SYMBOL INDEX (144 symbols across 20 files)

FILE: deepfloyd_if/model/gaussian_diffusion.py
  function get_named_beta_schedule (line 17) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
  function betas_for_alpha_bar (line 43) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  class ModelMeanType (line 62) | class ModelMeanType(enum.Enum):
  class ModelVarType (line 72) | class ModelVarType(enum.Enum):
  class LossType (line 85) | class LossType(enum.Enum):
    method is_vb (line 93) | def is_vb(self):
  class GaussianDiffusion (line 97) | class GaussianDiffusion:
    method __init__ (line 112) | def __init__(
    method dynamic_thresholding (line 165) | def dynamic_thresholding(self, x, p=0.995, c=1.7):
    method q_mean_variance (line 184) | def q_mean_variance(self, x_start, t):
    method q_sample (line 200) | def q_sample(self, x_start, t, noise=None):
    method q_posterior_mean_variance (line 218) | def q_posterior_mean_variance(self, x_start, x_t, t):
    method p_mean_variance (line 240) | def p_mean_variance(
    method _predict_xstart_from_eps (line 337) | def _predict_xstart_from_eps(self, x_t, t, eps):
    method _predict_xstart_from_xprev (line 344) | def _predict_xstart_from_xprev(self, x_t, t, xprev):
    method _predict_eps_from_xstart (line 354) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method _scale_timesteps (line 360) | def _scale_timesteps(self, t):
    method p_sample (line 365) | def p_sample(
    method p_sample_loop (line 404) | def p_sample_loop(
    method p_sample_loop_progressive (line 454) | def p_sample_loop_progressive(
    method ddim_sample (line 507) | def ddim_sample(
    method ddim_reverse_sample (line 555) | def ddim_reverse_sample(
    method ddim_sample_loop (line 597) | def ddim_sample_loop(
    method ddim_sample_loop_progressive (line 635) | def ddim_sample_loop_progressive(
    method _vb_terms_bpd (line 686) | def _vb_terms_bpd(
    method training_losses (line 719) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
    method _prior_bpd (line 793) | def _prior_bpd(self, x_start):
    method calc_bpd_loop (line 809) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
  function _extract_into_tensor (line 865) | def _extract_into_tensor(arr, timesteps, broadcast_shape):

FILE: deepfloyd_if/model/losses.py
  function normal_kl (line 12) | def normal_kl(mean1, logvar1, mean2, logvar2):
  function approx_standard_normal_cdf (line 41) | def approx_standard_normal_cdf(x):
  function discretized_gaussian_log_likelihood (line 49) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):

FILE: deepfloyd_if/model/nn.py
  function mean_flat (line 10) | def mean_flat(tensor):
  function gelu (line 17) | def gelu(x):
  function gelu_jit (line 22) | def gelu_jit(x):
  class GELUJit (line 27) | class GELUJit(torch.nn.Module):
    method forward (line 28) | def forward(self, input: Tensor) -> Tensor:
  function get_activation (line 32) | def get_activation(activation):
  class GroupNorm32 (line 45) | class GroupNorm32(nn.GroupNorm):
    method __init__ (line 46) | def __init__(self, num_groups, num_channels, eps=1e-5, dtype=None):
    method forward (line 49) | def forward(self, x):
  class AttentionPooling (line 54) | class AttentionPooling(nn.Module):
    method __init__ (line 56) | def __init__(self, num_heads, embed_dim, dtype=None):
    method forward (line 66) | def forward(self, x):
  function conv_nd (line 105) | def conv_nd(dims, *args, **kwargs):
  function linear (line 118) | def linear(*args, **kwargs):
  function avg_pool_nd (line 125) | def avg_pool_nd(dims, *args, **kwargs):
  function zero_module (line 138) | def zero_module(module):
  function scale_module (line 147) | def scale_module(module, scale):
  function normalization (line 156) | def normalization(channels, dtype=None):
  function timestep_embedding (line 165) | def timestep_embedding(timesteps, dim, max_period=10000, dtype=None):
  function attention (line 187) | def attention(q, k, v, d_k):

