 32) | def get_pytorch_version():
  function get_python_version (line 36) | def get_python_version():

FILE: more_apps/Facechain-SuDe/ldm/data/base.py
  class Txt2ImgIterableBaseDataset (line 5) | class Txt2ImgIterableBaseDataset(IterableDataset):
    method __init__ (line 9) | def __init__(self, num_records=0, valid_ids=None, size=256):
    method __len__ (line 18) | def __len__(self):
    method __iter__ (line 22) | def __iter__(self):

FILE: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/ldm/data/personalized.py
  class PersonalizedBase (line 144) | class PersonalizedBase(Dataset):
    method __init__ (line 145) | def __init__(self,
    method __len__ (line 191) | def __len__(self):
    method __getitem__ (line 194) | def __getitem__(self, i):

FILE: more_apps/Facechain-SuDe/ldm/data/personalized_style.py
  class PersonalizedBase (line 56) | class PersonalizedBase(Dataset):
    method __init__ (line 57) | def __init__(self,
    method __len__ (line 96) | def __len__(self):
    method __getitem__ (line 99) | def __getitem__(self, i):

FILE: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/ldm/models/diffusion/classifier.py
  function disabled_train (line 22) | def disabled_train(self, mode=True):
  class NoisyLatentImageClassifier (line 28) | class NoisyLatentImageClassifier(pl.LightningModule):
    method __init__ (line 30) | def __init__(self,
    method init_from_ckpt (line 70) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method load_diffusion (line 88) | def load_diffusion(self):
    method load_classifier (line 95) | def load_classifier(self, ckpt_path, pool):
    method get_x_noisy (line 110) | def get_x_noisy(self, x, t, noise=None):
    method forward (line 120) | def forward(self, x_noisy, t, *args, **kwargs):
    method get_input (line 124) | def get_input(self, batch, k):
    method get_conditioning (line 133) | def get_conditioning(self, batch, k=None):
    method compute_top_k (line 150) | def compute_top_k(self, logits, labels, k, reduction="mean"):
    method on_train_epoch_start (line 157) | def on_train_epoch_start(self):
    method write_logs (line 162) | def write_logs(self, loss, logits, targets):
    method shared_step (line 179) | def shared_step(self, batch, t=None):
    method training_step (line 198) | def training_step(self, batch, batch_idx):
    method reset_noise_accs (line 202) | def reset_noise_accs(self):
    method on_validation_start (line 206) | def on_validation_start(self):
    method validation_step (line 210) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 220) | def configure_optimizers(self):
    method log_images (line 238) | def log_images(self, batch, N=8, *args, **kwargs):

FILE: more_apps/Facechain-SuDe/ldm/models/diffusion/ddim.py
  class DDIMSampler (line 12) | class DDIMSampler(object):
    method __init__ (line 13) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 19) | def register_buffer(self, name, attr):
    method make_schedule (line 25) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 57) | def sample(self,
    method ddim_sampling (line 114) | def ddim_sampling(self, cond, shape,
    method p_sample_ddim (line 165) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin...
    method stochastic_encode (line 208) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
    method decode (line 224) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale...

