SYMBOL INDEX (578 symbols across 29 files)

FILE: diffusers_models.py
  class UNet2DConditionPatchModel (line 15) | class UNet2DConditionPatchModel(UNet2DConditionModel):
    method __init__ (line 17) | def __init__(
    method forward (line 105) | def forward(

FILE: diffusers_sample.py
  function import_model_class_from_model_name_or_path (line 16) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_...
  class StableDiffusionGuidancePipeline (line 36) | class StableDiffusionGuidancePipeline(StableDiffusionPipeline):
    method __init__ (line 39) | def __init__(
    method add_pretrained_model (line 61) | def add_pretrained_model(self, text_encoder, unet):
    method from_pretrained (line 66) | def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
    method _encode_prompt_orig (line 190) | def _encode_prompt_orig(
    method __call__ (line 325) | def __call__(
  function parse_args (line 482) | def parse_args(input_args=None):

FILE: diffusers_train.py
  function import_model_class_from_model_name_or_path (line 57) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_...
  function parse_args (line 77) | def parse_args(input_args=None):
  class SINEDatasetPatch (line 368) | class SINEDatasetPatch(Dataset):
    method __init__ (line 369) | def __init__(
    method __len__ (line 418) | def __len__(self):
    method _random_crop (line 421) | def _random_crop(self, pil_image):
    method __getitem__ (line 432) | def __getitem__(self, i):
  class SINEDatasetSingleRes (line 453) | class SINEDatasetSingleRes(Dataset):
    method __init__ (line 454) | def __init__(
    method __len__ (line 493) | def __len__(self):
    method __getitem__ (line 496) | def __getitem__(self, index):
  function collate_fn (line 505) | def collate_fn(examples):
  function get_full_repo_name (line 535) | def get_full_repo_name(model_id: str, organization: Optional[str] = None...
  function main (line 545) | def main(args):

FILE: 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: ldm/data/personalized.py
  class PersonalizedBase (line 163) | class PersonalizedBase(Dataset):
    method __init__ (line 164) | def __init__(self,
    method __len__ (line 210) | def __len__(self):
    method __getitem__ (line 213) | def __getitem__(self, i):
  function crop_image (line 249) | def crop_image(img, size=512, cropping='random', crop_scale=[1, 1]):
  class PersonalizedMulti (line 274) | class PersonalizedMulti(Dataset):
    method __init__ (line 275) | def __init__(
    method setup_templates (line 342) | def setup_templates(self, placeholder_token='sks', coarse_class_text='...
    method __len__ (line 359) | def __len__(self):
    method __getitem__ (line 362) | def __getitem__(self, i):
  class SinImageDataset (line 390) | class SinImageDataset(PersonalizedBase):
    method __init__ (line 391) | def __init__(
  class SinImageHighResDataset (line 439) | class SinImageHighResDataset(Dataset):
    method __init__ (line 440) | def __init__(self,
    method __len__ (line 497) | def __len__(self):
    method _random_crop (line 500) | def _random_crop(self, pil_image):
    method __getitem__ (line 511) | def __getitem__(self, i):

FILE: ldm/data/personalized_painting.py
  class PersonalizedBase (line 54) | class PersonalizedBase(Dataset):
    method __init__ (line 55) | def __init__(self,
    method __len__ (line 104) | def __len__(self):
    method __getitem__ (line 107) | def __getitem__(self, i):
  class SinImageDataset (line 151) | class SinImageDataset(PersonalizedBase):
    method __init__ (line 152) | def __init__(
  class SinImageHighResDataset (line 204) | class SinImageHighResDataset(Dataset):
    method __init__ (line 205) | def __init__(self,
    method __len__ (line 265) | def __len__(self):
    method _random_crop (line 268) | def _random_crop(self, pil_image):
    method __getitem__ (line 281) | def __getitem__(self, i):

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

FILE: ldm/modules/attention.py
  function exists (line 11) | def exists(val):
  function uniq (line 15) | def uniq(arr):
  function default (line 19) | def default(val, d):
  function max_neg_value (line 25) | def max_neg_value(t):
  function init_ (line 29) | def init_(tensor):
  class GEGLU (line 37) | class GEGLU(nn.Module):
    method __init__ (line 38) | def __init__(self, dim_in, dim_out):
    method forward (line 42) | def forward(self, x):
  class FeedForward (line 47) | class FeedForward(nn.Module):
    method __init__ (line 48) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 63) | def forward(self, x):
  function zero_module (line 67) | def zero_module(module):
  function Normalize (line 76) | def Normalize(in_channels):
  class LinearAttention (line 80) | class LinearAttention(nn.Module):
    method __init__ (line 81) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 88) | def forward(self, x):
  class SpatialSelfAttention (line 99) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 100) | def __init__(self, in_channels):
    method forward (line 126) | def forward(self, x):
  class CrossAttention (line 152) | class CrossAttention(nn.Module):
    method __init__ (line 153) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 170) | def forward(self, x, context=None, mask=None):
  class BasicTransformerBlock (line 196) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 197) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 208) | def forward(self, x, context=None):
    method _forward (line 211) | def _forward(self, x, context=None):
  class SpatialTransformer (line 218) | class SpatialTransformer(nn.Module):
    method __init__ (line 226) | def __init__(self, in_channels, n_heads, d_head,
    method forward (line 250) | def forward(self, x, context=None):

