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Functions2,606 in github.com/NVlabs/Sana

⨍ Functions 2,606◇ Types & classes 463↳ Endpoints 11
Method__getitem__
(self, idx)
diffusion/post_training/prompt_dataset.py:63
Method__getitem__
(self, index: int)
diffusion/model/dc_ae/efficientvit/apps/utils/image.py:110
Method__getitem__
(self, index: int, skip_image=False)
diffusion/model/dc_ae/efficientvit/apps/utils/image.py:169
Method__getitem__
(self, idx)
diffusion/longsana/utils/dataset.py:30
Method__getitem__
(self, idx)
diffusion/longsana/utils/dataset.py:73
Method__getitem__
Outputs: - prompts: List of Strings - latents: Tensor of shape (num_denoising_steps, num_frames, num_channels, height
diffusion/longsana/utils/dataset.py:91
Method__getitem__
Outputs: - prompts: List of Strings - latents: Tensor of shape (num_denoising_steps, num_frames, num_channels, height
diffusion/longsana/utils/dataset.py:127
Method__getitem__
Returns: dict: A dictionary containing: - image: PIL Image - caption: str - targe
diffusion/longsana/utils/dataset.py:197
Method__getitem__
(self, idx: int)
diffusion/longsana/utils/dataset.py:306
Method__getitem__
(self, idx)
diffusion/data/datasets/sana_data.py:158
Method__getitem__
(self, idx)
diffusion/data/datasets/sana_data.py:383
Method__getitem__
(self, idx)
diffusion/data/datasets/sana_data_multi_scale.py:110
Method__getitem__
(self, idx)
diffusion/data/datasets/sana_data_multi_scale.py:252
Method__getitem__
(self, idx)
diffusion/data/datasets/video/sana_video_data.py:414
Method__getitem__
(self, idx)
diffusion/data/datasets/video/sana_video_data.py:499
Method__getitem__
(self, key)
diffusion/data/wids/wids_mmtar.py:126
Method__getitem__
Return the value associated with the given key, or None.
diffusion/data/wids/wids_lru.py:28
Method__getitem__
(self, idx)
diffusion/data/wids/wids.py:306
Method__getitem__
Return the sample corresponding to the given index.
diffusion/data/wids/wids.py:639
Method__getitem__
(self, index)
train_scripts/train_dreambooth_lora_sana.py:759
Method__getitem__
(self, index)
train_scripts/train_dreambooth_lora_sana.py:813
Method__getitem__
(self, idx)
inference_video_scripts/inference_sana_video.py:100
Method__init__
(self, ckpt="damo/mplug_visual-question-answering_coco_large_en", device="gpu")
tools/metrics/dpg_bench/compute_dpg_bench.py:72
Method__init__
(self, files, transforms=None)
tools/metrics/pytorch-fid/compute_fid.py:28
Method__init__
(self, files, transforms=None)
tools/metrics/pytorch-fid/src/pytorch_fid/fid_score.py:83
Method__init__
(self, in_channels, pool_features)
tools/metrics/pytorch-fid/src/pytorch_fid/inception.py:221
Method__init__
(self, in_channels, channels_7x7)
tools/metrics/pytorch-fid/src/pytorch_fid/inception.py:246
Method__init__
(self, in_channels)
tools/metrics/pytorch-fid/src/pytorch_fid/inception.py:274
Method__init__
(self, in_channels)
tools/metrics/pytorch-fid/src/pytorch_fid/inception.py:307
Method__init__
(self, image: Image.Image, objects)
tools/metrics/geneval/evaluation/evaluate_images.py:71
Method__init__
( self, real_path, fake_path, real_flag: str = "img", fake_flag: str =
tools/metrics/clip-score/clip_score.py:32
Method__init__
( self, tar_path, transform=None, external_json_path=None, prompt_key="prompt", tokenizer=None, **kwar
tools/metrics/clip-score/clip_score.py:127
Method__init__
( self, real_path, fake_path, real_flag: str = "img", fake_flag: str = "img", transform=None, tokenize
tools/metrics/clip-score/src/clip_score/clip_score.py:69
Method__init__