FILE: more_apps/Facechain-SuDe/ldm/models/diffusion/ddpm.py
  function disabled_train (line 40) | def disabled_train(self, mode=True):
  function uniform_on_device (line 46) | def uniform_on_device(r1, r2, shape, device):
  class DDPM (line 50) | class DDPM(pl.LightningModule):
    method __init__ (line 52) | def __init__(self,
    method register_schedule (line 133) | def register_schedule(self, given_betas=None, beta_schedule="linear", ...
    method ema_scope (line 188) | def ema_scope(self, context=None):
    method init_from_ckpt (line 202) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method q_mean_variance (line 220) | def q_mean_variance(self, x_start, t):
    method predict_start_from_noise (line 232) | def predict_start_from_noise(self, x_t, t, noise):
    method q_posterior (line 240) | def q_posterior(self, x_start, x_t, t):
    method p_mean_variance (line 249) | def p_mean_variance(self, x, t, clip_denoised: bool):
    method p_sample (line 262) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
    method p_sample_loop (line 271) | def p_sample_loop(self, shape, return_intermediates=False):
    method sample (line 286) | def sample(self, batch_size=16, return_intermediates=False):
    method q_sample (line 292) | def q_sample(self, x_start, t, noise=None):
    method get_loss (line 297) | def get_loss(self, pred, target, mean=True):
    method p_losses (line 312) | def p_losses(self, x_start, t, noise=None):
    method forward (line 341) | def forward(self, x, *args, **kwargs):
    method get_input (line 347) | def get_input(self, batch, k):
    method shared_step (line 355) | def shared_step(self, batch):
    method training_step (line 360) | def training_step(self, batch, batch_idx):
    method validation_step (line 376) | def validation_step(self, batch, batch_idx):
    method on_train_batch_end (line 384) | def on_train_batch_end(self, *args, **kwargs):
    method _get_rows_from_list (line 388) | def _get_rows_from_list(self, samples):
    method log_images (line 396) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non...
    method configure_optimizers (line 433) | def configure_optimizers(self):
  class LatentDiffusion (line 442) | class LatentDiffusion(DDPM):
    method __init__ (line 444) | def __init__(self,
    method make_cond_schedule (line 515) | def make_cond_schedule(self, ):
    method on_train_batch_start (line 522) | def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
    method register_schedule (line 538) | def register_schedule(self,
    method instantiate_first_stage (line 547) | def instantiate_first_stage(self, config):
    method instantiate_cond_stage (line 554) | def instantiate_cond_stage(self, config):
    method _get_denoise_row_from_list (line 584) | def _get_denoise_row_from_list(self, samples, desc='', force_no_decode...
    method get_first_stage_encoding (line 596) | def get_first_stage_encoding(self, encoder_posterior):
    method get_learned_conditioning (line 606) | def get_learned_conditioning(self, c):
    method meshgrid (line 621) | def meshgrid(self, h, w):
    method delta_border (line 628) | def delta_border(self, h, w):
    method get_weighting (line 642) | def get_weighting(self, h, w, Ly, Lx, device):
    method get_fold_unfold (line 658) | def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo...
    method get_input_original (line 712) | def get_input_original(self, batch, k, return_first_stage_outputs=Fals...
    method get_input (line 767) | def get_input(self, batch, k, return_first_stage_outputs=False, force_...
    method decode_first_stage (line 830) | def decode_first_stage(self, z, predict_cids=False, force_not_quantize...
    method differentiable_decode_first_stage (line 890) | def differentiable_decode_first_stage(self, z, predict_cids=False, for...
    method encode_first_stage (line 950) | def encode_first_stage(self, x):
    method shared_step (line 989) | def shared_step(self, batch, **kwargs):
    method training_step (line 1001) | def training_step(self, batch, batch_idx):
    method forward (line 1024) | def forward(self, x, c, c_class, is_reg, *args, **kwargs):
    method _rescale_annotations (line 1042) | def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: mov...
    method apply_model (line 1052) | def apply_model(self, x_noisy, t, cond, return_ids=False):
    method _predict_eps_from_xstart (line 1151) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method _prior_bpd (line 1155) | def _prior_bpd(self, x_start):
    method norm (line 1170) | def norm(self, x):
    method prob_loss_func_class (line 1176) | def prob_loss_func_class(self, model_output, mu_c, mu, t, x_t, var_t, ...
    method p_losses (line 1210) | def p_losses(self, x_start, cond, cond_class, uc, t, is_reg, noise=None):
    method p_mean_variance (line 1268) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codeboo...
    method p_sample (line 1305) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
    method progressive_denoising (line 1336) | def progressive_denoising(self, cond, shape, verbose=True, callback=No...
    method p_sample_loop (line 1392) | def p_sample_loop(self, cond, shape, return_intermediates=False,
    method sample (line 1443) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=...
    method sample_log (line 1461) | def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
    method log_images (line 1476) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,...
    method configure_optimizers (line 1597) | def configure_optimizers(self):
    method configure_opt_embedding (line 1621) | def configure_opt_embedding(self):
    method configure_opt_model (line 1640) | def configure_opt_model(self):
    method to_rgb (line 1656) | def to_rgb(self, x):
  class DiffusionWrapper (line 1676) | class DiffusionWrapper(pl.LightningModule):
    method __init__ (line 1677) | def __init__(self, diff_model_config, conditioning_key):
    method forward (line 1683) | def forward(self, x, t, c_class: list = None, c_concat: list = None, c...
  class Layout2ImgDiffusion (line 1710) | class Layout2ImgDiffusion(LatentDiffusion):
    method __init__ (line 1712) | def __init__(self, cond_stage_key, *args, **kwargs):
    method log_images (line 1716) | def log_images(self, batch, N=8, *args, **kwargs):
  class FrozenClipImageEmbedder (line 1736) | class FrozenClipImageEmbedder(nn.Module):
    method __init__ (line 1740) | def __init__(
    method preprocess (line 1755) | def preprocess(self, x):
    method forward (line 1765) | def forward(self, x):
    method get_image_features (line 1770) | def get_image_features(self, img: torch.Tensor, norm: bool = True) -> ...
    method img_to_img_similarity (line 1778) | def img_to_img_similarity(self, src_images, generated_images):

FILE: more_apps/Facechain-SuDe/ldm/models/diffusion/plms.py
  class PLMSSampler (line 11) | class PLMSSampler(object):
    method __init__ (line 12) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 18) | def register_buffer(self, name, attr):
    method make_schedule (line 24) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 58) | def sample(self,
    method plms_sampling (line 115) | def plms_sampling(self, cond, shape,
    method p_sample_plms (line 173) | def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_origin...

