SYMBOL INDEX (650 symbols across 50 files)

FILE: gradio/app.py
  function install_dependencies (line 40) | def install_dependencies(enable_optimization=False):
  function read_config (line 83) | def read_config(config_path):
  function build_models (line 92) | def build_models(mode, resolution, enable_optimization=False):
  function parse_args (line 144) | def parse_args():
  function initialize_models (line 216) | def initialize_models(mode, resolution):
  function run_inference (line 220) | def run_inference(
  function run_image_inference (line 501) | def run_image_inference(
  function run_video_inference (line 541) | def run_video_inference(
  function generate_random_prompt (line 584) | def generate_random_prompt():
  function main (line 593) | def main():

FILE: opensora/acceleration/checkpoint.py
  class ActivationManager (line 17) | class ActivationManager:
    method __init__ (line 18) | def __init__(self):
    method setup_buffer (line 26) | def setup_buffer(self, numel: int, dtype: torch.dtype):
    method offload (line 31) | def offload(self, x: torch.Tensor) -> None:
    method onload (line 44) | def onload(self, x: torch.Tensor) -> None:
    method add_ignore_tensor (line 56) | def add_ignore_tensor(self, x: torch.Tensor) -> None:
    method is_top_tensor (line 59) | def is_top_tensor(self, x: torch.Tensor) -> bool:
  class CheckpointFunctionWithOffload (line 66) | class CheckpointFunctionWithOffload(torch.autograd.Function):
    method forward (line 68) | def forward(ctx, run_function, preserve_rng_state, *args):
    method backward (line 81) | def backward(ctx, *args):
  function checkpoint (line 99) | def checkpoint(
  function set_grad_checkpoint (line 254) | def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1):
  function auto_grad_checkpoint (line 265) | def auto_grad_checkpoint(module, *args, **kwargs):

FILE: opensora/acceleration/communications.py
  function _all_to_all (line 8) | def _all_to_all(
  class _AllToAll (line 21) | class _AllToAll(torch.autograd.Function):
    method forward (line 32) | def forward(ctx, input_, process_group, scatter_dim, gather_dim):
    method backward (line 41) | def backward(ctx, grad_output):
  function all_to_all (line 57) | def all_to_all(
  function _gather (line 66) | def _gather(
  function _split (line 83) | def _split(input_, pg: dist.ProcessGroup, dim=-1):
  function _gather (line 103) | def _gather(input_, pg: dist.ProcessGroup, dim=-1):
  class _GatherForwardSplitBackward (line 123) | class _GatherForwardSplitBackward(torch.autograd.Function):
    method symbolic (line 133) | def symbolic(graph, input_):
    method forward (line 137) | def forward(ctx, input_, process_group, dim, grad_scale):
    method backward (line 144) | def backward(ctx, grad_output):
  class _SplitForwardGatherBackward (line 153) | class _SplitForwardGatherBackward(torch.autograd.Function):
    method symbolic (line 164) | def symbolic(graph, input_):
    method forward (line 168) | def forward(ctx, input_, process_group, dim, grad_scale):
    method backward (line 175) | def backward(ctx, grad_output):
  function split_forward_gather_backward (line 183) | def split_forward_gather_backward(input_, process_group, dim, grad_scale...
  function gather_forward_split_backward (line 187) | def gather_forward_split_backward(input_, process_group, dim, grad_scale...

FILE: opensora/acceleration/parallel_states.py
  function set_data_parallel_group (line 6) | def set_data_parallel_group(group: dist.ProcessGroup):
  function get_data_parallel_group (line 10) | def get_data_parallel_group(get_mixed_dp_pg : bool = False):
  function set_sequence_parallel_group (line 16) | def set_sequence_parallel_group(group: dist.ProcessGroup):
  function get_sequence_parallel_group (line 20) | def get_sequence_parallel_group():
  function set_tensor_parallel_group (line 24) | def set_tensor_parallel_group(group: dist.ProcessGroup):
  function get_tensor_parallel_group (line 28) | def get_tensor_parallel_group():

FILE: opensora/acceleration/shardformer/modeling/t5.py
  class T5LayerNorm (line 5) | class T5LayerNorm(nn.Module):
    method __init__ (line 6) | def __init__(self, hidden_size, eps=1e-6):
    method forward (line 14) | def forward(self, hidden_states):
    method from_native_module (line 30) | def from_native_module(module, *args, **kwargs):

FILE: opensora/acceleration/shardformer/policy/t5_encoder.py
  class T5EncoderPolicy (line 6) | class T5EncoderPolicy(Policy):
    method config_sanity_check (line 7) | def config_sanity_check(self):
    method preprocess (line 11) | def preprocess(self):
    method module_policy (line 14) | def module_policy(self):
    method postprocess (line 40) | def postprocess(self):

FILE: opensora/models/dc_ae/ae_model_zoo.py
  function create_dc_ae_model_cfg (line 37) | def create_dc_ae_model_cfg(name: str, pretrained_path: Optional[str] = N...
  class DCAE_HF (line 45) | class DCAE_HF(DCAE, PyTorchModelHubMixin):
    method __init__ (line 46) | def __init__(self, model_name: str):
  function DC_AE (line 52) | def DC_AE(

