SYMBOL INDEX (269 symbols across 23 files)

FILE: datasets/video_transforms.py
  function _is_tensor_video_clip (line 7) | def _is_tensor_video_clip(clip):
  function center_crop_arr (line 17) | def center_crop_arr(pil_image, image_size):
  function crop (line 38) | def crop(clip, i, j, h, w):
  function resize (line 48) | def resize(clip, target_size, interpolation_mode):
  function resize_scale (line 53) | def resize_scale(clip, target_size, interpolation_mode):
  function resize_with_scale_factor (line 60) | def resize_with_scale_factor(clip, scale_factor, interpolation_mode):
  function resize_scale_with_height (line 63) | def resize_scale_with_height(clip, target_size, interpolation_mode):
  function resize_scale_with_weight (line 68) | def resize_scale_with_weight(clip, target_size, interpolation_mode):
  function resized_crop (line 74) | def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
  function center_crop (line 94) | def center_crop(clip, crop_size):
  function center_crop_using_short_edge (line 109) | def center_crop_using_short_edge(clip):
  function random_shift_crop (line 124) | def random_shift_crop(clip):
  function to_tensor (line 146) | def to_tensor(clip):
  function normalize (line 162) | def normalize(clip, mean, std, inplace=False):
  function hflip (line 182) | def hflip(clip):
  class RandomCropVideo (line 194) | class RandomCropVideo:
    method __init__ (line 195) | def __init__(self, size):
    method __call__ (line 201) | def __call__(self, clip):
    method get_params (line 212) | def get_params(self, clip):
    method __repr__ (line 227) | def __repr__(self) -> str:
  class CenterCropResizeVideo (line 230) | class CenterCropResizeVideo:
    method __init__ (line 235) | def __init__(
    method __call__ (line 250) | def __call__(self, clip):
    method __repr__ (line 264) | def __repr__(self) -> str:
  class CenterCropVideo (line 268) | class CenterCropVideo:
    method __init__ (line 269) | def __init__(
    method __call__ (line 284) | def __call__(self, clip):
    method __repr__ (line 295) | def __repr__(self) -> str:
  class NormalizeVideo (line 299) | class NormalizeVideo:
    method __init__ (line 308) | def __init__(self, mean, std, inplace=False):
    method __call__ (line 313) | def __call__(self, clip):
    method __repr__ (line 320) | def __repr__(self) -> str:
  class ToTensorVideo (line 324) | class ToTensorVideo:
    method __init__ (line 330) | def __init__(self):
    method __call__ (line 333) | def __call__(self, clip):
    method __repr__ (line 342) | def __repr__(self) -> str:
  class ResizeVideo (line 346) | class ResizeVideo():
    method __init__ (line 351) | def __init__(
    method __call__ (line 366) | def __call__(self, clip):
    method __repr__ (line 377) | def __repr__(self) -> str:

FILE: diffusion/__init__.py
  function create_diffusion (line 10) | def create_diffusion(

FILE: diffusion/diffusion_utils.py
  function normal_kl (line 10) | def normal_kl(mean1, logvar1, mean2, logvar2):
  function approx_standard_normal_cdf (line 39) | def approx_standard_normal_cdf(x):
  function continuous_gaussian_log_likelihood (line 47) | def continuous_gaussian_log_likelihood(x, *, means, log_scales):
  function discretized_gaussian_log_likelihood (line 62) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):

