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Functions508 in github.com/Francis-Rings/FlashPortrait

⨍ Functions 508◇ Types & classes 101
Method__init__
(self, dim, mode)
wan/models/wan_vae3_8.py:78
Method__init__
(self, in_dim, out_dim, dropout=0.0)
wan/models/wan_vae3_8.py:200
Method__init__
(self, dim)
wan/models/wan_vae3_8.py:248
Method__init__
( self, in_channels, out_channels, factor_t, factor_s=1, )
wan/models/wan_vae3_8.py:323
Method__init__
(self, in_dim, out_dim, dropout, mult,
wan/models/wan_vae3_8.py:422
Method__init__
(self, in_dim, out_dim, dropout, mult,
wan/models/wan_vae3_8.py:462
Method__init__
( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2,
wan/models/wan_vae3_8.py:507
Method__init__
( self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2,
wan/models/wan_vae3_8.py:623
Method__init__
( self, dim=160, dec_dim=256, z_dim=16, dim_mult=[1, 2, 4, 4],
wan/models/wan_vae3_8.py:741
Method__init__
( self, latent_channels=48, c_dim=160, vae_pth=None, dim_mult=[1, 2, 4
wan/models/wan_vae3_8.py:896
Method__init__
( self, tokenizer: AutoTokenizer, text_encoder: WanT5EncoderModel, vae: Autoen
wan/pipeline/pipeline_wan_fun_inpaint.py:168
Method__init__
( self, tokenizer: AutoTokenizer, text_encoder: WanT5EncoderModel, vae: Autoen
wan/pipeline/pipeline_wan_fun_control.py:169
Method__init__
( self, tokenizer: AutoTokenizer, text_encoder: WanT5EncoderModel, vae: Autoen
wan/pipeline/pipeline_wan.py:124
Method__init__
( self, tokenizer: AutoTokenizer, text_encoder: WanT5EncoderModel,
wan/pipeline/pipeline_wan_long.py:206
Method__len__
(self)
wan/utils/step_distill_scheduler.py:227
Method__len__
(self)
wan/utils/step_distill_scheduler.py:309
Method__len__
(self)
wan/utils/fm_solvers.py:856
Method__len__
(self)
wan/utils/fm_solvers_unipc.py:798
Method__len__
(self)
wan/data/portrait_data.py:123
Method_set_gradient_checkpointing
(self, *args, **kwargs)
wan/models/wan_transformer3d.py:762
Method_set_gradient_checkpointing
(self, *args, **kwargs)
wan/models/wan_vae.py:653
Method_set_gradient_checkpointing
(self, *args, **kwargs)
wan/models/wan_vae3_8.py:1024
Method_sigma_to_t
(self, sigma)
wan/utils/fm_solvers.py:330
Method_sigma_to_t
(self, sigma)
wan/utils/fm_solvers_unipc.py:268
Functionadaptive_fast_rope_apply_qk
(q, k, grid_sizes, freqs)
wan/models/__init__.py:95
Methodadd_noise
Add noise to samples.
wan/utils/step_distill_scheduler.py:302
Methodadd_noise
( self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.
wan/utils/fm_solvers.py:815
Methodadd_noise
( self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.
wan/utils/fm_solvers_unipc.py:757
Functionapply_checkpointing
Apply selective activation checkpointing. Selectivity is defined as a percentage p, which means we apply ac on p of the total blocks. p
wan/utils/ac_handle.py:16
Methodapply_to
(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True)
wan/utils/lora_utils.py:269
Methodattention_kwargs
(self)
wan/pipeline/pipeline_wan_fun_inpaint.py:462
Methodattention_kwargs
(self)
wan/pipeline/pipeline_wan_fun_control.py:461
Methodattention_kwargs
(self)
wan/pipeline/pipeline_wan.py:377
Methodattention_kwargs
(self)
wan/pipeline/pipeline_wan_long.py:509
Methodbegin_index
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
wan/utils/fm_solvers.py:209
Methodbegin_index
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
wan/utils/fm_solvers_unipc.py:141
Functioncfg_skip
()
wan/utils/cfg_optimization.py:5
Methodcreate_custom_forward
(module)
wan/models/wan_transformer3d.py:1037
Methodcreate_modules
( is_unet: bool, root_module: torch.nn.Module, target_replace_modules: Lis
wan/utils/lora_utils.py:191
Functioncreate_network
( multiplier: float, network_dim: Optional[int], network_alpha: Optional[float], text_encoder:
wan/utils/lora_utils.py:355
Methodcross_attn_ffn
(x, context, context_lens, e, emo_proj=None, emo_context_lens=None, latents_num_frames=21, ip_scale=1.0, emo_a
wan/models/wan_transformer3d.py:523
Methodcustom_forward
(*inputs)
wan/models/wan_transformer3d.py:1038
Methoddecode
High-level decode with tiling support
wan/models/wan_vae_tiled.py:282
Methoddecode
(self, z: torch.Tensor, return_dict: bool = True)
wan/models/wan_vae.py:831
Methoddecode
(self, latents, parallel=False)
wan/models/wan_vae_tiny.py:253
Methoddecode
(self, latents, parallel=False)
wan/models/wan_vae_tiny.py:302
Methoddecode_video
(self, vid_enc)
wan/models/wan_vae_tiny.py:269
Methoddecode_video
(self, vid_enc)
wan/models/wan_vae_tiny.py:318
Functiondecorator
(func)
wan/utils/utils.py:390
Functiondecorator
(func)
wan/utils/cfg_optimization.py:6
Methoddisable_cfg_skip
(self)
wan/models/wan_transformer3d.py:813
Methoddisable_distill
Disable step distillation mode (use original scheduler).