FILE: more_apps/Facechain-SuDe/ldm/modules/attention.py
  function exists (line 13) | def exists(val):
  function uniq (line 17) | def uniq(arr):
  function default (line 21) | def default(val, d):
  function max_neg_value (line 27) | def max_neg_value(t):
  function init_ (line 31) | def init_(tensor):
  class GEGLU (line 39) | class GEGLU(nn.Module):
    method __init__ (line 40) | def __init__(self, dim_in, dim_out):
    method forward (line 44) | def forward(self, x):
  class FeedForward (line 49) | class FeedForward(nn.Module):
    method __init__ (line 50) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 65) | def forward(self, x):
  function zero_module (line 69) | def zero_module(module):
  function Normalize (line 78) | def Normalize(in_channels):
  class LinearAttention (line 82) | class LinearAttention(nn.Module):
    method __init__ (line 83) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 90) | def forward(self, x):
  class SpatialSelfAttention (line 101) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 102) | def __init__(self, in_channels):
    method forward (line 128) | def forward(self, x):
  class CrossAttention (line 155) | class CrossAttention(nn.Module):
    method __init__ (line 156) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 173) | def forward(self, x, context=None, mask=None):
  class BasicTransformerBlock (line 214) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 215) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 226) | def forward(self, x, context=None):
    method _forward (line 229) | def _forward(self, x, context=None):
  class SpatialTransformer (line 236) | class SpatialTransformer(nn.Module):
    method __init__ (line 244) | def __init__(self, in_channels, n_heads, d_head,
    method forward (line 268) | def forward(self, x, context=None):

FILE: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/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, adj_index=None):
  class Upsample (line 94) | class Upsample(nn.Module):
    method __init__ (line 103) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 112) | def forward(self, x):
  class TransposedUpsample (line 126) | class TransposedUpsample(nn.Module):
    method __init__ (line 128) | def __init__(self, channels, out_channels=None, ks=5):
    method forward (line 135) | def forward(self,x):
  class Downsample (line 139) | class Downsample(nn.Module):
    method __init__ (line 148) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi...
    method forward (line 163) | def forward(self, x):
  class ResBlock (line 168) | class ResBlock(TimestepBlock):
    method __init__ (line 184) | def __init__(
    method forward (line 248) | def forward(self, x, emb):
    method _forward (line 260) | def _forward(self, x, emb):
  class AttentionBlock (line 283) | class AttentionBlock(nn.Module):
    method __init__ (line 290) | def __init__(
    method forward (line 319) | def forward(self, x):
    method _forward (line 323) | def _forward(self, x):
  function count_flops_attn (line 332) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 352) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 357) | def __init__(self, n_heads):
    method forward (line 361) | def forward(self, qkv):
    method count_flops (line 381) | def count_flops(model, _x, y):
  class QKVAttention (line 385) | class QKVAttention(nn.Module):
    method __init__ (line 390) | def __init__(self, n_heads):
    method forward (line 394) | def forward(self, qkv):
    method count_flops (line 415) | def count_flops(model, _x, y):
  class UNetModel (line 419) | class UNetModel(nn.Module):
    method __init__ (line 449) | def __init__(
    method convert_to_fp16 (line 700) | def convert_to_fp16(self):
    method convert_to_fp32 (line 708) | def convert_to_fp32(self):
    method forward (line 716) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
  class EncoderUNetModel (line 760) | class EncoderUNetModel(nn.Module):
    method __init__ (line 766) | def __init__(
    method convert_to_fp16 (line 939) | def convert_to_fp16(self):
    method convert_to_fp32 (line 946) | def convert_to_fp32(self):
    method forward (line 953) | def forward(self, x, timesteps):

FILE: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/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: more_apps/Facechain-SuDe/ldm/modules/ema.py
  class LitEma (line 5) | class LitEma(nn.Module):
    method __init__ (line 6) | def __init__(self, model, decay=0.9999, use_num_upates=True):
    method forward (line 25) | def forward(self,model):
    method copy_to (line 46) | def copy_to(self, model):
    method store (line 55) | def store(self, parameters):
    method restore (line 64) | def restore(self, parameters):