FILE: diffusion/gaussian_diffusion.py
  function mean_flat (line 16) | def mean_flat(tensor):
  class ModelMeanType (line 23) | class ModelMeanType(enum.Enum):
  class ModelVarType (line 33) | class ModelVarType(enum.Enum):
  class LossType (line 46) | class LossType(enum.Enum):
    method is_vb (line 54) | def is_vb(self):
  function _warmup_beta (line 58) | def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_f...
  function get_beta_schedule (line 65) | def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffus...
  function get_named_beta_schedule (line 98) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
  function betas_for_alpha_bar (line 128) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  class GaussianDiffusion (line 147) | class GaussianDiffusion:
    method __init__ (line 156) | def __init__(
    method q_mean_variance (line 206) | def q_mean_variance(self, x_start, t):
    method q_sample (line 218) | def q_sample(self, x_start, t, noise=None):
    method q_posterior_mean_variance (line 235) | def q_posterior_mean_variance(self, x_start, x_t, t):
    method p_mean_variance (line 257) | def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn...
    method _predict_xstart_from_eps (line 346) | def _predict_xstart_from_eps(self, x_t, t, eps):
    method _predict_eps_from_xstart (line 353) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method condition_mean (line 358) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
    method condition_score (line 370) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
    method p_sample (line 388) | def p_sample(
    method p_sample_loop (line 437) | def p_sample_loop(
    method p_sample_loop_progressive (line 488) | def p_sample_loop_progressive(
    method ddim_sample (line 543) | def ddim_sample(
    method ddim_reverse_sample (line 598) | def ddim_reverse_sample(
    method ddim_sample_loop (line 636) | def ddim_sample_loop(
    method ddim_sample_loop_progressive (line 675) | def ddim_sample_loop_progressive(
    method _vb_terms_bpd (line 730) | def _vb_terms_bpd(
    method training_losses (line 763) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
    method _prior_bpd (line 847) | def _prior_bpd(self, x_start):
    method calc_bpd_loop (line 863) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
  function _extract_into_tensor (line 919) | def _extract_into_tensor(arr, timesteps, broadcast_shape):

FILE: diffusion/respace.py
  function space_timesteps (line 12) | def space_timesteps(num_timesteps, section_counts):
  class SpacedDiffusion (line 65) | class SpacedDiffusion(GaussianDiffusion):
    method __init__ (line 73) | def __init__(self, use_timesteps, **kwargs):
    method p_mean_variance (line 89) | def p_mean_variance(
    method training_losses (line 95) | def training_losses(
    method condition_mean (line 100) | def condition_mean(self, cond_fn, *args, **kwargs):
    method condition_score (line 103) | def condition_score(self, cond_fn, *args, **kwargs):
    method _wrap_model (line 106) | def _wrap_model(self, model):
    method _scale_timesteps (line 113) | def _scale_timesteps(self, t):
  class _WrappedModel (line 118) | class _WrappedModel:
    method __init__ (line 119) | def __init__(self, model, timestep_map, original_num_steps):
    method __call__ (line 125) | def __call__(self, x, ts, **kwargs):

FILE: diffusion/timestep_sampler.py
  function create_named_schedule_sampler (line 13) | def create_named_schedule_sampler(name, diffusion):
  class ScheduleSampler (line 27) | class ScheduleSampler(ABC):
    method weights (line 38) | def weights(self):
    method sample (line 44) | def sample(self, batch_size, device):
  class UniformSampler (line 62) | class UniformSampler(ScheduleSampler):
    method __init__ (line 63) | def __init__(self, diffusion):
    method weights (line 67) | def weights(self):
  class LossAwareSampler (line 71) | class LossAwareSampler(ScheduleSampler):
    method update_with_local_losses (line 72) | def update_with_local_losses(self, local_ts, local_losses):
    method update_with_all_losses (line 106) | def update_with_all_losses(self, ts, losses):
  class LossSecondMomentResampler (line 120) | class LossSecondMomentResampler(LossAwareSampler):
    method __init__ (line 121) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
    method weights (line 130) | def weights(self):
    method update_with_all_losses (line 139) | def update_with_all_losses(self, ts, losses):
    method _warmed_up (line 149) | def _warmed_up(self):

FILE: models/__init__.py
  function customized_lr_scheduler (line 8) | def customized_lr_scheduler(optimizer, warmup_steps=5000): # 5000 from u...
  function get_lr_scheduler (line 18) | def get_lr_scheduler(optimizer, name, **kwargs):
  function get_models (line 27) | def get_models(args):