FILE: opensora/models/dc_ae/models/dc_ae.py
  class EncoderConfig (line 48) | class EncoderConfig:
  class DecoderConfig (line 68) | class DecoderConfig:
  class DCAEConfig (line 87) | class DCAEConfig:
  function build_block (line 116) | def build_block(
  function build_stage_main (line 147) | def build_stage_main(
  function build_downsample_block (line 166) | def build_downsample_block(
  function build_upsample_block (line 216) | def build_upsample_block(
  function build_encoder_project_in_block (line 253) | def build_encoder_project_in_block(
  function build_encoder_project_out_block (line 278) | def build_encoder_project_out_block(
  function build_decoder_project_in_block (line 314) | def build_decoder_project_in_block(in_channels: int, out_channels: int, ...
  function build_decoder_project_out_block (line 337) | def build_decoder_project_out_block(
  class Encoder (line 376) | class Encoder(nn.Module):
    method __init__ (line 377) | def __init__(self, cfg: EncoderConfig):
    method forward (line 431) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Decoder (line 443) | class Decoder(nn.Module):
    method __init__ (line 444) | def __init__(self, cfg: DecoderConfig):
    method forward (line 507) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DCAE (line 522) | class DCAE(nn.Module):
    method __init__ (line 523) | def __init__(self, cfg: DCAEConfig):
    method load_model (line 550) | def load_model(self):
    method get_last_layer (line 557) | def get_last_layer(self):
    method encode_single (line 564) | def encode_single(self, x: torch.Tensor, is_video_encoder: bool = Fals...
    method _encode (line 580) | def _encode(self, x: torch.Tensor) -> torch.Tensor:
    method blend_v (line 589) | def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method blend_h (line 597) | def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method blend_t (line 605) | def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method spatial_tiled_encode (line 613) | def spatial_tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
    method temporal_tiled_encode (line 642) | def temporal_tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
    method encode (line 666) | def encode(self, x: torch.Tensor) -> torch.Tensor:
    method spatial_tiled_decode (line 674) | def spatial_tiled_decode(self, z: torch.FloatTensor) -> torch.Tensor:
    method temporal_tiled_decode (line 704) | def temporal_tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
    method decode_single (line 727) | def decode_single(self, z: torch.Tensor, is_video_decoder: bool = Fals...
    method _decode (line 742) | def _decode(self, z: torch.Tensor) -> torch.Tensor:
    method decode (line 751) | def decode(self, z: torch.Tensor) -> torch.Tensor:
    method forward (line 761) | def forward(self, x: torch.Tensor) -> tuple[Any, Tensor, dict[Any, Any]]:
    method get_latent_size (line 780) | def get_latent_size(self, input_size: list[int]) -> list[int]:
  function dc_ae_f32 (line 790) | def dc_ae_f32(name: str, pretrained_path: str) -> DCAEConfig:

FILE: opensora/models/dc_ae/models/nn/act.py
  function build_act (line 38) | def build_act(name: str, **kwargs) -> Optional[nn.Module]:

FILE: opensora/models/dc_ae/models/nn/norm.py
  class LayerNorm2d (line 28) | class LayerNorm2d(nn.LayerNorm):
    method forward (line 29) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class RMSNorm2d (line 38) | class RMSNorm2d(nn.Module):
    method __init__ (line 39) | def __init__(
    method forward (line 56) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class RMSNorm3d (line 63) | class RMSNorm3d(RMSNorm2d):
    method forward (line 64) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function build_norm (line 81) | def build_norm(name="bn2d", num_features=None, **kwargs) -> Optional[nn....
  function set_norm_eps (line 94) | def set_norm_eps(model: nn.Module, eps: Optional[float] = None) -> None:

FILE: opensora/models/dc_ae/models/nn/ops.py
  class ConvLayer (line 56) | class ConvLayer(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 126) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class UpSampleLayer (line 139) | class UpSampleLayer(nn.Module):
    method __init__ (line 140) | def __init__(
    method forward (line 154) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ConvPixelUnshuffleDownSampleLayer (line 162) | class ConvPixelUnshuffleDownSampleLayer(nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 183) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class PixelUnshuffleChannelAveragingDownSampleLayer (line 189) | class PixelUnshuffleChannelAveragingDownSampleLayer(nn.Module):
    method __init__ (line 190) | def __init__(
    method forward (line 203) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method __repr__ (line 230) | def __repr__(self):
  class ConvPixelShuffleUpSampleLayer (line 234) | class ConvPixelShuffleUpSampleLayer(nn.Module):
    method __init__ (line 235) | def __init__(
    method forward (line 254) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class InterpolateConvUpSampleLayer (line 260) | class InterpolateConvUpSampleLayer(nn.Module):
    method __init__ (line 261) | def __init__(
    method forward (line 285) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method __repr__ (line 297) | def __repr__(self):
  class ChannelDuplicatingPixelShuffleUpSampleLayer (line 301) | class ChannelDuplicatingPixelShuffleUpSampleLayer(nn.Module):
    method __init__ (line 302) | def __init__(
    method forward (line 316) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method __repr__ (line 339) | def __repr__(self):
  class LinearLayer (line 343) | class LinearLayer(nn.Module):
    method __init__ (line 344) | def __init__(
    method _try_squeeze (line 360) | def _try_squeeze(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 365) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class IdentityLayer (line 377) | class IdentityLayer(nn.Module):
    method forward (line 378) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DSConv (line 387) | class DSConv(nn.Module):
    method __init__ (line 388) | def __init__(
    method forward (line 423) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MBConv (line 429) | class MBConv(nn.Module):
    method __init__ (line 430) | def __init__(
    method forward (line 477) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class FusedMBConv (line 484) | class FusedMBConv(nn.Module):
    method __init__ (line 485) | def __init__(
    method forward (line 524) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class GLUMBConv (line 530) | class GLUMBConv(nn.Module):
    method __init__ (line 531) | def __init__(
    method forward (line 582) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ResBlock (line 594) | class ResBlock(nn.Module):
    method __init__ (line 595) | def __init__(
    method forward (line 636) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LiteMLA (line 642) | class LiteMLA(nn.Module):
    method __init__ (line 645) | def __init__(
    method relu_linear_att (line 710) | def relu_linear_att(self, qkv: torch.Tensor) -> torch.Tensor:
    method relu_quadratic_att (line 768) | def relu_quadratic_att(self, qkv: torch.Tensor) -> torch.Tensor:
    method forward (line 800) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class EfficientViTBlock (line 826) | class EfficientViTBlock(nn.Module):
    method __init__ (line 827) | def __init__(
    method forward (line 885) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ResidualBlock (line 896) | class ResidualBlock(nn.Module):
    method __init__ (line 897) | def __init__(
    method forward_main (line 911) | def forward_main(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 917) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DAGBlock (line 929) | class DAGBlock(nn.Module):
    method __init__ (line 930) | def __init__(
    method forward (line 950) | def forward(self, feature_dict: dict[str, torch.Tensor]) -> dict[str, ...
  class OpSequential (line 966) | class OpSequential(nn.Module):
    method __init__ (line 967) | def __init__(self, op_list: list[Optional[nn.Module]]):
    method forward (line 975) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: opensora/models/dc_ae/models/nn/vo_ops.py
  function pixel_shuffle_3d (line 11) | def pixel_shuffle_3d(x, upscale_factor):
  function pixel_unshuffle_3d (line 38) | def pixel_unshuffle_3d(x, downsample_factor):
  function test_pixel_shuffle_3d (line 59) | def test_pixel_shuffle_3d():
  function chunked_interpolate (line 84) | def chunked_interpolate(x, scale_factor, mode="nearest"):
  function test_chunked_interpolate (line 144) | def test_chunked_interpolate():
  function get_same_padding (line 205) | def get_same_padding(kernel_size: Union[int, tuple[int, ...]]) -> Union[...
  function resize (line 213) | def resize(
  function build_kwargs_from_config (line 234) | def build_kwargs_from_config(config: dict, target_func: Callable) -> dic...