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

FILE: ldm/modules/diffusionmodules/openaimodel.py
  function convert_module_to_f16 (line 30) | def convert_module_to_f16(x):
  function convert_module_to_f32 (line 34) | def convert_module_to_f32(x):
  class AttentionPool2d (line 39) | class AttentionPool2d(nn.Module):
    method __init__ (line 44) | def __init__(
    method forward (line 59) | def forward(self, x):
  class TimestepBlock (line 70) | class TimestepBlock(nn.Module):
    method forward (line 76) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 82) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 88) | def forward(self, x, emb, context=None):
  class Upsample (line 99) | class Upsample(nn.Module):
    method __init__ (line 108) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 118) | def forward(self, x):
  class TransposedUpsample (line 131) | class TransposedUpsample(nn.Module):
    method __init__ (line 134) | def __init__(self, channels, out_channels=None, ks=5):
    method forward (line 142) | def forward(self, x):
  class Downsample (line 146) | class Downsample(nn.Module):
    method __init__ (line 155) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 170) | def forward(self, x):
  class ResBlock (line 175) | class ResBlock(TimestepBlock):
    method __init__ (line 191) | def __init__(
    method forward (line 257) | def forward(self, x, emb):
    method _forward (line 268) | def _forward(self, x, emb):
  class AttentionBlock (line 291) | class AttentionBlock(nn.Module):
    method __init__ (line 298) | def __init__(
    method forward (line 327) | def forward(self, x):
    method _forward (line 332) | def _forward(self, x):
  function count_flops_attn (line 341) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 361) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 366) | def __init__(self, n_heads):
    method forward (line 370) | def forward(self, qkv):
    method count_flops (line 390) | def count_flops(model, _x, y):
  class QKVAttention (line 394) | class QKVAttention(nn.Module):
    method __init__ (line 399) | def __init__(self, n_heads):
    method forward (line 403) | def forward(self, qkv):
    method count_flops (line 425) | def count_flops(model, _x, y):
  class UNetModel (line 429) | class UNetModel(nn.Module):
    method __init__ (line 459) | def __init__(
    method convert_to_fp16 (line 712) | def convert_to_fp16(self):
    method convert_to_fp32 (line 720) | def convert_to_fp32(self):
    method forward (line 728) | def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
  class UNetModelPatch (line 764) | class UNetModelPatch(UNetModel):
    method __init__ (line 765) | def __init__(
    method forward (line 832) | def forward(self, x, timesteps=None, context=None, y=None, crop_boxes=...
  class EncoderUNetModel (line 894) | class EncoderUNetModel(nn.Module):
    method __init__ (line 900) | def __init__(
    method convert_to_fp16 (line 1073) | def convert_to_fp16(self):
    method convert_to_fp32 (line 1080) | def convert_to_fp32(self):
    method forward (line 1087) | def forward(self, x, timesteps):

FILE: ldm/modules/diffusionmodules/positional_encoding.py
  class SinusoidalPositionalEmbedding (line 15) | class SinusoidalPositionalEmbedding(nn.Module):
    method __init__ (line 39) | def __init__(self,
    method get_embedding (line 59) | def get_embedding(num_embeddings,
    method forward (line 89) | def forward(self, input, **kwargs):
    method make_positions (line 115) | def make_positions(self, input, padding_idx):
    method make_grid2d (line 120) | def make_grid2d(self, height, width, num_batches=1, center_shift=None):
    method make_grid2d_like (line 168) | def make_grid2d_like(self, x, center_shift=None):
  class CatersianGrid (line 182) | class CatersianGrid(nn.Module):
    method forward (line 191) | def forward(self, x, **kwargs):
    method make_grid2d (line 195) | def make_grid2d(self, height, width, num_batches=1, requires_grad=False):
    method make_grid2d_like (line 208) | def make_grid2d_like(self, x, requires_grad=False):