(self, input_channel, output_channel, layer_number)
tools/controlnet/annotator/hed/__init__.py:19
Method__init__
(self)
tools/controlnet/annotator/hed/__init__.py:52
Method__init__
( self, model_fn: object, condition: torch.Tensor, uncondition: torch.Tensor,
diffusion/scheduler/self_forcing_flow_euler_sampler.py:294
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
diffusion/scheduler/lcm_scheduler.py:175
Method__init__
( self, num_train_timesteps: int = 1000, prediction_type: str = "trigflow", )
diffusion/scheduler/scm_scheduler.py:66
Method__init__
(self, model_fn, condition, uncondition, cfg_scale, flow_shift=3.0, model_kwargs=None)
diffusion/scheduler/flow_euler_sampler.py:87
Method__init__
( self, model, noise_schedule="linear", diffusion_steps=1000, device="
diffusion/scheduler/sa_sampler.py:27
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
diffusion/scheduler/trigflow_scheduler.py:100
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
diffusion/scheduler/sa_solver_diffusers.py:138
Method__init__
( self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0,
diffusion/scheduler/longlive_flow_euler_sampler.py:93
Method__init__
(self, sana_model, flow_shift: float = 3.0)
diffusion/scheduler/longlive_flow_euler_sampler.py:187
Method__init__
( self, model_fn, condition, model_kwargs, flow_shift=7.0, bas
diffusion/scheduler/longlive_flow_euler_sampler.py:325
Method__init__
( self, sampler: Sampler, dataset: Dataset, batch_size: int, aspect_ra
diffusion/utils/data_sampler.py:176
Method__init__
(self, *args, **kwargs)
diffusion/utils/data_sampler.py:260
Method__init__
(self, num_tasks, log_interval=1, desc="Process")
diffusion/utils/misc.py:142
Method__init__
(self, model, max_frames_to_save=21, trace_batch_nums=[], abort_after_batch_num=None)
diffusion/utils/misc.py:251
Method__init__
(self, *args, **kwargs)
diffusion/utils/optimizer.py:255
Method__init__
( self, params, lr=None, eps=(1e-30, 1e-16), clip_threshold=1.0,
diffusion/utils/optimizer.py:278
Method__init__
( self, params, lr=None, eps=(1e-30, 1e-16), clip_threshold=1.0,
diffusion/utils/optimizer.py:473
Method__init__
(self, fmt=None, datefmt=None, tz=None)
diffusion/utils/logger.py:52
Method__init__
(self, width: int, height: int)
diffusion/utils/action_overlay.py:166
Method__init__
(self, max_size, interpolation=InterpolationMode.BICUBIC, fn="max", fill=0)
diffusion/post_training/rewards.py:24
Method__init__
(self, mean, std)
diffusion/post_training/rewards.py:49
Method__init__
(self, dtype, device)
diffusion/post_training/rewards.py:191
Method__init__
(self, device="cuda", dtype=torch.float32)
diffusion/post_training/rewards.py:256
Method__init__
(self, device="cuda", dtype=torch.float32)
diffusion/post_training/rewards.py:294
Method__init__
( self, parameters: Iterable[torch.nn.Parameter], decay: float = 0.9999, updat
diffusion/post_training/ema.py:7
Method__init__
(self, global_std=False)
diffusion/post_training/stat_tracking.py:6
Method__init__
(self, dataset, split="train")
diffusion/post_training/prompt_dataset.py:35
Method__init__
(self, dataset, split="train")
diffusion/post_training/prompt_dataset.py:54
Method__init__
(self, dataset, batch_size, k, num_replicas, rank, seed=0)
diffusion/post_training/prompt_dataset.py:74
Method__init__
( self, dim: int, scale_factor: float = 1.0, eps: float = 1e-6
diffusion/model/liger_norms.py:68
Method__init__
r"""Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a pic
diffusion/model/dpm_solver.py:33
Method__init__
Create a wrapper class for the forward SDE (EDM type).