FILE: deepfloyd_if/model/resample.py
  class ScheduleSampler (line 8) | class ScheduleSampler(ABC):
    method weights (line 19) | def weights(self):
    method sample (line 25) | def sample(self, batch_size, device):
  class UniformSampler (line 43) | class UniformSampler(ScheduleSampler):
    method __init__ (line 44) | def __init__(self, num_timesteps):
    method weights (line 47) | def weights(self):
  class StaticSampler (line 51) | class StaticSampler(ABC):
    method sample (line 53) | def sample(self, batch_size, device, static_step=100):

FILE: deepfloyd_if/model/respace.py
  function create_gaussian_diffusion (line 8) | def create_gaussian_diffusion(
  function space_timesteps (line 49) | def space_timesteps(num_timesteps, section_counts):
  class SpacedDiffusion (line 108) | class SpacedDiffusion(gd.GaussianDiffusion):
    method __init__ (line 116) | def __init__(self, use_timesteps, **kwargs):
    method p_mean_variance (line 132) | def p_mean_variance(
    method training_losses (line 137) | def training_losses(
    method _wrap_model (line 142) | def _wrap_model(self, model):
    method _scale_timesteps (line 149) | def _scale_timesteps(self, t):
  class _WrappedModel (line 154) | class _WrappedModel:
    method __init__ (line 155) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu...
    method __call__ (line 161) | def __call__(self, x, ts, **kwargs):

FILE: deepfloyd_if/model/unet.py
  class TimestepBlock (line 20) | class TimestepBlock(nn.Module):
    method forward (line 26) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 32) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 38) | def forward(self, x, emb, encoder_out=None):
  class Upsample (line 49) | class Upsample(nn.Module):
    method __init__ (line 58) | def __init__(self, channels, use_conv, dims=2, out_channels=None, dtyp...
    method forward (line 68) | def forward(self, x):
  class Downsample (line 83) | class Downsample(nn.Module):
    method __init__ (line 92) | def __init__(self, channels, use_conv, dims=2, out_channels=None, dtyp...
    method forward (line 106) | def forward(self, x):
  class ResBlock (line 111) | class ResBlock(TimestepBlock):
    method __init__ (line 126) | def __init__(
    method forward (line 192) | def forward(self, x, emb):
  class AttentionBlock (line 225) | class AttentionBlock(nn.Module):
    method __init__ (line 232) | def __init__(
    method forward (line 265) | def forward(self, x, encoder_out=None):
  class QKVAttention (line 280) | class QKVAttention(nn.Module):
    method __init__ (line 285) | def __init__(self, n_heads, disable_self_attention=False):
    method forward (line 290) | def forward(self, qkv, encoder_kv=None):
  class UNetModel (line 326) | class UNetModel(nn.Module):
    method __init__ (line 353) | def __init__(
    method forward (line 628) | def forward(self, x, timesteps, text_emb, timestep_text_emb=None, aug_...
  class SuperResUNetModel (line 664) | class SuperResUNetModel(UNetModel):
    method __init__ (line 670) | def __init__(self, low_res_diffusion, interpolate_mode='bilinear', *ar...
    method forward (line 681) | def forward(self, x, timesteps, low_res, aug_level=None, **kwargs):