FILE: more_apps/Facechain-SuDe/ldm/modules/embedding_manager.py
  function get_clip_token_for_string (line 13) | def get_clip_token_for_string(tokenizer, string):
  function get_bert_token_for_string (line 21) | def get_bert_token_for_string(tokenizer, string):
  function get_embedding_for_clip_token (line 29) | def get_embedding_for_clip_token(embedder, token):
  class EmbeddingManager (line 33) | class EmbeddingManager(nn.Module):
    method __init__ (line 34) | def __init__(
    method forward (line 93) | def forward(
    method save (line 138) | def save(self, ckpt_path):
    method load (line 142) | def load(self, ckpt_path):
    method get_embedding_norms_squared (line 148) | def get_embedding_norms_squared(self):
    method embedding_parameters (line 154) | def embedding_parameters(self):
    method embedding_to_coarse_loss (line 157) | def embedding_to_coarse_loss(self):

FILE: more_apps/Facechain-SuDe/ldm/modules/encoders/modules.py
  function _expand_mask (line 11) | def _expand_mask(mask, dtype, tgt_len = None):
  function _build_causal_attention_mask (line 24) | def _build_causal_attention_mask(bsz, seq_len, dtype):
  class AbstractEncoder (line 33) | class AbstractEncoder(nn.Module):
    method __init__ (line 34) | def __init__(self):
    method encode (line 37) | def encode(self, *args, **kwargs):
  class ClassEmbedder (line 42) | class ClassEmbedder(nn.Module):
    method __init__ (line 43) | def __init__(self, embed_dim, n_classes=1000, key='class'):
    method forward (line 48) | def forward(self, batch, key=None):
  class TransformerEmbedder (line 57) | class TransformerEmbedder(AbstractEncoder):
    method __init__ (line 59) | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, devic...
    method forward (line 65) | def forward(self, tokens):
    method encode (line 70) | def encode(self, x):
  class BERTTokenizer (line 74) | class BERTTokenizer(AbstractEncoder):
    method __init__ (line 76) | def __init__(self, device="cuda", vq_interface=True, max_length=77):
    method forward (line 84) | def forward(self, text):
    method encode (line 91) | def encode(self, text):
    method decode (line 97) | def decode(self, text):
  class BERTEmbedder (line 101) | class BERTEmbedder(AbstractEncoder):
    method __init__ (line 103) | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
    method forward (line 114) | def forward(self, text, embedding_manager=None):
    method encode (line 124) | def encode(self, text, **kwargs):
  class SpatialRescaler (line 128) | class SpatialRescaler(nn.Module):
    method __init__ (line 129) | def __init__(self,
    method forward (line 147) | def forward(self,x):
    method encode (line 156) | def encode(self, x):
  class FrozenCLIPEmbedder (line 159) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 161) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
    method freeze (line 314) | def freeze(self):
    method forward (line 319) | def forward(self, text, **kwargs):
    method encode (line 331) | def encode(self, text, **kwargs):
  class FrozenCLIPTextEmbedder (line 335) | class FrozenCLIPTextEmbedder(nn.Module):
    method __init__ (line 339) | def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n...
    method freeze (line 347) | def freeze(self):
    method forward (line 352) | def forward(self, text):
    method encode (line 359) | def encode(self, text):
  class FrozenClipImageEmbedder (line 367) | class FrozenClipImageEmbedder(nn.Module):
    method __init__ (line 371) | def __init__(
    method preprocess (line 386) | def preprocess(self, x):
    method forward (line 396) | def forward(self, x):

FILE: more_apps/Facechain-SuDe/ldm/modules/encoders/modules_bak.py
  function _expand_mask (line 11) | def _expand_mask(mask, dtype, tgt_len = None):
  function _build_causal_attention_mask (line 24) | def _build_causal_attention_mask(bsz, seq_len, dtype):
  class AbstractEncoder (line 33) | class AbstractEncoder(nn.Module):
    method __init__ (line 34) | def __init__(self):
    method encode (line 37) | def encode(self, *args, **kwargs):
  class ClassEmbedder (line 42) | class ClassEmbedder(nn.Module):
    method __init__ (line 43) | def __init__(self, embed_dim, n_classes=1000, key='class'):
    method forward (line 48) | def forward(self, batch, key=None):
  class TransformerEmbedder (line 57) | class TransformerEmbedder(AbstractEncoder):
    method __init__ (line 59) | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, devic...
    method forward (line 65) | def forward(self, tokens):
    method encode (line 70) | def encode(self, x):
  class BERTTokenizer (line 74) | class BERTTokenizer(AbstractEncoder):
    method __init__ (line 76) | def __init__(self, device="cuda", vq_interface=True, max_length=77):
    method forward (line 84) | def forward(self, text):
    method encode (line 91) | def encode(self, text):
    method decode (line 97) | def decode(self, text):
  class BERTEmbedder (line 101) | class BERTEmbedder(AbstractEncoder):
    method __init__ (line 103) | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
    method forward (line 114) | def forward(self, text, embedding_manager=None):
    method encode (line 122) | def encode(self, text, **kwargs):
  class SpatialRescaler (line 126) | class SpatialRescaler(nn.Module):
    method __init__ (line 127) | def __init__(self,
    method forward (line 145) | def forward(self,x):
    method encode (line 154) | def encode(self, x):
  class FrozenCLIPEmbedder (line 157) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 159) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
    method freeze (line 410) | def freeze(self):
    method forward (line 415) | def forward(self, text, **kwargs):
    method encode (line 423) | def encode(self, text, **kwargs):
  class FrozenCLIPTextEmbedder (line 427) | class FrozenCLIPTextEmbedder(nn.Module):
    method __init__ (line 431) | def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n...
    method freeze (line 439) | def freeze(self):
    method forward (line 444) | def forward(self, text):
    method encode (line 451) | def encode(self, text):
  class FrozenClipImageEmbedder (line 459) | class FrozenClipImageEmbedder(nn.Module):
    method __init__ (line 463) | def __init__(
    method preprocess (line 478) | def preprocess(self, x):
    method forward (line 488) | def forward(self, x):