FILE: models/attention.py
  class Transformer3DModelOutput (line 28) | class Transformer3DModelOutput(BaseOutput):
  function exists (line 38) | def exists(x):
  class CrossAttention (line 42) | class CrossAttention(nn.Module):
    method __init__ (line 57) | def __init__(
    method ip_transform (line 115) | def ip_transform(self):
    method ip_train_set (line 121) | def ip_train_set(self):
    method set_scale (line 126) | def set_scale(self, scale):
    method reshape_heads_to_batch_dim (line 129) | def reshape_heads_to_batch_dim(self, tensor):
    method reshape_batch_dim_to_heads (line 136) | def reshape_batch_dim_to_heads(self, tensor):
    method reshape_for_scores (line 143) | def reshape_for_scores(self, tensor):
    method same_batch_dim_to_heads (line 152) | def same_batch_dim_to_heads(self, tensor):
    method set_attention_slice (line 157) | def set_attention_slice(self, slice_size):
    method forward (line 163) | def forward(self, hidden_states, encoder_hidden_states=None, attention...
    method _attention (line 248) | def _attention(self, query, key, value, attention_mask=None):
    method _sliced_attention (line 273) | def _sliced_attention(self, query, key, value, sequence_length, dim, a...
    method _memory_efficient_attention_xformers (line 316) | def _memory_efficient_attention_xformers(self, query, key, value, atte...
  class Transformer3DModel (line 326) | class Transformer3DModel(ModelMixin, ConfigMixin):
    method __init__ (line 328) | def __init__(
    method forward (line 390) | def forward(self, hidden_states, encoder_hidden_states=None, timestep=...
  class BasicTransformerBlock (line 472) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 473) | def __init__(
    method tca_transform (line 555) | def tca_transform(self):
    method set_use_memory_efficient_attention_xformers (line 570) | def set_use_memory_efficient_attention_xformers(self, use_memory_effic...
    method forward (line 598) | def forward(self, hidden_states, encoder_hidden_states=None, timestep=...
  class SparseCausalAttention (line 676) | class SparseCausalAttention(CrossAttention):
    method forward_video (line 677) | def forward_video(self, hidden_states, encoder_hidden_states=None, att...
    method forward_image (line 734) | def forward_image(self, hidden_states, encoder_hidden_states=None, att...
    method forward (line 793) | def forward(self, hidden_states, encoder_hidden_states=None, attention...
  class TemporalAttention (line 817) | class TemporalAttention(CrossAttention):
    method __init__ (line 818) | def __init__(self,
    method forward (line 835) | def forward(self, hidden_states, encoder_hidden_states=None, attention...
    method _attention (line 890) | def _attention(self, query, key, value, attention_mask=None, time_rel_...
  class RelativePositionBias (line 928) | class RelativePositionBias(nn.Module):
    method __init__ (line 929) | def __init__(
    method _relative_position_bucket (line 941) | def _relative_position_bucket(relative_position, num_buckets=32, max_d...
    method forward (line 960) | def forward(self, n, device):

FILE: models/clip.py
  class AbstractEncoder (line 24) | class AbstractEncoder(nn.Module):
    method __init__ (line 25) | def __init__(self):
    method encode (line 28) | def encode(self, *args, **kwargs):
  class FrozenCLIPEmbedder (line 32) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 35) | def __init__(self, path, device="cuda", max_length=77):
    method freeze (line 43) | def freeze(self):
    method forward (line 48) | def forward(self, text):
    method encode (line 57) | def encode(self, text):
  class TextEmbedder (line 61) | class TextEmbedder(nn.Module):
    method __init__ (line 65) | def __init__(self, path, dropout_prob=0.1):
    method token_drop (line 70) | def token_drop(self, text_prompts, force_drop_ids=None):
    method forward (line 83) | def forward(self, text_prompts, train, force_drop_ids=None):

FILE: models/resnet.py
  class InflatedConv3d (line 13) | class InflatedConv3d(nn.Conv2d):
    method forward (line 14) | def forward(self, x):
  class Upsample3D (line 24) | class Upsample3D(nn.Module):
    method __init__ (line 25) | def __init__(self, channels, use_conv=False, use_conv_transpose=False,...
    method forward (line 44) | def forward(self, hidden_states, output_size=None):
  class Downsample3D (line 79) | class Downsample3D(nn.Module):
    method __init__ (line 80) | def __init__(self, channels, use_conv=False, out_channels=None, paddin...
    method forward (line 102) | def forward(self, hidden_states):
  class ResnetBlock3D (line 113) | class ResnetBlock3D(nn.Module):
    method __init__ (line 114) | def __init__(
    method forward (line 177) | def forward(self, input_tensor, temb):
  class Mish (line 210) | class Mish(torch.nn.Module):
    method forward (line 211) | def forward(self, hidden_states):