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

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

FILE: ldm/modules/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: ldm/modules/embedding_manager.py
  function get_clip_token_for_string (line 12) | def get_clip_token_for_string(tokenizer, string):
  function get_bert_token_for_string (line 20) | def get_bert_token_for_string(tokenizer, string):
  function get_embedding_for_clip_token (line 28) | def get_embedding_for_clip_token(embedder, token):
  class EmbeddingManager (line 32) | class EmbeddingManager(nn.Module):
    method __init__ (line 33) | def __init__(
    method forward (line 88) | def forward(
    method save (line 131) | def save(self, ckpt_path):
    method load (line 135) | def load(self, ckpt_path):
    method get_embedding_norms_squared (line 141) | def get_embedding_norms_squared(self):
    method embedding_parameters (line 147) | def embedding_parameters(self):
    method embedding_to_coarse_loss (line 150) | def embedding_to_coarse_loss(self):

FILE: 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 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 310) | def freeze(self):
    method forward (line 315) | def forward(self, text, **kwargs):
    method encode (line 324) | def encode(self, text, **kwargs):
  class FrozenCLIPTextEmbedder (line 328) | class FrozenCLIPTextEmbedder(nn.Module):
    method __init__ (line 332) | def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n...
    method freeze (line 340) | def freeze(self):
    method forward (line 345) | def forward(self, text):
    method encode (line 352) | def encode(self, text):
  class FrozenClipImageEmbedder (line 360) | class FrozenClipImageEmbedder(nn.Module):
    method __init__ (line 364) | def __init__(
    method preprocess (line 379) | def preprocess(self, x):
    method forward (line 389) | def forward(self, x):

FILE: 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: 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: 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: 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: 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: 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: 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: 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: 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 181) | def nondefault_trainer_args(opt):
  class WrappedDataset (line 188) | class WrappedDataset(Dataset):
    method __init__ (line 191) | def __init__(self, dataset):
    method __len__ (line 194) | def __len__(self):
    method __getitem__ (line 197) | def __getitem__(self, idx):
  function worker_init_fn (line 201) | def worker_init_fn(_):
  class ConcatDataset (line 216) | class ConcatDataset(Dataset):
    method __init__ (line 217) | def __init__(self, *datasets):
    method __getitem__ (line 220) | def __getitem__(self, idx):
    method __len__ (line 223) | def __len__(self):
  class DataModuleFromConfig (line 226) | class DataModuleFromConfig(pl.LightningDataModule):
    method __init__ (line 227) | def __init__(self, batch_size, train=None, reg = None, validation=None...
    method prepare_data (line 253) | def prepare_data(self):
    method setup (line 257) | def setup(self, stage=None):
    method _train_dataloader (line 265) | def _train_dataloader(self):
    method _val_dataloader (line 278) | def _val_dataloader(self, shuffle=False):
    method _test_dataloader (line 289) | def _test_dataloader(self, shuffle=False):
    method _predict_dataloader (line 302) | def _predict_dataloader(self, shuffle=False):
  class SetupCallback (line 311) | class SetupCallback(Callback):
    method __init__ (line 312) | def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, light...
    method on_keyboard_interrupt (line 322) | def on_keyboard_interrupt(self, trainer, pl_module):
    method on_pretrain_routine_start (line 328) | def on_pretrain_routine_start(self, trainer, pl_module):
  class ImageLogger (line 360) | class ImageLogger(Callback):
    method __init__ (line 361) | def __init__(self, batch_frequency, max_images, clamp=True, increase_l...
    method _testtube (line 381) | def _testtube(self, pl_module, images, batch_idx, split):
    method log_local (line 392) | def log_local(self, save_dir, split, images,
    method log_img (line 411) | def log_img(self, pl_module, batch, batch_idx, split="train"):
    method check_frequency (line 443) | def check_frequency(self, check_idx):
    method on_train_batch_end (line 454) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...
    method on_validation_batch_end (line 458) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ...
  class CUDACallback (line 466) | class CUDACallback(Callback):
    method on_train_epoch_start (line 468) | def on_train_epoch_start(self, trainer, pl_module):
    method on_train_epoch_end (line 474) | def on_train_epoch_end(self, trainer, pl_module):
  class ModeSwapCallback (line 488) | class ModeSwapCallback(Callback):
    method __init__ (line 490) | def __init__(self, swap_step=2000):
    method on_train_epoch_start (line 495) | def on_train_epoch_start(self, trainer, pl_module):
  function melk (line 768) | def melk(*args, **kwargs):
  function divein (line 776) | def divein(*args, **kwargs):

FILE: 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: scripts/score.py
  function _transform (line 26) | def _transform():
  function _convert_image_to_rgb (line 35) | def _convert_image_to_rgb(image):

FILE: scripts/stable_txt2img_guidance.py
  function chunk (line 20) | def chunk(it, size):
  function load_model_from_config (line 25) | def load_model_from_config(config, ckpt, verbose=False):
  function main (line 45) | def main():

FILE: scripts/stable_txt2img_multi_guidance.py
  function chunk (line 20) | def chunk(it, size):
  function load_model_from_config (line 25) | def load_model_from_config(config, ckpt, verbose=False):
  function main (line 45) | def main():