wan/utils/step_distill_scheduler.py:272
Methoddisable_riflex
(self)
wan/models/wan_transformer3d.py:834
Methoddisable_teacache
(self)
wan/models/wan_transformer3d.py:792
Methodenable_distill
Enable step distillation mode.
wan/utils/step_distill_scheduler.py:268
Methodenable_riflex
( self, k = 6, L_test = 66, L_test_scale = 4.886, )
wan/models/wan_transformer3d.py:818
Methodencode
High-level encode with tiling support
wan/models/wan_vae_tiled.py:264
Methodencode
( self, x: torch.Tensor, return_dict: bool = True )
wan/models/wan_vae.py:810
Methodencode_video
(self, vid)
wan/models/wan_vae_tiny.py:265
Methodencode_video
(self, vid)
wan/models/wan_vae_tiny.py:314
Methodenumerate_params
(loras)
wan/utils/lora_utils.py:303
Methodfilter_kwargs
(cls, kwargs)
wan/models/wan_text_encoder.py:308
Methodfilter_kwargs
(cls, kwargs)
wan/models/wan_vae.py:700
Methodfilter_kwargs
(cls, kwargs)
wan/models/wan_vae.py:840
Methodfilter_kwargs
(cls, kwargs)
wan/models/wan_image_encoder.py:532
Methodfilter_kwargs
(cls, kwargs)
wan/models/wan_vae3_8.py:1071
Functionfind_replacement
(a)
train_portrait.py:68
Methodforward
(self, x, *args, **kwargs)
wan/utils/lora_utils.py:81
Methodforward
(self, src_image, face_box=None, pre_pts=None, iterations=3)
wan/models/face_align.py:51
Methodforward
(self, src_image, reset=False, pre_rect=None)
wan/models/face_model.py:339
Methodforward
(self, x)
wan/models/pdf.py:297
Methodforward
(self, x)
wan/models/pdf.py:320
Methodforward
(self, x)
wan/models/pdf.py:442
Methodforward
Args: x (torch.Tensor): image features shape (b, n1, D) latent (torch.Tensor): latent features
wan/models/portrait_encoder.py:42
Methodforward
(self, x)
wan/models/portrait_encoder.py:108
Methodforward
(self, adapter_embeds)
wan/models/portrait_encoder.py:134
Methodforward
(self, x)
wan/models/wan_camera_adapter.py:21
Methodforward
(self, x)
wan/models/wan_camera_adapter.py:51
Methodforward
r""" Args: x(Tensor): Shape [B, L, C]
wan/models/wan_transformer3d.py:181
Methodforward
r""" Args: x(Tensor): Shape [B, L, C]
wan/models/wan_transformer3d.py:197
Methodforward
r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor
wan/models/wan_transformer3d.py:230
Methodforward
r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B]
wan/models/wan_transformer3d.py:267
Methodforward
r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B]
wan/models/wan_transformer3d.py:314
Methodforward
r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B]
wan/models/wan_transformer3d.py:407
Methodforward
r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, 6, C] seq_lens(Tensor): Shape [B], length of ea
wan/models/wan_transformer3d.py:474
Methodforward
(self, image_embeds)
wan/models/wan_transformer3d.py:598
Methodforward
r""" Forward pass through the diffusion model Args: x (List[Tensor]): List of input video tensors, each w
wan/models/wan_transformer3d.py:862
Methodforward
x: [B, L, C].
wan/models/wan_xlm_roberta.py:27
Methodforward
(self, x, mask)
wan/models/wan_xlm_roberta.py:66
Methodforward
ids: [B, L] of torch.LongTensor.
wan/models/wan_xlm_roberta.py:118
Methodforward
(self, x)
wan/models/wan_text_encoder.py:39
Methodforward
(self, x)
wan/models/wan_text_encoder.py:51
Methodforward
x: [B, L1, C]. context: [B, L2, C] or None. mask: [B, L2] or [B, L1, L2] or None.
wan/models/wan_text_encoder.py:75
Methodforward
(self, x)
wan/models/wan_text_encoder.py:125
Methodforward
(self, x, mask=None, pos_bias=None)
wan/models/wan_text_encoder.py:158
Methodforward
(self, x, mask=None, encoder_states=None, enco
wan/models/wan_text_encoder.py:193
Methodforward
(self, lq, lk)
wan/models/wan_text_encoder.py:219
Methodforward
( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.
wan/models/wan_text_encoder.py:291
Methodforward
(self, x, cache_x=None)
wan/models/wan_vae.py:32
Methodforward
Fix bfloat16 support for nearest neighbor interpolation.
wan/models/wan_vae.py:63
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