diffusion/model/dpm_solver.py:212
Method__init__
(self, *args, **kwargs)
diffusion/model/dpm_solver.py:662
Method__init__
(self, diffusion)
diffusion/model/timestep_sampler.py:79
Method__init__
(self, diffusion, history_per_term=10, uniform_prob=0.001)
diffusion/model/timestep_sampler.py:132
Method__init__
Thanks to DPM-Solver for their code base
diffusion/model/sa_solver.py:25
Method__init__
Construct a SA-Solver The default value for algorithm_type is "data_prediction" and we recommend not to change it to "noise_p
diffusion/model/sa_solver.py:364
Method__init__
Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (flo
diffusion/model/norms.py:183
Method__init__
( self, *, betas, model_mean_type, model_var_type, loss_type,
diffusion/model/gaussian_diffusion.py:182
Method__init__
(self, F: int | list | None, T: int, device: Optional[th.device] = None, dtype: th.dtype = th.float64)
diffusion/model/respace.py:164
Method__init__
(self, use_timesteps, **kwargs)
diffusion/model/respace.py:434
Method__init__
Args: target_model: target model (deeper model) config: ModelGrowthConfig config object
diffusion/model/model_growth_utils.py:39
Method__init__
(self, *args, **kwargs)
diffusion/model/wan2_2/vae.py:25
Method__init__
(self, dim, channel_first=True, images=True, bias=False)
diffusion/model/wan2_2/vae.py:49
Method__init__
(self, dim, mode)
diffusion/model/wan2_2/vae.py:72
Method__init__
(self, in_dim, out_dim, dropout=0.0)
diffusion/model/wan2_2/vae.py:179
Method__init__
(self, dim)
diffusion/model/wan2_2/vae.py:224
Method__init__
( self, in_channels, out_channels, factor_t, factor_s=1, )
diffusion/model/wan2_2/vae.py:293
Method__init__
(self, in_dim, out_dim, dropout, mult, temperal_downsample=False, down_flag=False)
diffusion/model/wan2_2/vae.py:390
Method__init__
(self, in_dim, out_dim, dropout, mult, temperal_upsample=False, up_flag=False)
diffusion/model/wan2_2/vae.py:423
Method__init__
( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2,
diffusion/model/wan2_2/vae.py:461
Method__init__
( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2,
diffusion/model/wan2_2/vae.py:573
Method__init__
( self, dim=160, dec_dim=256, z_dim=16, dim_mult=[1, 2, 4, 4],
diffusion/model/wan2_2/vae.py:688
Method__init__
( self, z_dim=48, c_dim=160, vae_pth=None, dim_mult=[1, 2, 4, 4],
diffusion/model/wan2_2/vae.py:844
Method__init__
(self, name, seq_len=None, clean=None, **kwargs)
diffusion/model/wan/tokenizers.py:39
Method__init__
(self, dim, num_heads, causal=False, attn_dropout=0.0, proj_dropout=0.0)
diffusion/model/wan/clip.py:58
Method__init__
( self, dim, mlp_ratio, num_heads, post_norm=False, causal=Fal
diffusion/model/wan/clip.py:110
Method__init__
(self, dim, mlp_ratio, num_heads, activation="gelu", proj_dropout=0.0, norm_eps=1e-5)
diffusion/model/wan/clip.py:156
Method__init__
( self, image_size=224, patch_size=16, dim=768, mlp_ratio=4, o
diffusion/model/wan/clip.py:204
Method__init__
(self, **kwargs)
diffusion/model/wan/clip.py:294
Method__init__
( self, embed_dim=1024, image_size=224, patch_size=14, vision_dim=1280
diffusion/model/wan/clip.py:318
Method__init__
(self, dtype, device, checkpoint_path, tokenizer_path)
diffusion/model/wan/clip.py:490
Method__init__
(self, dim, eps=1e-6)
diffusion/model/wan/t5.py:51
Method__init__
(self, dim, dim_attn, num_heads, dropout=0.1)
diffusion/model/wan/t5.py:65
Method__init__
(self, dim, dim_ffn, dropout=0.1)
diffusion/model/wan/t5.py:117
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