FILE: models/unet.py
  class RelativePositionBias (line 54) | class RelativePositionBias(nn.Module):
    method __init__ (line 55) | def __init__(
    method _relative_position_bucket (line 67) | def _relative_position_bucket(relative_position, num_buckets=32, max_d...
    method forward (line 86) | def forward(self, n, device):
  class UNet3DConditionOutput (line 95) | class UNet3DConditionOutput(BaseOutput):
  class UNet3DConditionModel (line 99) | class UNet3DConditionModel(ModelMixin, ConfigMixin):
    method __init__ (line 103) | def __init__(
    method set_attention_slice (line 291) | def set_attention_slice(self, slice_size):
    method _set_gradient_checkpointing (line 356) | def _set_gradient_checkpointing(self, module, value=False):
    method forward (line 360) | def forward(
    method forward_with_cfg (line 519) | def forward_with_cfg(self,
    method from_pretrained_2d (line 547) | def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, use...

FILE: models/unet_blocks.py
  function get_down_block (line 17) | def get_down_block(
  function get_up_block (line 82) | def get_up_block(
  class UNetMidBlock3DCrossAttn (line 145) | class UNetMidBlock3DCrossAttn(nn.Module):
    method __init__ (line 146) | def __init__(
    method forward (line 226) | def forward(self, hidden_states, temb=None, encoder_hidden_states=None...
  class CrossAttnDownBlock3D (line 235) | class CrossAttnDownBlock3D(nn.Module):
    method __init__ (line 236) | def __init__(
    method forward (line 320) | def forward(self, hidden_states, temb=None, encoder_hidden_states=None...
  class DownBlock3D (line 365) | class DownBlock3D(nn.Module):
    method __init__ (line 366) | def __init__(
    method forward (line 417) | def forward(self, hidden_states, temb=None):
  class CrossAttnUpBlock3D (line 444) | class CrossAttnUpBlock3D(nn.Module):
    method __init__ (line 445) | def __init__(
    method forward (line 524) | def forward(
  class UpBlock3D (line 579) | class UpBlock3D(nn.Module):
    method __init__ (line 580) | def __init__(
    method forward (line 627) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, u...

FILE: models/utils.py
  function checkpoint (line 25) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 42) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 44) | def forward(ctx, run_function, length, *args):
    method backward (line 54) | def backward(ctx, *output_grads):
  function timestep_embedding (line 74) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
  function zero_module (line 97) | def zero_module(module):
  function scale_module (line 106) | def scale_module(module, scale):
  function mean_flat (line 115) | def mean_flat(tensor):
  function normalization (line 122) | def normalization(channels):
  class SiLU (line 132) | class SiLU(nn.Module):
    method forward (line 133) | def forward(self, x):
  class GroupNorm32 (line 137) | class GroupNorm32(nn.GroupNorm):
    method forward (line 138) | def forward(self, x):
  function conv_nd (line 141) | def conv_nd(dims, *args, **kwargs):
  function linear (line 154) | def linear(*args, **kwargs):
  function avg_pool_nd (line 161) | def avg_pool_nd(dims, *args, **kwargs):
  function noise_like (line 187) | def noise_like(shape, device, repeat=False):
  function count_flops_attn (line 192) | def count_flops_attn(model, _x, y):
  function count_params (line 211) | def count_params(model, verbose=False):

FILE: sample_scripts/vlog_read_script_sample.py
  function auto_inpainting (line 37) | def auto_inpainting(args,
  function main (line 110) | def main(args):

FILE: sample_scripts/vlog_write_script.py
  function main (line 18) | def main(args):

FILE: sample_scripts/with_mask_ref_sample.py
  function get_input (line 48) | def get_input(args):
  function auto_inpainting (line 120) | def auto_inpainting(args,
  function main (line 185) | def main(args):