FILE: deepfloyd_if/modules/base.py
  class IFBaseModule (line 23) | class IFBaseModule:
    method __init__ (line 57) | def __init__(self, dir_or_name, device, pil_img_size=256, cache_dir=No...
    method use_diffusers (line 67) | def use_diffusers(self):
    method embeddings_to_image (line 70) | def embeddings_to_image(
    method load_conf (line 231) | def load_conf(self, dir_or_name, filename='config.yml'):
    method load_checkpoint (line 236) | def load_checkpoint(self, model, dir_or_name, filename='pytorch_model....
    method _get_path_or_download_file_from_hf (line 247) | def _get_path_or_download_file_from_hf(self, dir_or_name, filename):
    method get_diffusion (line 256) | def get_diffusion(self, timestep_respacing):
    method seed_everything (line 272) | def seed_everything(seed=None):
    method device_name (line 284) | def device_name(self):
    method to_images (line 291) | def to_images(self, generations, disable_watermark=False):
    method show (line 312) | def show(self, pil_images, nrow=None, size=10):
    method _clear_cache (line 330) | def _clear_cache(self):
    method _get_image_sizes (line 333) | def _get_image_sizes(self, low_res, img_size, aspect_ratio, img_scale):
    method __validate_generations (line 352) | def __validate_generations(self, generations):

FILE: deepfloyd_if/modules/stage_I.py
  class IFStageI (line 8) | class IFStageI(IFBaseModule):
    method __init__ (line 12) | def __init__(self, *args, model_kwargs=None, pil_img_size=64, **kwargs):
    method embeddings_to_image (line 27) | def embeddings_to_image(self, t5_embs, style_t5_embs=None, positive_t5...

FILE: deepfloyd_if/modules/stage_II.py
  class IFStageII (line 8) | class IFStageII(IFBaseModule):
    method __init__ (line 12) | def __init__(self, *args, model_kwargs=None, pil_img_size=256, **kwargs):
    method embeddings_to_image (line 21) | def embeddings_to_image(

FILE: deepfloyd_if/modules/stage_III.py
  class IFStageIII (line 8) | class IFStageIII(IFBaseModule):
    method __init__ (line 12) | def __init__(self, *args, model_kwargs=None, pil_img_size=1024, **kwar...
    method embeddings_to_image (line 21) | def embeddings_to_image(

FILE: deepfloyd_if/modules/stage_III_sd_x4.py
  class StableStageIII (line 11) | class StableStageIII(IFBaseModule):
    method __init__ (line 15) | def __init__(self, *args, model_kwargs=None, pil_img_size=1024, **kwar...
    method use_diffusers (line 41) | def use_diffusers(self):
    method embeddings_to_image (line 48) | def embeddings_to_image(

FILE: deepfloyd_if/modules/t5.py
  class T5Embedder (line 14) | class T5Embedder:
    method __init__ (line 19) | def __init__(self, device, dir_or_name='t5-v1_1-xxl', *, cache_dir=Non...
    method get_text_embeddings (line 87) | def get_text_embeddings(self, texts):
    method text_preprocessing (line 110) | def text_preprocessing(self, text):
    method basic_clean (line 120) | def basic_clean(text):
    method clean_caption (line 125) | def clean_caption(self, caption):

FILE: deepfloyd_if/modules/utils.py
  function predict_proba (line 6) | def predict_proba(X, weights, biases):
  function load_model_weights (line 12) | def load_model_weights(path):
  function clip_process_generations (line 17) | def clip_process_generations(generations):

FILE: deepfloyd_if/pipelines/dream.py
  function dream (line 7) | def dream(

FILE: deepfloyd_if/pipelines/inpainting.py
  function inpainting (line 10) | def inpainting(

FILE: deepfloyd_if/pipelines/style_transfer.py
  function style_transfer (line 11) | def style_transfer(

FILE: deepfloyd_if/pipelines/super_resolution.py
  function super_resolution (line 9) | def super_resolution(

FILE: deepfloyd_if/pipelines/utils.py
  function _prepare_pil_image (line 8) | def _prepare_pil_image(raw_pil_img, img_size):

FILE: deepfloyd_if/utils.py
  function drop_shadow (line 11) | def drop_shadow(image, offset=(5, 5), background=0xffffff, shadow=0x4444...
  function pil_list_to_torch_tensors (line 56) | def pil_list_to_torch_tensors(pil_images):

FILE: setup.py
  function read (line 7) | def read(filename):
  function get_requirements (line 13) | def get_requirements():
  function get_links (line 29) | def get_links():
  function get_version (line 36) | def get_version():