SYMBOL INDEX (430 symbols across 24 files)

FILE: lvdm/basics.py
  function disabled_train (line 14) | def disabled_train(self, mode=True):
  function zero_module (line 19) | def zero_module(module):
  function scale_module (line 27) | def scale_module(module, scale):
  function conv_nd (line 36) | def conv_nd(dims, *args, **kwargs):
  function linear (line 49) | def linear(*args, **kwargs):
  function avg_pool_nd (line 56) | def avg_pool_nd(dims, *args, **kwargs):
  function nonlinearity (line 69) | def nonlinearity(type='silu'):
  class GroupNormSpecific (line 76) | class GroupNormSpecific(nn.GroupNorm):
    method forward (line 77) | def forward(self, x):
  function normalization (line 81) | def normalization(channels, num_groups=32):
  class HybridConditioner (line 90) | class HybridConditioner(nn.Module):
    method __init__ (line 92) | def __init__(self, c_concat_config, c_crossattn_config):
    method forward (line 97) | def forward(self, c_concat, c_crossattn):

FILE: lvdm/common.py
  function gather_data (line 8) | def gather_data(data, return_np=True):
  function autocast (line 16) | def autocast(f):
  function extract_into_tensor (line 25) | def extract_into_tensor(a, t, x_shape):
  function noise_like (line 31) | def noise_like(shape, device, repeat=False):
  function default (line 37) | def default(val, d):
  function exists (line 42) | def exists(val):
  function identity (line 45) | def identity(*args, **kwargs):
  function uniq (line 48) | def uniq(arr):
  function mean_flat (line 51) | def mean_flat(tensor):
  function ismap (line 57) | def ismap(x):
  function isimage (line 62) | def isimage(x):
  function max_neg_value (line 67) | def max_neg_value(t):
  function shape_to_str (line 70) | def shape_to_str(x):
  function init_ (line 74) | def init_(tensor):
  function checkpoint (line 81) | def checkpoint(func, inputs, params, flag):

FILE: lvdm/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, noise=None):
    method kl (line 42) | def kl(self, other=None):
    method nll (line 56) | def nll(self, sample, dims=[1,2,3]):
    method mode (line 64) | def mode(self):
  function normal_kl (line 68) | def normal_kl(mean1, logvar1, mean2, logvar2):

FILE: lvdm/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: lvdm/models/autoencoder.py
  class AutoencoderKL (line 13) | class AutoencoderKL(pl.LightningModule):
    method __init__ (line 14) | def __init__(self,
    method init_test (line 51) | def init_test(self,):
    method init_from_ckpt (line 80) | def init_from_ckpt(self, path, ignore_keys=list()):
    method encode (line 97) | def encode(self, x, **kwargs):
    method decode (line 104) | def decode(self, z, **kwargs):
    method forward (line 109) | def forward(self, input, sample_posterior=True):
    method get_input (line 118) | def get_input(self, batch, k):
    method training_step (line 128) | def training_step(self, batch, batch_idx, optimizer_idx):
    method validation_step (line 149) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 163) | def configure_optimizers(self):
    method get_last_layer (line 174) | def get_last_layer(self):
    method log_images (line 178) | def log_images(self, batch, only_inputs=False, **kwargs):
    method to_rgb (line 194) | def to_rgb(self, x):
  class IdentityFirstStage (line 202) | class IdentityFirstStage(torch.nn.Module):
    method __init__ (line 203) | def __init__(self, *args, vq_interface=False, **kwargs):
    method encode (line 207) | def encode(self, x, *args, **kwargs):
    method decode (line 210) | def decode(self, x, *args, **kwargs):
    method quantize (line 213) | def quantize(self, x, *args, **kwargs):
    method forward (line 218) | def forward(self, x, *args, **kwargs):