FILE: sample_scripts/with_mask_sample.py
  function get_input (line 47) | def get_input(args):
  function auto_inpainting (line 119) | def auto_inpainting(args, video_input, masked_video, mask, prompt, vae, ...
  function main (line 176) | def main(args):

FILE: utils.py
  function fetch_files_by_numbers (line 20) | def fetch_files_by_numbers(start_number, count, file_list):
  function get_grad_norm (line 35) | def get_grad_norm(
  function clip_grad_norm_ (line 72) | def clip_grad_norm_(
  function separation_content_motion (line 129) | def separation_content_motion(video_clip):
  function get_experiment_dir (line 147) | def get_experiment_dir(root_dir, args):
  function create_logger (line 166) | def create_logger(logging_dir):
  function create_accelerate_logger (line 185) | def create_accelerate_logger(logging_dir, is_main_process=False):
  function create_tensorboard (line 204) | def create_tensorboard(tensorboard_dir):
  function write_tensorboard (line 214) | def write_tensorboard(writer, *args):
  function update_ema (line 227) | def update_ema(ema_model, model, decay=0.9999):
  function requires_grad (line 239) | def requires_grad(model, flag=True):
  function cleanup (line 246) | def cleanup():
  function setup_distributed (line 253) | def setup_distributed(backend="nccl", port=None):
  function save_video_grid (line 292) | def save_video_grid(video, nrow=None):
  function save_videos_grid_tav (line 312) | def save_videos_grid_tav(videos: torch.Tensor, path: str, rescale=False,...
  function collect_env (line 336) | def collect_env():
  function mask_generation_before (line 356) | def mask_generation_before(mask_type, shape, dtype, device, dropout_prob...

FILE: vlogger/STEB/model_transform.py
  function tca_transform_model (line 11) | def tca_transform_model(model):
  class ImageProjModel (line 32) | class ImageProjModel(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024,...
    method forward (line 42) | def forward(self, image_embeds):
  function ip_transform_model (line 49) | def ip_transform_model(model):
  function ip_scale_set (line 72) | def ip_scale_set(model, scale):
  function ip_train_set (line 93) | def ip_train_set(model):

FILE: vlogger/planning_utils/gpt4_utils.py
  function smart_openai_key (line 12) | def smart_openai_key():
  function json_completion (line 20) | def json_completion(prompt):
  function ExtractProtagonist (line 41) | def ExtractProtagonist(story, file_path):
  function ExtractAProtagonist (line 89) | def ExtractAProtagonist(story, file_path):
  function protagonist_place_reference (line 131) | def protagonist_place_reference(video_list, character_places):
  function protagonist_place_reference1 (line 193) | def protagonist_place_reference1(video_list, character_places, file_path):
  function split_story (line 260) | def split_story(story, file_path):
  function patch_story_scripts (line 309) | def patch_story_scripts(story, video_list, file_path):
  function refine_story_scripts (line 361) | def refine_story_scripts(video_list, file_path):
  function time_scripts (line 413) | def time_scripts(video_list, file_path):
  function translate_video_script (line 479) | def translate_video_script(video_list, file_path):
  function readscript (line 527) | def readscript(script_file_path):
  function readzhscript (line 537) | def readzhscript(zh_file_path):
  function readtimescript (line 547) | def readtimescript(time_file_path):
  function readprotagonistscript (line 558) | def readprotagonistscript(protagonist_file_path):
  function readreferencescript (line 568) | def readreferencescript(video_list, character_places, reference_file_path):

FILE: vlogger/videoaudio.py
  function make_audio (line 15) | def make_audio(en_prompt_file, output_dir):
  function merge_video_audio (line 41) | def merge_video_audio(video_dir, audio_dir, output_dir):
  function concatenate_videos (line 81) | def concatenate_videos(video_dir, output_dir=None):

FILE: vlogger/videocaption.py
  function captioning (line 11) | def captioning(en_prompt_file, zh_prompt_file, input_video_dir, output_v...

FILE: vlogger/videofusion.py
  function fusion (line 8) | def fusion(path):