FILE: opensora/models/dc_ae/utils/init.py
  function init_modules (line 26) | def init_modules(model: Union[nn.Module, list[nn.Module]], init_type="tr...

FILE: opensora/models/dc_ae/utils/list.py
  function list_sum (line 30) | def list_sum(x: list) -> Any:
  function list_mean (line 34) | def list_mean(x: list) -> Any:
  function weighted_list_sum (line 38) | def weighted_list_sum(x: list, weights: list) -> Any:
  function list_join (line 43) | def list_join(x: list, sep="\t", format_str="%s") -> str:
  function val2list (line 47) | def val2list(x: Union[list, tuple, Any], repeat_time=1) -> list:
  function val2tuple (line 53) | def val2tuple(x: Union[list, tuple, Any], min_len: int = 1, idx_repeat: ...
  function squeeze_list (line 63) | def squeeze_list(x: Optional[list]) -> Union[list, Any]:

FILE: opensora/models/hunyuan_vae/autoencoder_kl_causal_3d.py
  class AutoEncoder3DConfig (line 60) | class AutoEncoder3DConfig:
  class AutoencoderKLCausal3D (line 84) | class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
    method __init__ (line 95) | def __init__(self, config: AutoEncoder3DConfig):
    method enable_temporal_tiling (line 148) | def enable_temporal_tiling(self, use_tiling: bool = True):
    method disable_temporal_tiling (line 151) | def disable_temporal_tiling(self):
    method enable_spatial_tiling (line 154) | def enable_spatial_tiling(self, use_tiling: bool = True):
    method disable_spatial_tiling (line 157) | def disable_spatial_tiling(self):
    method enable_tiling (line 160) | def enable_tiling(self, use_tiling: bool = True):
    method disable_tiling (line 169) | def disable_tiling(self):
    method enable_slicing (line 177) | def enable_slicing(self):
    method disable_slicing (line 184) | def disable_slicing(self):
    method attn_processors (line 193) | def attn_processors(self) -> Dict[str, AttentionProcessor]:
    method set_attn_processor (line 217) | def set_attn_processor(
    method set_default_attn_processor (line 254) | def set_default_attn_processor(self):
    method encode (line 270) | def encode(
    method _decode (line 318) | def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> U...
    method decode (line 338) | def decode(self, z: torch.FloatTensor) -> torch.FloatTensor:
    method blend_v (line 360) | def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method blend_h (line 368) | def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method blend_t (line 376) | def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int)...
    method spatial_tiled_encode (line 384) | def spatial_tiled_encode(self, x: torch.FloatTensor, return_moments: b...
    method spatial_tiled_decode (line 436) | def spatial_tiled_decode(
    method temporal_tiled_encode (line 486) | def temporal_tiled_encode(self, x: torch.FloatTensor) -> DiagonalGauss...
    method temporal_tiled_decode (line 517) | def temporal_tiled_decode(
    method forward (line 554) | def forward(
    method fuse_qkv_projections (line 575) | def fuse_qkv_projections(self):
    method unfuse_qkv_projections (line 599) | def unfuse_qkv_projections(self):
    method get_last_layer (line 612) | def get_last_layer(self):
    method get_latent_size (line 615) | def get_latent_size(self, input_size: list[int]) -> list[int]:
  function CausalVAE3D_HUNYUAN (line 626) | def CausalVAE3D_HUNYUAN(

FILE: opensora/models/hunyuan_vae/distributed.py
  function align_atten_bias (line 31) | def align_atten_bias(attn_bias):
  function _attn_fwd (line 41) | def _attn_fwd(
  function _attn_bwd (line 58) | def _attn_bwd(
  class MemEfficientRingAttention (line 76) | class MemEfficientRingAttention(torch.autograd.Function):
    method forward (line 81) | def forward(
    method backward (line 180) | def backward(ctx, grad_output, grad_softmax_lse):
    method attention (line 236) | def attention(
  class MemEfficientRingAttnProcessor (line 271) | class MemEfficientRingAttnProcessor:
    method __init__ (line 272) | def __init__(self, sp_group: dist.ProcessGroup):
    method __call__ (line 277) | def __call__(
  class ContextParallelAttention (line 360) | class ContextParallelAttention:
    method __init__ (line 361) | def __init__(self):
    method from_native_module (line 365) | def from_native_module(module: Attention, process_group, *args, **kwar...
  function _context_chunk_attn_fwd (line 395) | def _context_chunk_attn_fwd(
  function _context_chunk_attn_bwd (line 433) | def _context_chunk_attn_bwd(
  function prepare_parallel_causal_attention_mask (line 502) | def prepare_parallel_causal_attention_mask(
  function prepare_parallel_attention_mask (line 523) | def prepare_parallel_attention_mask(
  class TPUpDecoderBlockCausal3D (line 539) | class TPUpDecoderBlockCausal3D(UpsampleCausal3D):
    method __init__ (line 540) | def __init__(
    method forward (line 564) | def forward(self, input_tensor):
    method from_native_module (line 568) | def from_native_module(module: UpsampleCausal3D, process_group, **kwar...

FILE: opensora/models/hunyuan_vae/policy.py
  function gen_resnets_replacements (line 13) | def gen_resnets_replacements(prefix: str, with_shortcut: bool = False):
  class HunyuanVaePolicy (line 51) | class HunyuanVaePolicy(Policy):
    method config_sanity_check (line 52) | def config_sanity_check(self):
    method preprocess (line 55) | def preprocess(self):
    method module_policy (line 58) | def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDes...
    method postprocess (line 154) | def postprocess(self):

