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Functions2,606 in github.com/NVlabs/Sana

Method__init__
(self, root: str, transform: Optional[Callable] = None, return_dict: bool = False)
diffusion/model/dc_ae/efficientvit/apps/utils/image.py:105
Method__init__
( self, data_dirs: Union[str, list[str]], splits: Optional[Union[str, list[Optional[st
diffusion/model/dc_ae/efficientvit/apps/utils/image.py:127
Method__init__
(self, model: nn.Module, decay: float, warmup_steps=2000)
diffusion/model/dc_ae/efficientvit/apps/utils/ema.py:35
Method__init__
( self, optimizer: torch.optim.Optimizer, warmup_steps: int, warmup_lr: float,
diffusion/model/dc_ae/efficientvit/apps/utils/lr.py:61
Method__init__
(self, **kwargs)
diffusion/model/dc_ae/efficientvit/apps/trainer/run_config.py:53
Method__init__
(self, cfg: EncoderConfig, is_video: bool)
diffusion/model/dc_ae/efficientvit/models/efficientvit/dc_ae.py:376
Method__init__
(self, cfg: DecoderConfig, is_video: bool)
diffusion/model/dc_ae/efficientvit/models/efficientvit/dc_ae.py:441
Method__init__
(self, cfg: DCAEWithTemporalEncoderConfig)
diffusion/model/dc_ae/efficientvit/models/efficientvit/dc_ae_with_temporal.py:369
Method__init__
(self, cfg: DCAEWithTemporalDecoderConfig)
diffusion/model/dc_ae/efficientvit/models/efficientvit/dc_ae_with_temporal.py:443
Method__init__
( self, main: nn.Module, shortcut: Optional[nn.Module], post_act=None,
diffusion/model/dc_ae/efficientvit/models/nn/drop.py:71
Method__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:65
Method__init__
( self, mode="bicubic", size: Optional[int | tuple[int, int] | list[int]] = None,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:140
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int, factor:
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:163
Method__init__
( self, in_channels: int, out_channels: int, factor: int, temporal_dow
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:190
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int, factor:
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:231
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int, factor:
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:257
Method__init__
( self, in_channels: int, out_channels: int, factor: int, temporal_ups
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:295
Method__init__
( self, in_features: int, out_features: int, use_bias=True, dropout=0,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:332
Method__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:418
Method__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:473
Method__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:519
Method__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:583
Method__init__
( self, in_channels: int, out_channels: int, heads: Optional[int] = None,
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:673
Method__init__
( self, in_channels: int, heads_ratio: float = 1.0, dim=32, expand_rat
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:853
Method__init__
( self, main: Optional[nn.Module], shortcut: Optional[nn.Module], post_act=Non
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:923
Method__init__
( self, inputs: dict[str, nn.Module], merge: str, post_input: Optional[nn.Modu
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:956
Method__init__
(self, op_list: list[Optional[nn.Module]])
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:993
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int | tuple[int] = 3
diffusion/model/dc_ae/efficientvit/models/nn/ops_3d.py:74
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int | tuple[int],
diffusion/model/dc_ae/efficientvit/models/nn/ops_3d.py:376
Method__init__
( self, in_channels: int, out_channels: int, spatial_factor: int, temp
diffusion/model/dc_ae/efficientvit/models/nn/ops_3d.py:423
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int | tuple[int],
diffusion/model/dc_ae/efficientvit/models/nn/ops_3d.py:450
Method__init__
( self, in_channels: int, out_channels: int, spatial_factor: int, temp
diffusion/model/dc_ae/efficientvit/models/nn/ops_3d.py:498
Method__init__
( self, num_features: int, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True )
diffusion/model/dc_ae/efficientvit/models/nn/norm.py:57
Method__init__
( self, vae: AutoencoderKLCausalLTX2Video, *, scaling_factor: float | None = N
diffusion/model/ltx2/streaming_decoder.py:42
Method__init__
(self)
diffusion/model/ltx2/causal_vae.py:99
Method__init__
( self, in_channels: int, out_channels: int, kernel_size: int | tuple[int, int
diffusion/model/ltx2/causal_vae.py:266
Method__init__
( self, in_channels: int, out_channels: int | None = None, dropout: float = 0.