FILE: more_apps/Facechain-SuDe/ldm/modules/image_degradation/bsrgan.py
  function modcrop_np (line 29) | def modcrop_np(img, sf):
  function analytic_kernel (line 49) | def analytic_kernel(k):
  function anisotropic_Gaussian (line 65) | def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
  function gm_blur_kernel (line 86) | def gm_blur_kernel(mean, cov, size=15):
  function shift_pixel (line 99) | def shift_pixel(x, sf, upper_left=True):
  function blur (line 128) | def blur(x, k):
  function gen_kernel (line 145) | def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]),...
  function fspecial_gaussian (line 187) | def fspecial_gaussian(hsize, sigma):
  function fspecial_laplacian (line 201) | def fspecial_laplacian(alpha):
  function fspecial (line 210) | def fspecial(filter_type, *args, **kwargs):
  function bicubic_degradation (line 228) | def bicubic_degradation(x, sf=3):
  function srmd_degradation (line 240) | def srmd_degradation(x, k, sf=3):
  function dpsr_degradation (line 262) | def dpsr_degradation(x, k, sf=3):
  function classical_degradation (line 284) | def classical_degradation(x, k, sf=3):
  function add_sharpening (line 299) | def add_sharpening(img, weight=0.5, radius=50, threshold=10):
  function add_blur (line 325) | def add_blur(img, sf=4):
  function add_resize (line 339) | def add_resize(img, sf=4):
  function add_Gaussian_noise (line 369) | def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
  function add_speckle_noise (line 386) | def add_speckle_noise(img, noise_level1=2, noise_level2=25):
  function add_Poisson_noise (line 404) | def add_Poisson_noise(img):
  function add_JPEG_noise (line 418) | def add_JPEG_noise(img):
  function random_crop (line 427) | def random_crop(lq, hq, sf=4, lq_patchsize=64):
  function degradation_bsrgan (line 438) | def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
  function degradation_bsrgan_variant (line 530) | def degradation_bsrgan_variant(image, sf=4, isp_model=None):
  function degradation_bsrgan_plus (line 617) | def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True,...

FILE: more_apps/Facechain-SuDe/ldm/modules/image_degradation/bsrgan_light.py
  function modcrop_np (line 29) | def modcrop_np(img, sf):
  function analytic_kernel (line 49) | def analytic_kernel(k):
  function anisotropic_Gaussian (line 65) | def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
  function gm_blur_kernel (line 86) | def gm_blur_kernel(mean, cov, size=15):
  function shift_pixel (line 99) | def shift_pixel(x, sf, upper_left=True):
  function blur (line 128) | def blur(x, k):
  function gen_kernel (line 145) | def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]),...
  function fspecial_gaussian (line 187) | def fspecial_gaussian(hsize, sigma):
  function fspecial_laplacian (line 201) | def fspecial_laplacian(alpha):
  function fspecial (line 210) | def fspecial(filter_type, *args, **kwargs):
  function bicubic_degradation (line 228) | def bicubic_degradation(x, sf=3):
  function srmd_degradation (line 240) | def srmd_degradation(x, k, sf=3):
  function dpsr_degradation (line 262) | def dpsr_degradation(x, k, sf=3):
  function classical_degradation (line 284) | def classical_degradation(x, k, sf=3):
  function add_sharpening (line 299) | def add_sharpening(img, weight=0.5, radius=50, threshold=10):
  function add_blur (line 325) | def add_blur(img, sf=4):
  function add_resize (line 343) | def add_resize(img, sf=4):
  function add_Gaussian_noise (line 373) | def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
  function add_speckle_noise (line 390) | def add_speckle_noise(img, noise_level1=2, noise_level2=25):
  function add_Poisson_noise (line 408) | def add_Poisson_noise(img):
  function add_JPEG_noise (line 422) | def add_JPEG_noise(img):
  function random_crop (line 431) | def random_crop(lq, hq, sf=4, lq_patchsize=64):
  function degradation_bsrgan (line 442) | def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
  function degradation_bsrgan_variant (line 534) | def degradation_bsrgan_variant(image, sf=4, isp_model=None):