FILE: opensora/models/hunyuan_vae/unet_causal_3d_blocks.py
  function chunk_nearest_interpolate (line 41) | def chunk_nearest_interpolate(
  function prepare_causal_attention_mask (line 52) | def prepare_causal_attention_mask(n_frame: int, n_hw: int, dtype, device...
  class CausalConv3d (line 63) | class CausalConv3d(nn.Module):
    method __init__ (line 69) | def __init__(
    method forward (line 94) | def forward(self, x):
  class UpsampleCausal3D (line 98) | class UpsampleCausal3D(nn.Module):
    method __init__ (line 103) | def __init__(
    method forward (line 117) | def forward(
  class DownsampleCausal3D (line 160) | class DownsampleCausal3D(nn.Module):
    method __init__ (line 165) | def __init__(
    method forward (line 177) | def forward(self, input_tensor: torch.FloatTensor) -> torch.FloatTensor:
  class ResnetBlockCausal3D (line 184) | class ResnetBlockCausal3D(nn.Module):
    method __init__ (line 189) | def __init__(
    method forward (line 240) | def forward(
  class UNetMidBlockCausal3D (line 262) | class UNetMidBlockCausal3D(nn.Module):
    method __init__ (line 267) | def __init__(
    method forward (line 345) | def forward(self, hidden_states: torch.FloatTensor, attention_mask: Op...
  class DownEncoderBlockCausal3D (line 358) | class DownEncoderBlockCausal3D(nn.Module):
    method __init__ (line 359) | def __init__(
    method forward (line 405) | def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
  class UpDecoderBlockCausal3D (line 416) | class UpDecoderBlockCausal3D(nn.Module):
    method __init__ (line 417) | def __init__(
    method forward (line 468) | def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:

FILE: opensora/models/hunyuan_vae/vae.py
  class DecoderOutput (line 28) | class DecoderOutput(BaseOutput):
  class EncoderCausal3D (line 40) | class EncoderCausal3D(nn.Module):
    method __init__ (line 45) | def __init__(
    method prepare_attention_mask (line 121) | def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch...
    method forward (line 128) | def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
  class DecoderCausal3D (line 153) | class DecoderCausal3D(nn.Module):
    method __init__ (line 158) | def __init__(
    method post_process (line 233) | def post_process(self, sample: torch.Tensor) -> torch.Tensor:
    method prepare_attention_mask (line 238) | def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch...
    method forward (line 245) | def forward(
  class DiagonalGaussianDistribution (line 280) | class DiagonalGaussianDistribution(object):
    method __init__ (line 281) | def __init__(self, parameters: torch.Tensor, deterministic: bool = Fal...
    method sample (line 299) | def sample(self, generator: Optional[torch.Generator] = None) -> torch...
    method kl (line 310) | def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Te...
    method nll (line 330) | def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3])...
    method mode (line 339) | def mode(self) -> torch.Tensor:

FILE: opensora/models/mmdit/distributed.py
  class _SplitForwardGatherBackwardVarLen (line 39) | class _SplitForwardGatherBackwardVarLen(torch.autograd.Function):
    method forward (line 51) | def forward(ctx, input_, dim, process_group, splits: List[int]):
    method backward (line 60) | def backward(ctx, grad_output):
  function split_forward_gather_backward_var_len (line 72) | def split_forward_gather_backward_var_len(input_, dim, process_group, sp...
  class _GatherForwardSplitBackwardVarLen (line 76) | class _GatherForwardSplitBackwardVarLen(torch.autograd.Function):
    method forward (line 88) | def forward(ctx, input_, dim, process_group, splits: List[int]):
    method backward (line 105) | def backward(ctx, grad_output):
  function gather_forward_split_backward_var_len (line 111) | def gather_forward_split_backward_var_len(input_, dim, process_group, sp...
  function _fa_forward (line 115) | def _fa_forward(
  function _fa_backward (line 164) | def _fa_backward(
  class RingAttention (line 219) | class RingAttention(torch.autograd.Function):
    method forward (line 224) | def forward(
    method backward (line 316) | def backward(ctx, grad_output, grad_softmax_lse):
    method attention (line 376) | def attention(
  function ring_attention (line 413) | def ring_attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, sp_group...
  class DistributedDoubleStreamBlockProcessor (line 425) | class DistributedDoubleStreamBlockProcessor:
    method __init__ (line 426) | def __init__(self, shard_config: ShardConfig) -> None:
    method __call__ (line 429) | def __call__(
  class DistributedSingleStreamBlockProcessor (line 508) | class DistributedSingleStreamBlockProcessor:
    method __init__ (line 509) | def __init__(self, shard_config: ShardConfig) -> None:
    method __call__ (line 512) | def __call__(self, attn: SingleStreamBlock, x: Tensor, vec: Tensor, pe...
  class _TempSwitchCP (line 561) | class _TempSwitchCP(torch.autograd.Function):
    method forward (line 563) | def forward(ctx, input_, shard_config: ShardConfig, value: bool):
    method backward (line 570) | def backward(ctx, grad_output):
  function switch_sequence_parallelism (line 576) | def switch_sequence_parallelism(input_, shard_config: ShardConfig, value...
  function mmdit_model_forward (line 580) | def mmdit_model_forward(
  class MMDiTPolicy (line 686) | class MMDiTPolicy(Policy):
    method config_sanity_check (line 687) | def config_sanity_check(self):
    method preprocess (line 693) | def preprocess(self) -> nn.Module:
    method postprocess (line 696) | def postprocess(self) -> nn.Module:
    method tie_weight_check (line 699) | def tie_weight_check(self) -> bool:
    method module_policy (line 702) | def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDes...
    method get_held_layers (line 853) | def get_held_layers(self) -> List[nn.Module]:

FILE: opensora/models/mmdit/layers.py
  class EmbedND (line 31) | class EmbedND(nn.Module):
    method __init__ (line 32) | def __init__(self, dim: int, theta: int, axes_dim: list[int]):
    method forward (line 38) | def forward(self, ids: Tensor) -> Tensor:
  class LigerEmbedND (line 47) | class LigerEmbedND(nn.Module):
    method __init__ (line 48) | def __init__(self, dim: int, theta: int, axes_dim: list[int]):
    method forward (line 54) | def forward(self, ids: Tensor) -> Tensor:
  function timestep_embedding (line 69) | def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: fl...
  class MLPEmbedder (line 91) | class MLPEmbedder(nn.Module):
    method __init__ (line 92) | def __init__(self, in_dim: int, hidden_dim: int):
    method forward (line 98) | def forward(self, x: Tensor) -> Tensor:
  class RMSNorm (line 102) | class RMSNorm(torch.nn.Module):
    method __init__ (line 103) | def __init__(self, dim: int):
    method forward (line 107) | def forward(self, x: Tensor):
  class FusedRMSNorm (line 114) | class FusedRMSNorm(RMSNorm):
    method forward (line 115) | def forward(self, x: Tensor):
  class QKNorm (line 126) | class QKNorm(torch.nn.Module):
    method __init__ (line 127) | def __init__(self, dim: int):
    method forward (line 132) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Te...
  class SelfAttention (line 138) | class SelfAttention(nn.Module):
    method __init__ (line 139) | def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = Fals...
    method forward (line 154) | def forward(self, x: Tensor, pe: Tensor) -> Tensor:
  class ModulationOut (line 173) | class ModulationOut:
  class Modulation (line 179) | class Modulation(nn.Module):
    method __init__ (line 180) | def __init__(self, dim: int, double: bool):
    method forward (line 186) | def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut |...
  class DoubleStreamBlockProcessor (line 195) | class DoubleStreamBlockProcessor:
    method __call__ (line 196) | def __call__(self, attn: nn.Module, img: Tensor, txt: Tensor, vec: Ten...
  class DoubleStreamBlock (line 256) | class DoubleStreamBlock(nn.Module):
    method __init__ (line 257) | def __init__(
    method set_processor (line 299) | def set_processor(self, processor) -> None:
    method get_processor (line 302) | def get_processor(self):
    method forward (line 305) | def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, *...
  class SingleStreamBlockProcessor (line 309) | class SingleStreamBlockProcessor:
    method __call__ (line 310) | def __call__(self, attn: nn.Module, x: Tensor, vec: Tensor, pe: Tensor...
  class SingleStreamBlock (line 337) | class SingleStreamBlock(nn.Module):
    method __init__ (line 343) | def __init__(
    method set_processor (line 381) | def set_processor(self, processor) -> None:
    method get_processor (line 384) | def get_processor(self):
    method forward (line 387) | def forward(self, x: Tensor, vec: Tensor, pe: Tensor, **kwargs) -> Ten...
  class LastLayer (line 391) | class LastLayer(nn.Module):
    method __init__ (line 392) | def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
    method forward (line 398) | def forward(self, x: Tensor, vec: Tensor) -> Tensor:

FILE: opensora/models/mmdit/math.py
  function flash_attn_func (line 16) | def flash_attn_func(q: Tensor, k: Tensor, v: Tensor) -> Tensor:
  function attention (line 22) | def attention(q: Tensor, k: Tensor, v: Tensor, pe) -> Tensor:
  function liger_rope (line 39) | def liger_rope(pos: Tensor, dim: int, theta: int) -> Tuple:
  function rope (line 50) | def rope(pos: Tensor, dim: int, theta: int) -> Tuple:
  function apply_rope (line 60) | def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tenso...
  function rearrange_tensor (line 68) | def rearrange_tensor(tensor):
  function reverse_rearrange_tensor (line 94) | def reverse_rearrange_tensor(tensor):

FILE: opensora/models/mmdit/model.py
  class MMDiTConfig (line 40) | class MMDiTConfig:
    method get (line 62) | def get(self, attribute_name, default=None):
    method __contains__ (line 65) | def __contains__(self, attribute_name):
  class MMDiTModel (line 69) | class MMDiTModel(nn.Module):
    method __init__ (line 72) | def __init__(self, config: MMDiTConfig):
    method initialize_weights (line 149) | def initialize_weights(self):
    method prepare_block_inputs (line 154) | def prepare_block_inputs(
    method enable_input_require_grads (line 204) | def enable_input_require_grads(self):
    method forward_ckpt (line 208) | def forward_ckpt(
    method forward_selective_ckpt (line 235) | def forward_selective_ckpt(
  function Flux (line 272) | def Flux(

FILE: opensora/models/mmdit/policy.py
  function gen_resnets_replacements (line 13) | def gen_resnets_replacements(prefix: str, with_shortcut: bool = False):
  class HunyuanVaePolicy (line 51) | class HunyuanVaePolicy(Policy):
    method config_sanity_check (line 52) | def config_sanity_check(self):
    method preprocess (line 55) | def preprocess(self):
    method module_policy (line 58) | def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDes...
    method postprocess (line 154) | def postprocess(self):

FILE: opensora/models/text/conditioner.py
  class HFEmbedder (line 10) | class HFEmbedder(nn.Module):
    method __init__ (line 11) | def __init__(self, from_pretrained: str, max_length: int, shardformer:...
    method forward (line 31) | def forward(self, text: list[str], added_tokens: int = 0, seq_align: i...
  function shardformer_t5 (line 56) | def shardformer_t5(t5: T5EncoderModel) -> T5EncoderModel:

FILE: opensora/models/vae/autoencoder_2d.py
  class AutoEncoderConfig (line 34) | class AutoEncoderConfig:
  class AttnBlock (line 49) | class AttnBlock(nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels: int):
    method attention (line 58) | def attention(self, h_: Tensor) -> Tensor:
    method forward (line 70) | def forward(self, x: Tensor) -> Tensor:
  class ResnetBlock (line 74) | class ResnetBlock(nn.Module):
    method __init__ (line 75) | def __init__(self, in_channels: int, out_channels: int):
    method forward (line 88) | def forward(self, x):
  class Downsample (line 104) | class Downsample(nn.Module):
    method __init__ (line 105) | def __init__(self, in_channels: int):
    method forward (line 109) | def forward(self, x: Tensor) -> Tensor:
  class Upsample (line 115) | class Upsample(nn.Module):
    method __init__ (line 116) | def __init__(self, in_channels: int):
    method forward (line 120) | def forward(self, x: Tensor) -> Tensor:
  class Encoder (line 125) | class Encoder(nn.Module):
    method __init__ (line 126) | def __init__(self, config: AutoEncoderConfig):
    method forward (line 168) | def forward(self, x: Tensor) -> Tensor:
  class Decoder (line 192) | class Decoder(nn.Module):
    method __init__ (line 193) | def __init__(self, config: AutoEncoderConfig):
    method forward (line 236) | def forward(self, z: Tensor) -> Tensor:
  class AutoEncoder (line 260) | class AutoEncoder(nn.Module):
    method __init__ (line 261) | def __init__(self, config: AutoEncoderConfig):
    method encode_ (line 269) | def encode_(self, x: Tensor) -> tuple[Tensor, DiagonalGaussianDistribu...
    method encode (line 282) | def encode(self, x: Tensor) -> Tensor:
    method decode (line 285) | def decode(self, z: Tensor) -> Tensor:
    method forward (line 293) | def forward(self, x: Tensor) -> tuple[Tensor, DiagonalGaussianDistribu...
    method get_last_layer (line 302) | def get_last_layer(self):
  function AutoEncoderFlux (line 307) | def AutoEncoderFlux(

FILE: opensora/models/vae/discriminator.py
  function weights_init (line 9) | def weights_init(m):
  function weights_init_conv (line 18) | def weights_init_conv(m):
  class NLayerDiscriminator3D (line 29) | class NLayerDiscriminator3D(nn.Module):
    method __init__ (line 32) | def __init__(
    method forward (line 95) | def forward(self, x):
  function N_LAYER_DISCRIMINATOR_3D (line 101) | def N_LAYER_DISCRIMINATOR_3D(from_pretrained=None, force_huggingface=Non...