diffusion/model/ltx2/causal_vae.py:444
Method__init__
( self, in_channels: int, out_channels: int, stride: int | tuple[int, int, int
diffusion/model/ltx2/causal_vae.py:562
Method__init__
( self, in_channels: int, stride: int | tuple[int, int, int] = 1, residual: bo
diffusion/model/ltx2/causal_vae.py:694
Method__init__
( self, in_channels: int, out_channels: int | None = None, num_layers: int = 1
diffusion/model/ltx2/causal_vae.py:804
Method__init__
( self, in_channels: int, num_layers: int = 1, dropout: float = 0.0, r
diffusion/model/ltx2/causal_vae.py:915
Method__init__
( self, in_channels: int, out_channels: int | None = None, num_layers: int = 1
diffusion/model/ltx2/causal_vae.py:995
Method__init__
( self, in_channels: int = 3, out_channels: int = 128, block_out_channels: tup
diffusion/model/ltx2/causal_vae.py:1124
Method__init__
( self, in_channels: int = 128, out_channels: int = 3, block_out_channels: tup
diffusion/model/ltx2/causal_vae.py:1276
Method__init__
( self, in_channels: int = 3, out_channels: int = 3, latent_channels: int = 12
diffusion/model/ltx2/causal_vae.py:1472
Method__init__
Initializes the tokenizer and text encoder model. Args: model_id (str): The model identifier from the Hugging Face Hub.
diffusion/model/qwen/qwen_vl.py:15
Method__init__
( self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0,
diffusion/longsana/utils/scheduler.py:87
Method__init__
(self, fsdp_module: torch.nn.Module, decay: float = 0.999)
diffusion/longsana/utils/distributed.py:101
Method__init__
(self, prompt_path, extended_prompt_path=None)
diffusion/longsana/utils/dataset.py:16
Method__init__
(self, prompt_path: str, switch_prompt_path: str)
diffusion/longsana/utils/dataset.py:56
Method__init__
(self, data_path: str, max_pair: int = int(1e8))
diffusion/longsana/utils/dataset.py:82
Method__init__
(self, data_path: str, max_pair: int = int(1e8))
diffusion/longsana/utils/dataset.py:107
Method__init__
Args: data_dir (str): Path to the directory containing: - target_crop_info_*.json (metadata file)
diffusion/longsana/utils/dataset.py:148
Method__init__
(self, prompt_path: str, field: str = "prompts", cache_dir: str | None = None)
diffusion/longsana/utils/dataset.py:282
Method__init__
(self, sana_model, flow_shift: float = 3.0)
diffusion/longsana/utils/model_wrapper.py:17
Method__init__
(self, sana_cfg, device: torch.device, dtype: torch.dtype = torch.float32)
diffusion/longsana/utils/model_wrapper.py:168
Method__init__
Initialize the DMD (Distribution Matching Distillation) module. This class is self-contained and compute generator and fake score los
diffusion/longsana/model/dmd_sana.py:24
Method__init__
Initialize the ODERegression module. This class is self-contained and compute generator losses in the forward pass given prec
diffusion/longsana/model/ode_regression_sana.py:82
Method__init__
Initialize the streaming training model. Args: base_model: underlying model (DMD, DMDSwitch, etc.) config: c
diffusion/longsana/model/streaming_sana_long.py:36
Method__init__
(self, config)
diffusion/longsana/trainer/longsana_trainer.py:27
Method__init__
(self, config)
diffusion/longsana/trainer/ode.py:28
Method__init__
(self, config)
diffusion/longsana/trainer/self_forcing_trainer.py:26
Method__init__
(self, args, device, generator, text_encoder, vae, **kwargs)
diffusion/longsana/pipeline/sana_inference_interactive_pipeline_long_chunk.py:11
Method__init__
Sana training pipeline, refer to SelfForcingTrainingPipeline's interface Args: denoising_step_list: denoising step list
diffusion/longsana/pipeline/sana_training_pipeline.py:15
Method__init__
SANA inference pipeline: generate a full video without gradients. The initialization signature is consistent with the use in Trainer
diffusion/longsana/pipeline/sana_inference_pipeline.py:14
Method__init__
(self, *args, **kwargs)
diffusion/longsana/pipeline/sana_switch_training_pipeline.py:15
Method__init__
(self, args, device, generator, text_encoder, vae, **kwargs)
diffusion/longsana/pipeline/sana_inference_interactive_pipeline.py:11
Method__init__
(self)
diffusion/data/transforms.py:115
Method__init__
(self, size)
diffusion/data/transforms.py:180
Method__init__
( self, size, interpolation_mode="bilinear", )
diffusion/data/transforms.py:200
Method__init__
( self, data_dir="", transform=None, resolution=256, load_vae_feat=Fal
diffusion/data/datasets/sana_data.py:38
Method__init__
( self, data_dir="", meta_path=None, cache_dir="/cache/data/sana-webds-meta",
diffusion/data/datasets/sana_data.py:223
Method__init__
( self, data_dir="", meta_path=None, cache_dir="/cache/data/sana-webds-meta",
diffusion/data/datasets/sana_data_multi_scale.py:40
Method__init__
( self, data_dir={}, transform=None, load_vae_feat=False, load_text_fe
diffusion/data/datasets/video/sana_video_data.py:44
Method__init__
(self, prompts, original_indices=None)
diffusion/data/datasets/video/sana_video_data.py:482
Method__init__
(self, fname, index_file=None, verbose=True, cleanup_callback=None)
diffusion/data/wids/wids_mmtar.py:66
Method__init__
(self, path)
diffusion/data/wids/wids_dl.py:38
Method__init__
(self, file, index_file=find_index_file, verbose=True)
diffusion/data/wids/wids_tar.py:37
Method__init__
Initialize a new LRU cache with the given capacity.
diffusion/data/wids/wids_lru.py:22
Method__init__
( self, *, path=None, stream=None, md5sum=None, expected_size=
diffusion/data/wids/wids.py:250
Method__init__
Create a ShardListDataset. Args: shards: a list of (filename, length) pairs or a URL pointing to a JSON descriptor file
diffusion/data/wids/wids.py:488
Method__init__
Create a ShardListDataset. Args: shards: a list of (filename, length) pairs or a URL pointing to a JSON descriptor file
diffusion/data/wids/wids.py:676
Method__init__
(self, dataset, *, lengths=None, seed=0, shufflefirst=False)
diffusion/data/wids/wids.py:860
Method__init__
( self, dataset, *, num_samples=None, chunksize=2000, seed=0,
diffusion/data/wids/wids.py:884
Method__init__
( self, dataset: Dataset, num_replicas: Optional[int] = None, num_samples: Opt
diffusion/data/wids/wids.py:975
Method__init__
( self, refiner_root: str | Path, gemma_root: str | Path, *, dtype: to
diffusion/refiner/diffusers_ltx2_refiner.py:53
Method__init__
( self, refiner: DiffusersLTX2Refiner, *, prompt_embeds: torch.Tensor,
diffusion/refiner/diffusers_ltx2_refiner.py:911
Method__init__
(self, *, enabled: bool, device: torch.device, block_idx: int)
diffusion/refiner/diffusers_ltx2_refiner.py:2095
Method__init__
(self, profiler: _RefinerCudaProfiler, name: str)
diffusion/refiner/diffusers_ltx2_refiner.py:2140
Method__init__
(self, *, enabled: bool, device: torch.device, label: str)
diffusion/refiner/diffusers_ltx2_refiner.py:2190
Method__init__
(self, profiler: _RefinerLayerCudaProfiler, name: str)
diffusion/refiner/diffusers_ltx2_refiner.py:2233
Method__init__
( self, guidance_scale: float = 7.5, adaptive_projected_guidance_momentum: Optional[fl
diffusion/guiders/adaptive_projected_guidance.py:49
Method__init__
(self, momentum: float)
diffusion/guiders/adaptive_projected_guidance.py:97
Method__init__
( self, instance_data_root, instance_prompt, class_prompt, class_data_
train_scripts/train_dreambooth_lora_sana.py:623
Method__init__
(self, prompt, num_samples)
train_scripts/train_dreambooth_lora_sana.py:806
Method__init__
(self, device)
train_scripts/sol_rl/train_utils.py:90
Method__init__
(self, te_linear)
train_scripts/sol_rl/train_utils.py:386
Method__init__
(self)
app/app_sana_inpaint.py:96
Method__init__
(self)
app/app_sana_inpaint.py:173
Method__init__
( self, config: Optional[str] = "configs/sana_config/1024ms/Sana_1600M_img1024.yaml", )
app/sana_controlnet_pipeline.py:99
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