FILE: lvdm/models/ddpm3d.py
  class DDPM (line 38) | class DDPM(pl.LightningModule):
    method __init__ (line 40) | def __init__(self,
    method register_schedule (line 113) | def register_schedule(self, given_betas=None, beta_schedule="linear", ...
    method ema_scope (line 168) | def ema_scope(self, context=None):
    method init_from_ckpt (line 182) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method q_mean_variance (line 200) | def q_mean_variance(self, x_start, t):
    method predict_start_from_noise (line 212) | def predict_start_from_noise(self, x_t, t, noise):
    method q_posterior (line 218) | def q_posterior(self, x_start, x_t, t):
    method p_mean_variance (line 227) | def p_mean_variance(self, x, t, clip_denoised: bool):
    method p_sample (line 240) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
    method p_sample_loop (line 249) | def p_sample_loop(self, shape, return_intermediates=False):
    method sample (line 264) | def sample(self, batch_size=16, return_intermediates=False):
    method q_sample (line 270) | def q_sample(self, x_start, t, noise=None):
    method get_input (line 276) | def get_input(self, batch, k):
    method _get_rows_from_list (line 281) | def _get_rows_from_list(self, samples):
    method log_images (line 289) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non...
  class LatentDiffusion (line 327) | class LatentDiffusion(DDPM):
    method __init__ (line 329) | def __init__(self,
    method make_cond_schedule (line 407) | def make_cond_schedule(self, ):
    method q_sample (line 412) | def q_sample(self, x_start, t, noise=None):
    method _freeze_model (line 423) | def _freeze_model(self):
    method instantiate_first_stage (line 427) | def instantiate_first_stage(self, config):
    method instantiate_cond_stage (line 434) | def instantiate_cond_stage(self, config):
    method get_learned_conditioning (line 445) | def get_learned_conditioning(self, c):
    method get_first_stage_encoding (line 458) | def get_first_stage_encoding(self, encoder_posterior, noise=None):
    method encode_first_stage (line 468) | def encode_first_stage(self, x):
    method encode_first_stage_2DAE (line 485) | def encode_first_stage_2DAE(self, x):
    method decode_core (line 492) | def decode_core(self, z, **kwargs):
    method decode_first_stage (line 509) | def decode_first_stage(self, z, **kwargs):
    method apply_model (line 512) | def apply_model(self, x_noisy, t, cond, **kwargs):
    method _get_denoise_row_from_list (line 529) | def _get_denoise_row_from_list(self, samples, desc=''):
    method decode_first_stage_2DAE (line 556) | def decode_first_stage_2DAE(self, z, **kwargs):
    method p_mean_variance (line 565) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=Fals...
    method p_sample (line 591) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, r...
    method p_sample_loop (line 613) | def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=N...
  class LatentVisualDiffusion (line 660) | class LatentVisualDiffusion(LatentDiffusion):
    method __init__ (line 661) | def __init__(self, cond_img_config, finegrained=False, random_cond=Fal...
    method instantiate_img_embedder (line 669) | def instantiate_img_embedder(self, config, freeze=True):
    method init_projector (line 677) | def init_projector(self, use_finegrained, num_tokens, input_dim, cross...
    method get_image_embeds (line 689) | def get_image_embeds(self, batch_imgs):
  class DiffusionWrapper (line 696) | class DiffusionWrapper(pl.LightningModule):
    method __init__ (line 697) | def __init__(self, diff_model_config, conditioning_key):
    method forward (line 702) | def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,

FILE: lvdm/models/samplers/ddim.py
  class DDIMSampler (line 8) | class DDIMSampler(object):
    method __init__ (line 9) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 16) | def register_buffer(self, name, attr):
    method make_schedule (line 22) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 63) | def sample(self,
    method ddim_sampling (line 133) | def ddim_sampling(self, cond, shape,
    method p_sample_ddim (line 213) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin...
    method stochastic_encode (line 295) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
    method decode (line 317) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale...

FILE: lvdm/models/samplers/ddim_mp.py
  class DDIMSampler (line 8) | class DDIMSampler(object):
    method __init__ (line 9) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 16) | def register_buffer(self, name, attr):
    method make_schedule (line 22) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 63) | def sample(self,
    method ddim_sampling (line 133) | def ddim_sampling(self, cond, shape,
    method p_sample_ddim (line 214) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin...
    method stochastic_encode (line 299) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
    method decode (line 321) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale...