FILE: opensora/models/vae/losses.py
  function hinge_d_loss (line 9) | def hinge_d_loss(logits_real, logits_fake):
  function vanilla_d_loss (line 16) | def vanilla_d_loss(logits_real, logits_fake):
  function wgan_gp_loss (line 23) | def wgan_gp_loss(logits_real, logits_fake):
  function adopt_weight (line 28) | def adopt_weight(weight, global_step, threshold=0, value=0.0):
  function measure_perplexity (line 34) | def measure_perplexity(predicted_indices, n_embed):
  function l1 (line 44) | def l1(x, y):
  function l2 (line 48) | def l2(x, y):
  function batch_mean (line 52) | def batch_mean(x):
  function sigmoid_cross_entropy_with_logits (line 56) | def sigmoid_cross_entropy_with_logits(labels, logits):
  function lecam_reg (line 65) | def lecam_reg(real_pred, fake_pred, ema_real_pred, ema_fake_pred):
  function gradient_penalty_fn (line 72) | def gradient_penalty_fn(images, output):
  class VAELoss (line 86) | class VAELoss(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 115) | def forward(
  class GeneratorLoss (line 156) | class GeneratorLoss(nn.Module):
    method __init__ (line 157) | def __init__(self, gen_start=2001, disc_factor=1.0, disc_weight=0.5):
    method calculate_adaptive_weight (line 163) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer):
    method forward (line 171) | def forward(
  class DiscriminatorLoss (line 192) | class DiscriminatorLoss(nn.Module):
    method __init__ (line 193) | def __init__(self, disc_start=2001, disc_factor=1.0, disc_loss_type="h...
    method forward (line 210) | def forward(

FILE: opensora/models/vae/lpips.py
  function md5_hash (line 20) | def md5_hash(path):
  function download (line 26) | def download(url, local_path, chunk_size=1024):
  function get_ckpt_path (line 38) | def get_ckpt_path(name, root=".", check=False):
  class LPIPS (line 49) | class LPIPS(nn.Module):
    method __init__ (line 51) | def __init__(self, use_dropout=True):
    method load_from_pretrained (line 66) | def load_from_pretrained(self, name="vgg_lpips"):
    method from_pretrained (line 72) | def from_pretrained(cls, name="vgg_lpips"):
    method forward_old (line 80) | def forward_old(self, input, target):
    method get_layer_loss (line 95) | def get_layer_loss(self, input, target, i):
    method forward (line 102) | def forward(self, input, target):
  class ScalingLayer (line 112) | class ScalingLayer(nn.Module):
    method __init__ (line 113) | def __init__(self):
    method forward (line 118) | def forward(self, inp):
  class NetLinLayer (line 122) | class NetLinLayer(nn.Module):
    method __init__ (line 125) | def __init__(self, chn_in, chn_out=1, use_dropout=False):
  class vgg16 (line 140) | class vgg16(torch.nn.Module):
    method __init__ (line 141) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 164) | def forward(self, X):
  function normalize_tensor (line 180) | def normalize_tensor(x, eps=1e-10):
  function spatial_average (line 185) | def spatial_average(x, keepdim=True):

FILE: opensora/models/vae/tensor_parallel.py
  function shard_channelwise (line 27) | def shard_channelwise(
  class Conv3dTPCol (line 57) | class Conv3dTPCol(nn.Conv3d):
    method __init__ (line 60) | def __init__(
    method from_native_module (line 116) | def from_native_module(
    method forward (line 147) | def forward(self, input: torch.Tensor) -> torch.Tensor:
  class Conv3dTPRow (line 168) | class Conv3dTPRow(nn.Conv3d):
    method __init__ (line 171) | def __init__(
    method from_native_module (line 226) | def from_native_module(
    method forward (line 253) | def forward(self, input: torch.Tensor) -> torch.Tensor:
  class Conv2dTPRow (line 276) | class Conv2dTPRow(nn.Conv2d):
    method __init__ (line 279) | def __init__(
    method forward (line 333) | def forward(self, input: torch.Tensor) -> torch.Tensor:
    method from_native_module (line 355) | def from_native_module(
  class Conv1dTPRow (line 384) | class Conv1dTPRow(nn.Conv1d):
    method __init__ (line 387) | def __init__(
    method forward (line 441) | def forward(self, input: torch.Tensor) -> torch.Tensor:
    method from_native_module (line 464) | def from_native_module(
  class GroupNormTP (line 493) | class GroupNormTP(nn.GroupNorm):
    method __init__ (line 494) | def __init__(
    method forward (line 529) | def forward(self, input: torch.Tensor) -> torch.Tensor:
    method from_native_module (line 539) | def from_native_module(

FILE: opensora/models/vae/utils.py
  function ceil_to_divisible (line 11) | def ceil_to_divisible(n: int, dividend: int) -> int:
  function chunked_avg_pool1d (line 15) | def chunked_avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_...
  function chunked_interpolate (line 32) | def chunked_interpolate(input, scale_factor):
  function get_conv3d_output_shape (line 47) | def get_conv3d_output_shape(
  function get_conv3d_n_chunks (line 59) | def get_conv3d_n_chunks(numel: int, n_channels: int, numel_limit: int):
  function channel_chunk_conv3d (line 65) | def channel_chunk_conv3d(
  class DiagonalGaussianDistribution (line 112) | class DiagonalGaussianDistribution(object):
    method __init__ (line 113) | def __init__(
    method sample (line 128) | def sample(self):
    method kl (line 133) | def kl(self, other=None):
    method mode (line 149) | def mode(self):
  class ChannelChunkConv3d (line 153) | class ChannelChunkConv3d(nn.Conv3d):
    method _get_output_numel (line 156) | def _get_output_numel(self, input_shape: torch.Size) -> int:
    method _get_n_chunks (line 167) | def _get_n_chunks(self, numel: int, n_channels: int):
    method forward (line 172) | def forward(self, input: Tensor) -> Tensor:
  function pad_for_conv3d (line 194) | def pad_for_conv3d(x: torch.Tensor, width_pad: int, height_pad: int, tim...
  function pad_for_conv3d_kernel_3x3x3 (line 202) | def pad_for_conv3d_kernel_3x3x3(x: torch.Tensor) -> torch.Tensor:
  class PadConv3D (line 218) | class PadConv3D(nn.Module):
    method __init__ (line 223) | def __init__(self, in_channels: int, out_channels: int, kernel_size: i...
    method forward (line 247) | def forward(self, x: Tensor) -> Tensor:
  class ChannelChunkPadConv3D (line 254) | class ChannelChunkPadConv3D(PadConv3D):
    method __init__ (line 255) | def __init__(self, in_channels: int, out_channels: int, kernel_size: i...