FILE: more_apps/Facechain-SuDe/ldm/modules/image_degradation/utils_image.py
  function is_image_file (line 29) | def is_image_file(filename):
  function get_timestamp (line 33) | def get_timestamp():
  function imshow (line 37) | def imshow(x, title=None, cbar=False, figsize=None):
  function surf (line 47) | def surf(Z, cmap='rainbow', figsize=None):
  function get_image_paths (line 67) | def get_image_paths(dataroot):
  function _get_paths_from_images (line 74) | def _get_paths_from_images(path):
  function patches_from_image (line 93) | def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
  function imssave (line 112) | def imssave(imgs, img_path):
  function split_imageset (line 125) | def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_si...
  function mkdir (line 153) | def mkdir(path):
  function mkdirs (line 158) | def mkdirs(paths):
  function mkdir_and_rename (line 166) | def mkdir_and_rename(path):
  function imread_uint (line 185) | def imread_uint(path, n_channels=3):
  function imsave (line 203) | def imsave(img, img_path):
  function imwrite (line 209) | def imwrite(img, img_path):
  function read_img (line 220) | def read_img(path):
  function uint2single (line 249) | def uint2single(img):
  function single2uint (line 254) | def single2uint(img):
  function uint162single (line 259) | def uint162single(img):
  function single2uint16 (line 264) | def single2uint16(img):
  function uint2tensor4 (line 275) | def uint2tensor4(img):
  function uint2tensor3 (line 282) | def uint2tensor3(img):
  function tensor2uint (line 289) | def tensor2uint(img):
  function single2tensor3 (line 302) | def single2tensor3(img):
  function single2tensor4 (line 307) | def single2tensor4(img):
  function tensor2single (line 312) | def tensor2single(img):
  function tensor2single3 (line 320) | def tensor2single3(img):
  function single2tensor5 (line 329) | def single2tensor5(img):
  function single32tensor5 (line 333) | def single32tensor5(img):
  function single42tensor4 (line 337) | def single42tensor4(img):
  function tensor2img (line 342) | def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
  function augment_img (line 380) | def augment_img(img, mode=0):
  function augment_img_tensor4 (line 401) | def augment_img_tensor4(img, mode=0):
  function augment_img_tensor (line 422) | def augment_img_tensor(img, mode=0):
  function augment_img_np3 (line 441) | def augment_img_np3(img, mode=0):
  function augment_imgs (line 469) | def augment_imgs(img_list, hflip=True, rot=True):
  function modcrop (line 494) | def modcrop(img_in, scale):
  function shave (line 510) | def shave(img_in, border=0):
  function rgb2ycbcr (line 529) | def rgb2ycbcr(img, only_y=True):
  function ycbcr2rgb (line 553) | def ycbcr2rgb(img):
  function bgr2ycbcr (line 573) | def bgr2ycbcr(img, only_y=True):
  function channel_convert (line 597) | def channel_convert(in_c, tar_type, img_list):
  function calculate_psnr (line 621) | def calculate_psnr(img1, img2, border=0):
  function calculate_ssim (line 642) | def calculate_ssim(img1, img2, border=0):
  function ssim (line 669) | def ssim(img1, img2):
  function cubic (line 700) | def cubic(x):
  function calculate_weights_indices (line 708) | def calculate_weights_indices(in_length, out_length, scale, kernel, kern...
  function imresize (line 766) | def imresize(img, scale, antialiasing=True):
  function imresize_np (line 839) | def imresize_np(img, scale, antialiasing=True):

FILE: more_apps/Facechain-SuDe/ldm/modules/losses/contperceptual.py
  class LPIPSWithDiscriminator (line 7) | class LPIPSWithDiscriminator(nn.Module):
    method __init__ (line 8) | def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixello...
    method calculate_adaptive_weight (line 32) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
    method forward (line 45) | def forward(self, inputs, reconstructions, posteriors, optimizer_idx,

FILE: more_apps/Facechain-SuDe/ldm/modules/losses/vqperceptual.py
  function hinge_d_loss_with_exemplar_weights (line 11) | def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
  function adopt_weight (line 20) | def adopt_weight(weight, global_step, threshold=0, value=0.):
  function measure_perplexity (line 26) | def measure_perplexity(predicted_indices, n_embed):
  function l1 (line 35) | def l1(x, y):
  function l2 (line 39) | def l2(x, y):
  class VQLPIPSWithDiscriminator (line 43) | class VQLPIPSWithDiscriminator(nn.Module):
    method __init__ (line 44) | def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
    method calculate_adaptive_weight (line 85) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
    method forward (line 98) | def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,

FILE: more_apps/Facechain-SuDe/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 542) | class Encoder(AttentionLayers):
    method __init__ (line 543) | def __init__(self, **kwargs):
  class TransformerWrapper (line 549) | class TransformerWrapper(nn.Module):
    method __init__ (line 550) | def __init__(
    method init_ (line 596) | def init_(self):
    method forward (line 599) | def forward(

FILE: more_apps/Facechain-SuDe/ldm/util.py
  function log_txt_as_img (line 17) | def log_txt_as_img(wh, xc, size=10):
  function ismap (line 41) | def ismap(x):
  function isimage (line 47) | def isimage(x):
  function exists (line 53) | def exists(x):
  function default (line 57) | def default(val, d):
  function mean_flat (line 63) | def mean_flat(tensor):
  function count_params (line 71) | def count_params(model, verbose=False):
  function instantiate_from_config (line 78) | def instantiate_from_config(config, **kwargs):
  function get_obj_from_str (line 88) | def get_obj_from_str(string, reload=False):
  function _do_parallel_data_prefetch (line 96) | def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
  function parallel_data_prefetch (line 108) | def parallel_data_prefetch(

FILE: more_apps/Facechain-SuDe/main.py
  function load_model_from_config (line 24) | def load_model_from_config(config, ckpt, verbose=False):
  function get_parser (line 41) | def get_parser(**parser_kwargs):
  function nondefault_trainer_args (line 186) | def nondefault_trainer_args(opt):
  class WrappedDataset (line 193) | class WrappedDataset(Dataset):
    method __init__ (line 196) | def __init__(self, dataset):
    method __len__ (line 199) | def __len__(self):
    method __getitem__ (line 202) | def __getitem__(self, idx):
  function worker_init_fn (line 206) | def worker_init_fn(_):
  class ConcatDataset (line 221) | class ConcatDataset(Dataset):
    method __init__ (line 222) | def __init__(self, *datasets):
    method __getitem__ (line 225) | def __getitem__(self, idx):
    method __len__ (line 228) | def __len__(self):
  class DataModuleFromConfig (line 231) | class DataModuleFromConfig(pl.LightningDataModule):
    method __init__ (line 232) | def __init__(self, batch_size, train=None, reg=None, validation=None, ...
    method prepare_data (line 258) | def prepare_data(self):
    method setup (line 262) | def setup(self, stage=None):
    method _train_dataloader (line 270) | def _train_dataloader(self):
    method _val_dataloader (line 284) | def _val_dataloader(self, shuffle=False):
    method _test_dataloader (line 295) | def _test_dataloader(self, shuffle=False):
    method _predict_dataloader (line 308) | def _predict_dataloader(self, shuffle=False):
  class SetupCallback (line 317) | class SetupCallback(Callback):
    method __init__ (line 318) | def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, light...
    method on_keyboard_interrupt (line 328) | def on_keyboard_interrupt(self, trainer, pl_module):
    method on_pretrain_routine_start (line 334) | def on_pretrain_routine_start(self, trainer, pl_module):
  class ImageLogger (line 366) | class ImageLogger(Callback):
    method __init__ (line 367) | def __init__(self, batch_frequency, max_images, clamp=True, increase_l...
    method _testtube (line 387) | def _testtube(self, pl_module, images, batch_idx, split):
    method log_local (line 398) | def log_local(self, save_dir, split, images,
    method log_img (line 417) | def log_img(self, pl_module, batch, batch_idx, split="train"):
    method check_frequency (line 449) | def check_frequency(self, check_idx):
    method on_train_batch_end (line 460) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...
    method on_validation_batch_end (line 464) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ...
  class CUDACallback (line 472) | class CUDACallback(Callback):
    method on_train_epoch_start (line 474) | def on_train_epoch_start(self, trainer, pl_module):
    method on_train_epoch_end (line 480) | def on_train_epoch_end(self, trainer, pl_module):
  class ModeSwapCallback (line 494) | class ModeSwapCallback(Callback):
    method __init__ (line 496) | def __init__(self, swap_step=2000):
    method on_train_epoch_start (line 501) | def on_train_epoch_start(self, trainer, pl_module):
  function melk (line 815) | def melk(*args, **kwargs):
  function divein (line 823) | def divein(*args, **kwargs):