FILE: lvdm/models/utils_diffusion.py
  function timestep_embedding (line 8) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
  function make_beta_schedule (line 31) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
  function make_ddim_timesteps (line 56) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
  function make_ddim_sampling_parameters (line 73) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
  function betas_for_alpha_bar (line 88) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...

FILE: lvdm/modules/attention.py
  class RelativePosition (line 21) | class RelativePosition(nn.Module):
    method __init__ (line 24) | def __init__(self, num_units, max_relative_position):
    method forward (line 31) | def forward(self, length_q, length_k):
  class CrossAttention (line 43) | class CrossAttention(nn.Module):
    method __init__ (line 45) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 76) | def forward(self, x, context=None, mask=None):
    method efficient_forward (line 129) | def efficient_forward(self, x, context=None, mask=None):
  class BasicTransformerBlock (line 187) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 189) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 204) | def forward(self, x, context=None, mask=None):
    method _forward (line 216) | def _forward(self, x, context=None, mask=None):
  class SpatialTransformer (line 223) | class SpatialTransformer(nn.Module):
    method __init__ (line 233) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ...
    method forward (line 262) | def forward(self, x, context=None):
  class TemporalTransformer (line 281) | class TemporalTransformer(nn.Module):
    method __init__ (line 288) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ...
    method forward (line 331) | def forward(self, x, context=None):
  class GEGLU (line 376) | class GEGLU(nn.Module):
    method __init__ (line 377) | def __init__(self, dim_in, dim_out):
    method forward (line 381) | def forward(self, x):
  class FeedForward (line 386) | class FeedForward(nn.Module):
    method __init__ (line 387) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 402) | def forward(self, x):
  class LinearAttention (line 406) | class LinearAttention(nn.Module):
    method __init__ (line 407) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 414) | def forward(self, x):
  class SpatialSelfAttention (line 425) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 426) | def __init__(self, in_channels):
    method forward (line 452) | def forward(self, x):

FILE: lvdm/modules/attention_freenoise.py
  function generate_weight_sequence (line 21) | def generate_weight_sequence(n):
  class RelativePosition (line 30) | class RelativePosition(nn.Module):
    method __init__ (line 33) | def __init__(self, num_units, max_relative_position):
    method forward (line 40) | def forward(self, length_q, length_k):
  class CrossAttention (line 52) | class CrossAttention(nn.Module):
    method __init__ (line 54) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 87) | def forward(self, x, context=None, mask=None, context_next=None, use_i...
    method efficient_forward (line 202) | def efficient_forward(self, x, context=None, mask=None, context_next=N...
  class BasicTransformerBlock (line 274) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 276) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 291) | def forward(self, x, context=None, mask=None, context_next=None, use_i...
    method _forward (line 304) | def _forward(self, x, context=None, mask=None, context_next=None, use_...
  class SpatialTransformer (line 311) | class SpatialTransformer(nn.Module):
    method __init__ (line 321) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ...
    method forward (line 351) | def forward(self, x, context=None, **kwargs):
  class TemporalTransformer (line 370) | class TemporalTransformer(nn.Module):
    method __init__ (line 377) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ...
    method forward (line 421) | def forward(self, x, context=None, **kwargs):
  class GEGLU (line 466) | class GEGLU(nn.Module):
    method __init__ (line 467) | def __init__(self, dim_in, dim_out):
    method forward (line 471) | def forward(self, x):
  class FeedForward (line 476) | class FeedForward(nn.Module):
    method __init__ (line 477) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 492) | def forward(self, x):
  class LinearAttention (line 496) | class LinearAttention(nn.Module):
    method __init__ (line 497) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 504) | def forward(self, x):
  class SpatialSelfAttention (line 515) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 516) | def __init__(self, in_channels):
    method forward (line 542) | def forward(self, x, **kwargs):

FILE: lvdm/modules/encoders/condition.py
  class AbstractEncoder (line 10) | class AbstractEncoder(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method encode (line 14) | def encode(self, *args, **kwargs):
  class IdentityEncoder (line 18) | class IdentityEncoder(AbstractEncoder):
    method encode (line 20) | def encode(self, x):
  class ClassEmbedder (line 24) | class ClassEmbedder(nn.Module):
    method __init__ (line 25) | def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
    method forward (line 32) | def forward(self, batch, key=None, disable_dropout=False):
    method get_unconditional_conditioning (line 44) | def get_unconditional_conditioning(self, bs, device="cuda"):
  function disabled_train (line 51) | def disabled_train(self, mode=True):
  class FrozenT5Embedder (line 57) | class FrozenT5Embedder(AbstractEncoder):
    method __init__ (line 60) | def __init__(self, version="google/t5-v1_1-large", device="cuda", max_...
    method freeze (line 70) | def freeze(self):
    method forward (line 76) | def forward(self, text):
    method encode (line 85) | def encode(self, text):
  class FrozenCLIPEmbedder (line 89) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 97) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu...
    method freeze (line 113) | def freeze(self):
    method forward (line 119) | def forward(self, text):
    method encode (line 132) | def encode(self, text):
  class ClipImageEmbedder (line 136) | class ClipImageEmbedder(nn.Module):
    method __init__ (line 137) | def __init__(
    method preprocess (line 155) | def preprocess(self, x):
    method forward (line 165) | def forward(self, x, no_dropout=False):
  class FrozenOpenCLIPEmbedder (line 174) | class FrozenOpenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 184) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic...
    method freeze (line 204) | def freeze(self):
    method forward (line 209) | def forward(self, text):
    method encode_with_transformer (line 215) | def encode_with_transformer(self, text):
    method text_transformer_forward (line 224) | def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
    method encode (line 234) | def encode(self, text):
  class FrozenOpenCLIPImageEmbedder (line 238) | class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
    method __init__ (line 243) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic...
    method preprocess (line 266) | def preprocess(self, x):
    method freeze (line 276) | def freeze(self):
    method forward (line 282) | def forward(self, image, no_dropout=False):
    method encode_with_vision_transformer (line 288) | def encode_with_vision_transformer(self, img):
    method encode (line 293) | def encode(self, text):
  class FrozenOpenCLIPImageEmbedderV2 (line 298) | class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
    method __init__ (line 303) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic...
    method preprocess (line 324) | def preprocess(self, x):
    method freeze (line 334) | def freeze(self):
    method forward (line 339) | def forward(self, image, no_dropout=False):
    method encode_with_vision_transformer (line 344) | def encode_with_vision_transformer(self, x):
  class FrozenCLIPT5Encoder (line 377) | class FrozenCLIPT5Encoder(AbstractEncoder):
    method __init__ (line 378) | def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_ve...
    method encode (line 386) | def encode(self, text):
    method forward (line 389) | def forward(self, text):

FILE: lvdm/modules/encoders/ip_resampler.py
  class ImageProjModel (line 7) | class ImageProjModel(nn.Module):
    method __init__ (line 9) | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024,...
    method forward (line 16) | def forward(self, image_embeds):
  function FeedForward (line 24) | def FeedForward(dim, mult=4):
  function reshape_tensor (line 34) | def reshape_tensor(x, heads):
  class PerceiverAttention (line 45) | class PerceiverAttention(nn.Module):
    method __init__ (line 46) | def __init__(self, *, dim, dim_head=64, heads=8):
    method forward (line 61) | def forward(self, x, latents):
  class Resampler (line 93) | class Resampler(nn.Module):
    method __init__ (line 94) | def __init__(
    method forward (line 125) | def forward(self, x):

FILE: lvdm/modules/networks/ae_modules.py
  function nonlinearity (line 10) | def nonlinearity(x):
  function Normalize (line 15) | def Normalize(in_channels, num_groups=32):
  class LinAttnBlock (line 20) | class LinAttnBlock(LinearAttention):
    method __init__ (line 22) | def __init__(self, in_channels):
  class AttnBlock (line 26) | class AttnBlock(nn.Module):
    method __init__ (line 27) | def __init__(self, in_channels):
    method forward (line 53) | def forward(self, x):
  function make_attn (line 80) | def make_attn(in_channels, attn_type="vanilla"):
  class Downsample (line 90) | class Downsample(nn.Module):
    method __init__ (line 91) | def __init__(self, in_channels, with_conv):
    method forward (line 102) | def forward(self, x):
  class Upsample (line 111) | class Upsample(nn.Module):
    method __init__ (line 112) | def __init__(self, in_channels, with_conv):
    method forward (line 123) | def forward(self, x):
  function get_timestep_embedding (line 129) | def get_timestep_embedding(timesteps, embedding_dim):
  class ResnetBlock (line 151) | class ResnetBlock(nn.Module):
    method __init__ (line 152) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 190) | def forward(self, x, temb):
  class Model (line 212) | class Model(nn.Module):
    method __init__ (line 213) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 312) | def forward(self, x, t=None, context=None):
    method get_last_layer (line 360) | def get_last_layer(self):
  class Encoder (line 364) | class Encoder(nn.Module):
    method __init__ (line 365) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 430) | def forward(self, x):
  class Decoder (line 466) | class Decoder(nn.Module):
    method __init__ (line 467) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 539) | def forward(self, z):
  class SimpleDecoder (line 581) | class SimpleDecoder(nn.Module):
    method __init__ (line 582) | def __init__(self, in_channels, out_channels, *args, **kwargs):
    method forward (line 604) | def forward(self, x):
  class UpsampleDecoder (line 617) | class UpsampleDecoder(nn.Module):
    method __init__ (line 618) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso...
    method forward (line 651) | def forward(self, x):
  class LatentRescaler (line 665) | class LatentRescaler(nn.Module):
    method __init__ (line 666) | def __init__(self, factor, in_channels, mid_channels, out_channels, de...
    method forward (line 690) | def forward(self, x):
  class MergedRescaleEncoder (line 702) | class MergedRescaleEncoder(nn.Module):
    method __init__ (line 703) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
    method forward (line 715) | def forward(self, x):
  class MergedRescaleDecoder (line 721) | class MergedRescaleDecoder(nn.Module):
    method __init__ (line 722) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att...
    method forward (line 732) | def forward(self, x):
  class Upsampler (line 738) | class Upsampler(nn.Module):
    method __init__ (line 739) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu...
    method forward (line 751) | def forward(self, x):
  class Resize (line 757) | class Resize(nn.Module):
    method __init__ (line 758) | def __init__(self, in_channels=None, learned=False, mode="bilinear"):
    method forward (line 773) | def forward(self, x, scale_factor=1.0):
  class FirstStagePostProcessor (line 780) | class FirstStagePostProcessor(nn.Module):
    method __init__ (line 782) | def __init__(self, ch_mult:list, in_channels,
    method instantiate_pretrained (line 817) | def instantiate_pretrained(self, config):
    method encode_with_pretrained (line 826) | def encode_with_pretrained(self,x):
    method forward (line 832) | def forward(self,x):

FILE: lvdm/modules/networks/openaimodel3d.py
  class TimestepBlock (line 19) | class TimestepBlock(nn.Module):
    method forward (line 24) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 30) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 36) | def forward(self, x, emb, context=None, batch_size=None):
  class Downsample (line 51) | class Downsample(nn.Module):
    method __init__ (line 60) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 75) | def forward(self, x):
  class Upsample (line 80) | class Upsample(nn.Module):
    method __init__ (line 89) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 98) | def forward(self, x):
  class ResBlock (line 109) | class ResBlock(TimestepBlock):
    method __init__ (line 124) | def __init__(
    method forward (line 195) | def forward(self, x, emb,  batch_size=None):
    method _forward (line 208) | def _forward(self, x, emb,  batch_size=None,):
  class TemporalConvBlock (line 237) | class TemporalConvBlock(nn.Module):
    method __init__ (line 242) | def __init__(self, in_channels, out_channels=None, dropout=0.0, spatia...
    method forward (line 269) | def forward(self, x):
  class UNetModel (line 279) | class UNetModel(nn.Module):
    method __init__ (line 307) | def __init__(self,
    method forward (line 534) | def forward(self, x, timesteps, context=None, features_adapter=None, f...

FILE: lvdm/modules/networks/openaimodel3d_freenoise.py
  class TimestepBlock (line 19) | class TimestepBlock(nn.Module):
    method forward (line 24) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 30) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 36) | def forward(self, x, emb, context=None, batch_size=None, use_injection...
  class Downsample (line 51) | class Downsample(nn.Module):
    method __init__ (line 60) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 75) | def forward(self, x):
  class Upsample (line 80) | class Upsample(nn.Module):
    method __init__ (line 89) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 98) | def forward(self, x):
  class ResBlock (line 109) | class ResBlock(TimestepBlock):
    method __init__ (line 124) | def __init__(
    method forward (line 195) | def forward(self, x, emb,  batch_size=None):
    method _forward (line 208) | def _forward(self, x, emb,  batch_size=None,):
  class TemporalConvBlock (line 237) | class TemporalConvBlock(nn.Module):
    method __init__ (line 242) | def __init__(self, in_channels, out_channels=None, dropout=0.0, spatia...
    method forward (line 269) | def forward(self, x):
  class UNetModel (line 279) | class UNetModel(nn.Module):
    method __init__ (line 307) | def __init__(self,
    method forward (line 534) | def forward(self, x, timesteps, context=None, features_adapter=None, f...

FILE: lvdm/modules/x_transformer.py
  class AbsolutePositionalEmbedding (line 24) | class AbsolutePositionalEmbedding(nn.Module):
    method __init__ (line 25) | def __init__(self, dim, max_seq_len):
    method init_ (line 30) | def init_(self):
    method forward (line 33) | def forward(self, x):
  class FixedPositionalEmbedding (line 38) | class FixedPositionalEmbedding(nn.Module):
    method __init__ (line 39) | def __init__(self, dim):
    method forward (line 44) | def forward(self, x, seq_dim=1, offset=0):
  function exists (line 53) | def exists(val):
  function default (line 57) | def default(val, d):
  function always (line 63) | def always(val):
  function not_equals (line 69) | def not_equals(val):
  function equals (line 75) | def equals(val):
  function max_neg_value (line 81) | def max_neg_value(tensor):
  function pick_and_pop (line 87) | def pick_and_pop(keys, d):
  function group_dict_by_key (line 92) | def group_dict_by_key(cond, d):
  function string_begins_with (line 101) | def string_begins_with(prefix, str):
  function group_by_key_prefix (line 105) | def group_by_key_prefix(prefix, d):
  function groupby_prefix_and_trim (line 109) | def groupby_prefix_and_trim(prefix, d):
  class Scale (line 116) | class Scale(nn.Module):
    method __init__ (line 117) | def __init__(self, value, fn):
    method forward (line 122) | def forward(self, x, **kwargs):
  class Rezero (line 127) | class Rezero(nn.Module):
    method __init__ (line 128) | def __init__(self, fn):
    method forward (line 133) | def forward(self, x, **kwargs):
  class ScaleNorm (line 138) | class ScaleNorm(nn.Module):
    method __init__ (line 139) | def __init__(self, dim, eps=1e-5):
    method forward (line 145) | def forward(self, x):
  class RMSNorm (line 150) | class RMSNorm(nn.Module):
    method __init__ (line 151) | def __init__(self, dim, eps=1e-8):
    method forward (line 157) | def forward(self, x):
  class Residual (line 162) | class Residual(nn.Module):
    method forward (line 163) | def forward(self, x, residual):
  class GRUGating (line 167) | class GRUGating(nn.Module):
    method __init__ (line 168) | def __init__(self, dim):
    method forward (line 172) | def forward(self, x, residual):
  class GEGLU (line 183) | class GEGLU(nn.Module):
    method __init__ (line 184) | def __init__(self, dim_in, dim_out):
    method forward (line 188) | def forward(self, x):
  class FeedForward (line 193) | class FeedForward(nn.Module):
    method __init__ (line 194) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 209) | def forward(self, x):
  class Attention (line 214) | class Attention(nn.Module):
    method __init__ (line 215) | def __init__(
    method forward (line 267) | def forward(
  class AttentionLayers (line 369) | class AttentionLayers(nn.Module):
    method __init__ (line 370) | def __init__(
    method forward (line 480) | def forward(
  class Encoder (line 540) | class Encoder(AttentionLayers):
    method __init__ (line 541) | def __init__(self, **kwargs):
  class TransformerWrapper (line 547) | class TransformerWrapper(nn.Module):
    method __init__ (line 548) | def __init__(
    method init_ (line 594) | def init_(self):
    method forward (line 597) | def forward(

FILE: predict.py
  class Predictor (line 26) | class Predictor(BasePredictor):
    method setup (line 27) | def setup(self) -> None:
    method predict (line 49) | def predict(

FILE: scripts/evaluation/ddp_wrapper.py
  function setup_dist (line 8) | def setup_dist(local_rank):
  function get_dist_info (line 15) | def get_dist_info():

FILE: scripts/evaluation/funcs.py
  function get_views (line 13) | def get_views(video_length, window_size=16, stride=4):
  function batch_ddim_sampling (line 22) | def batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_step...
  function batch_ddim_sampling_freenoise (line 79) | def batch_ddim_sampling_freenoise(model, cond, noise_shape, n_samples=1,...
  function batch_ddim_sampling_freenoise_mp (line 139) | def batch_ddim_sampling_freenoise_mp(model, cond, noise_shape, n_samples...
  function get_filelist (line 214) | def get_filelist(data_dir, ext='*'):
  function get_dirlist (line 219) | def get_dirlist(path):
  function load_model_checkpoint (line 231) | def load_model_checkpoint(model, ckpt):
  function load_prompts (line 250) | def load_prompts(prompt_file):
  function load_prompts_mp (line 260) | def load_prompts_mp(prompt_file):
  function load_video_batch (line 277) | def load_video_batch(filepath_list, frame_stride, video_size=(256,256), ...
  function load_image_batch (line 316) | def load_image_batch(filepath_list, image_size=(256,256)):
  function save_videos (line 340) | def save_videos(batch_tensors, savedir, filenames, fps=10):

FILE: scripts/evaluation/inference.py
  function get_parser (line 18) | def get_parser():
  function run_inference (line 42) | def run_inference(args, gpu_num, gpu_no, **kwargs):

FILE: scripts/evaluation/inference_freenoise.py
  function get_parser (line 18) | def get_parser():
  function run_inference (line 45) | def run_inference(args, gpu_num, gpu_no, **kwargs):

FILE: scripts/evaluation/inference_freenoise_mp.py
  function get_parser (line 18) | def get_parser():
  function run_inference (line 45) | def run_inference(args, gpu_num, gpu_no, **kwargs):

FILE: utils/utils.py
  function count_params (line 8) | def count_params(model, verbose=False):
  function check_istarget (line 15) | def check_istarget(name, para_list):
  function instantiate_from_config (line 27) | def instantiate_from_config(config):
  function get_obj_from_str (line 37) | def get_obj_from_str(string, reload=False):
  function load_npz_from_dir (line 45) | def load_npz_from_dir(data_dir):
  function load_npz_from_paths (line 51) | def load_npz_from_paths(data_paths):
  function resize_numpy_image (line 57) | def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edg...
  function setup_dist (line 70) | def setup_dist(args):