FILE: opensora/registry.py
  function build_module (line 7) | def build_module(module: dict | nn.Module, builder: Registry, **kwargs) ...

FILE: opensora/utils/cai.py
  function set_group_size (line 20) | def set_group_size(plugin_config: dict):
  function init_inference_environment (line 39) | def init_inference_environment():
  function get_booster (line 51) | def get_booster(cfg: dict, ae: bool = False):
  function get_is_saving_process (line 74) | def get_is_saving_process(cfg: dict):

FILE: opensora/utils/ckpt.py
  function load_from_hf_hub (line 33) | def load_from_hf_hub(repo_path: str, cache_dir: str = None) -> str:
  function load_from_sharded_state_dict (line 50) | def load_from_sharded_state_dict(model: nn.Module, ckpt_path: str, model...
  function print_load_warning (line 64) | def print_load_warning(missing: list[str], unexpected: list[str]) -> None:
  function load_checkpoint (line 84) | def load_checkpoint(
  function rm_checkpoints (line 143) | def rm_checkpoints(
  function model_sharding (line 172) | def model_sharding(model: torch.nn.Module, device: torch.device = None):
  function model_gathering (line 195) | def model_gathering(model: torch.nn.Module, model_shape_dict: dict, pinn...
  function remove_padding (line 223) | def remove_padding(tensor: torch.Tensor, original_shape: tuple) -> torch...
  function record_model_param_shape (line 234) | def record_model_param_shape(model: torch.nn.Module) -> dict:
  function load_json (line 250) | def load_json(file_path: str) -> dict:
  function save_json (line 264) | def save_json(data, file_path: str):
  function _prepare_ema_pinned_state_dict (line 276) | def _prepare_ema_pinned_state_dict(model: nn.Module, ema_shape_dict: dict):
  function _search_valid_path (line 289) | def _search_valid_path(path: str) -> str:
  function master_weights_gathering (line 297) | def master_weights_gathering(model: torch.nn.Module, optimizer: LowLevel...
  function load_master_weights (line 321) | def load_master_weights(model: torch.nn.Module, optimizer: LowLevelZeroO...
  class CheckpointIO (line 335) | class CheckpointIO:
    method __init__ (line 336) | def __init__(self, n_write_entries: int = 32):
    method _sync_io (line 343) | def _sync_io(self):
    method __del__ (line 351) | def __del__(self):
    method _prepare_pinned_state_dict (line 354) | def _prepare_pinned_state_dict(self, ema: nn.Module, ema_shape_dict: d...
    method _prepare_master_pinned_state_dict (line 358) | def _prepare_master_pinned_state_dict(self, model: nn.Module, optimize...
    method save (line 367) | def save(
    method load (line 463) | def load(

FILE: opensora/utils/config.py
  function parse_args (line 13) | def parse_args() -> tuple[str, argparse.Namespace]:
  function read_config (line 26) | def read_config(config_path: str) -> Config:
  function parse_configs (line 40) | def parse_configs() -> Config:
  function merge_args (line 58) | def merge_args(cfg: Config, args: argparse.Namespace) -> Config:
  function auto_convert (line 91) | def auto_convert(value: str) -> int | float | bool | list | dict | None:
  function sync_string (line 140) | def sync_string(value: str):
  function create_experiment_workspace (line 157) | def create_experiment_workspace(
  function config_to_name (line 190) | def config_to_name(cfg: Config) -> str:
  function parse_alias (line 198) | def parse_alias(cfg: Config) -> Config:

FILE: opensora/utils/inference.py
  class SamplingMethod (line 16) | class SamplingMethod(Enum):
  function create_tmp_csv (line 21) | def create_tmp_csv(save_dir: str, prompt: str, ref: str = None, create=T...
  function modify_option_to_t2i (line 43) | def modify_option_to_t2i(sampling_option, distilled: bool = False, img_r...
  function get_save_path_name (line 58) | def get_save_path_name(
  function get_names_from_path (line 86) | def get_names_from_path(path):
  function process_and_save (line 101) | def process_and_save(
  function check_fps_added (line 166) | def check_fps_added(sentence):
  function ensure_sentence_ends_with_period (line 176) | def ensure_sentence_ends_with_period(sentence: str):
  function add_fps_info_to_text (line 186) | def add_fps_info_to_text(text: list[str], fps: int = 16):
  function add_motion_score_to_text (line 199) | def add_motion_score_to_text(text, motion_score: int | str):
  function add_noise_to_ref (line 210) | def add_noise_to_ref(masked_ref: torch.Tensor, masks: torch.Tensor, t: f...
  function collect_references_batch (line 216) | def collect_references_batch(
  function prepare_inference_condition (line 283) | def prepare_inference_condition(

FILE: opensora/utils/logger.py
  function is_distributed (line 7) | def is_distributed() -> bool:
  function is_main_process (line 17) | def is_main_process() -> bool:
  function get_world_size (line 27) | def get_world_size() -> int:
  function create_logger (line 40) | def create_logger(logging_dir: str = None) -> logging.Logger:
  function log_message (line 72) | def log_message(*args, level: str = "info"):