FILE: more_apps/Facechain-SuDe/merge_embeddings.py
  function get_placeholder_loop (line 9) | def get_placeholder_loop(placeholder_string, embedder, is_sd):
  function get_clip_token_for_string (line 24) | def get_clip_token_for_string(tokenizer, string):
  function get_bert_token_for_string (line 34) | def get_bert_token_for_string(tokenizer, string):

FILE: more_apps/Facechain-SuDe/scripts/evaluate_model.py
  function load_model_from_config (line 21) | def load_model_from_config(config, ckpt, verbose=False):
  function evaluate_model_func (line 40) | def evaluate_model_func(temp_prompt_adj, prompt, ckpt_path, data_dir, ou...
  function extract_all_images (line 67) | def extract_all_images(images, model, datasetclass, device, batch_size=6...
  function dinoeval_image (line 86) | def dinoeval_image(image_dir, image_dir_ref, device):
  function Convert (line 109) | def Convert(image):
  class DINOImageDataset (line 113) | class DINOImageDataset(torch.utils.data.Dataset):
    method __init__ (line 114) | def __init__(self, data):
    method _transform_test (line 119) | def _transform_test(self, n_px):
    method __getitem__ (line 128) | def __getitem__(self, idx):
    method __len__ (line 134) | def __len__(self):

FILE: more_apps/Facechain-SuDe/scripts/inpaint.py
  function make_batch (line 11) | def make_batch(image, mask, device):

FILE: more_apps/Facechain-SuDe/scripts/sample_diffusion.py
  function custom_to_pil (line 15) | def custom_to_pil(x):
  function custom_to_np (line 27) | def custom_to_np(x):
  function logs2pil (line 36) | def logs2pil(logs, keys=["sample"]):
  function convsample (line 54) | def convsample(model, shape, return_intermediates=True,
  function convsample_ddim (line 69) | def convsample_ddim(model, steps, shape, eta=1.0
  function make_convolutional_sample (line 79) | def make_convolutional_sample(model, batch_size, vanilla=False, custom_s...
  function run (line 108) | def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, ...
  function save_logs (line 143) | def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
  function get_parser (line 162) | def get_parser():
  function load_model_from_config (line 220) | def load_model_from_config(config, sd):
  function load_model (line 228) | def load_model(config, ckpt, gpu, eval_mode):

FILE: more_apps/Facechain-SuDe/scripts/stable_txt2img.py
  function chunk (line 22) | def chunk(it, size):
  function load_model_from_config (line 27) | def load_model_from_config(config, ckpt, verbose=False):
  function load_model_from_config_st (line 47) | def load_model_from_config_st(config, ckpt, verbose=False):
  function main (line 68) | def main():

FILE: more_apps/Facechain-SuDe/scripts/txt2img.py
  function load_model_from_config (line 14) | def load_model_from_config(config, ckpt, verbose=False):

FILE: run_inference.py
  function generate_pos_prompt (line 10) | def generate_pos_prompt(style_model, prompt_cloth):

FILE: scripts/facechain_sdwebui.py
  function on_ui_tabs (line 8) | def on_ui_tabs():
  function on_ui_settings (line 32) | def on_ui_settings():

FILE: train_style/convert_lora.py
  function convert_lora (line 4) | def convert_lora(src, dst):

FILE: train_style/deepbooru.py
  class DeepDanbooruModel (line 20) | class DeepDanbooruModel(nn.Module):
    method __init__ (line 21) | def __init__(self):
    method forward (line 207) | def forward(self, *inputs):
    method load_state_dict (line 685) | def load_state_dict(self, state_dict, **kwargs):
  function resize_image (line 691) | def resize_image(im, width, height):
  class DeepDanbooru (line 716) | class DeepDanbooru:
    method __init__ (line 717) | def __init__(self):
    method start (line 728) | def start(self):
    method stop (line 731) | def stop(self):
    method tag (line 736) | def tag(self, pil_image, threshold=0.5):

FILE: train_style/demo.py
  function set_img (line 13) | def set_img(files, uuid, output_model_name):
  function init_tag (line 50) | def init_tag():
  function cut_img (line 55) | def cut_img(img_path):
  function train_lora (line 85) | def train_lora(uuid, output_model_name, prompt_input, train_folder, gall...
  function set_prompt (line 167) | def set_prompt():

FILE: train_style/train_text_to_image_lora.py
  function save_model_card (line 74) | def save_model_card(
  function log_validation (line 115) | def log_validation(
  function parse_args (line 157) | def parse_args():
  function main (line 449) | def main():