FILE: opensora/utils/misc.py
  function create_tensorboard_writer (line 20) | def create_tensorboard_writer(exp_dir: str) -> SummaryWriter:
  function log_cuda_memory (line 43) | def log_cuda_memory(stage: str = None):
  function log_cuda_max_memory (line 56) | def log_cuda_max_memory(stage: str = None):
  function get_model_numel (line 75) | def get_model_numel(model: torch.nn.Module) -> tuple[int, int]:
  function log_model_params (line 94) | def log_model_params(model: nn.Module):
  function format_numel_str (line 112) | def format_numel_str(numel: int) -> str:
  function format_duration (line 135) | def format_duration(seconds: int) -> str:
  function all_reduce_mean (line 158) | def all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
  function all_reduce_sum (line 164) | def all_reduce_sum(tensor: torch.Tensor) -> torch.Tensor:
  function to_tensor (line 169) | def to_tensor(data: torch.Tensor | np.ndarray | Sequence | int | float) ...
  function to_ndarray (line 197) | def to_ndarray(data: torch.Tensor | np.ndarray | Sequence | int | float)...
  function to_torch_dtype (line 224) | def to_torch_dtype(dtype: str | torch.dtype) -> torch.dtype:
  class Timer (line 259) | class Timer:
    method __init__ (line 260) | def __init__(self, name, log=False, barrier=False, coordinator: DistCo...
    method elapsed_time (line 269) | def elapsed_time(self) -> float:
    method __enter__ (line 272) | def __enter__(self):
    method __exit__ (line 279) | def __exit__(self, exc_type, exc_val, exc_tb):
  class Timers (line 290) | class Timers:
    method __init__ (line 291) | def __init__(self, record_time: bool, record_barrier: bool = False, co...
    method __getitem__ (line 297) | def __getitem__(self, name: str) -> Timer:
    method to_dict (line 305) | def to_dict(self):
    method to_str (line 308) | def to_str(self, epoch: int, step: int) -> str:
  function is_pipeline_enabled (line 315) | def is_pipeline_enabled(plugin_type: str, plugin_config: dict) -> bool:
  function is_log_process (line 319) | def is_log_process(plugin_type: str, plugin_config: dict) -> bool:
  class NsysRange (line 325) | class NsysRange:
    method __init__ (line 326) | def __init__(self, range_name: str):
    method __enter__ (line 329) | def __enter__(self):
    method __exit__ (line 333) | def __exit__(self, exc_type, exc_val, exc_tb):
  class NsysProfiler (line 337) | class NsysProfiler:
    method __init__ (line 359) | def __init__(self, warmup_steps: int = 0, num_steps: int = 1, enabled:...
    method step (line 365) | def step(self):
    method range (line 374) | def range(self, range_name: str) -> NsysRange:
  class ProfilerContext (line 380) | class ProfilerContext:
    method __init__ (line 381) | def __init__(
    method step (line 410) | def step(self):
    method is_profile_end (line 420) | def is_profile_end(self):
  function get_process_mem (line 424) | def get_process_mem():
  function get_total_mem (line 429) | def get_total_mem():
  function print_mem (line 433) | def print_mem(prefix: str = ""):

FILE: opensora/utils/optimizer.py
  function create_optimizer (line 7) | def create_optimizer(
  function create_lr_scheduler (line 33) | def create_lr_scheduler(
  class LinearWarmupLR (line 69) | class LinearWarmupLR(_LRScheduler):
    method __init__ (line 79) | def __init__(self, optimizer, initial_lr=0, warmup_steps: int = 0, las...
    method get_lr (line 84) | def get_lr(self):

FILE: opensora/utils/prompt_refine.py
  function image_to_url (line 66) | def image_to_url(image_path):
  function refine_prompt (line 75) | def refine_prompt(prompt: str, retry_times: int = 3, type: str = "t2v", ...
  function refine_prompts (line 227) | def refine_prompts(prompts: list[str], retry_times: int = 3, type: str =...

FILE: opensora/utils/sampling.py
  class SamplingOption (line 29) | class SamplingOption:
  function sanitize_sampling_option (line 82) | def sanitize_sampling_option(sampling_option: SamplingOption) -> Samplin...
  function get_oscillation_gs (line 120) | def get_oscillation_gs(guidance_scale: float, i: int, force_num=10):
  class Denoiser (line 141) | class Denoiser(ABC):
    method denoise (line 143) | def denoise(self, model: MMDiTModel, **kwargs) -> Tensor:
    method prepare_guidance (line 147) | def prepare_guidance(
  class I2VDenoiser (line 158) | class I2VDenoiser(Denoiser):
    method denoise (line 159) | def denoise(self, model: MMDiTModel, **kwargs) -> Tensor:
    method prepare_guidance (line 228) | def prepare_guidance(
  class DistilledDenoiser (line 248) | class DistilledDenoiser(Denoiser):
    method denoise (line 249) | def denoise(self, model: MMDiTModel, **kwargs) -> Tensor:
    method prepare_guidance (line 273) | def prepare_guidance(
  function time_shift (line 295) | def time_shift(alpha: float, t: Tensor) -> Tensor:
  function get_res_lin_function (line 299) | def get_res_lin_function(
  function get_schedule (line 307) | def get_schedule(
  function get_noise (line 335) | def get_noise(
  function pack (line 375) | def pack(x: Tensor, patch_size: int = 2) -> Tensor:
  function unpack (line 381) | def unpack(
  function prepare (line 401) | def prepare(
  function prepare_ids (line 462) | def prepare_ids(
  function prepare_models (line 511) | def prepare_models(
  function prepare_api (line 562) | def prepare_api(

FILE: opensora/utils/train.py
  function set_lr (line 25) | def set_lr(
  function set_warmup_steps (line 39) | def set_warmup_steps(
  function set_eps (line 47) | def set_eps(
  function setup_device (line 56) | def setup_device() -> tuple[torch.device, DistCoordinator]:
  function create_colossalai_plugin (line 73) | def create_colossalai_plugin(
  function update_ema (line 132) | def update_ema(
  function dropout_condition (line 166) | def dropout_condition(prob: float, txt: torch.Tensor, null_txt: torch.Te...
  function prepare_visual_condition_uncausal (line 186) | def prepare_visual_condition_uncausal(
  function prepare_visual_condition_causal (line 316) | def prepare_visual_condition_causal(x: torch.Tensor, condition_config: d...
  function get_batch_loss (line 410) | def get_batch_loss(model_pred, v_t, masks=None):
  function warmup_ae (line 454) | def warmup_ae(model_ae: nn.Module, shapes: list[tuple[int, ...]], device...

FILE: scripts/cnv/meta.py
  function set_parallel (line 10) | def set_parallel(num_workers: int = None) -> callable:
  function get_video_info (line 21) | def get_video_info(path: str) -> pd.Series:
  function parse_args (line 43) | def parse_args():
  function main (line 53) | def main():

FILE: scripts/cnv/shard.py
  function shard_parquet (line 14) | def shard_parquet(input_path, k):

FILE: scripts/diffusion/inference.py
  function main (line 42) | def main():

FILE: scripts/diffusion/train.py
  function main (line 83) | def main():

FILE: scripts/vae/inference.py
  function main (line 19) | def main():

FILE: scripts/vae/stats.py
  function main (line 17) | def main():

FILE: scripts/vae/train.py
  function main (line 56) | def main():

FILE: setup.py
  function fetch_requirements (line 6) | def fetch_requirements(paths) -> List[str]:
  function fetch_readme (line 25) | def fetch_readme() -> str: