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Functions460 in github.com/MeiGen-AI/InfiniteTalk

⨍ Functions 460◇ Types & classes 102
↓ 1 callersFunctionproject
( v0: torch.Tensor, # [B, C, T, H, W] v1: torch.Tensor, # [B, C, T, H, W] )
wan/utils/multitalk_utils.py:327
↓ 1 callersFunctionrand_name
(length=8, suffix='')
wan/utils/multitalk_utils.py:195
↓ 1 callersFunctionrand_name
(length=8, suffix='')
wan/utils/utils.py:21
↓ 1 callersMethodreparameterize
(self, mu, log_var)
wan/modules/vae.py:570
↓ 1 callersMethodresize_crop
Resize, center crop and normalize for decord loaded video (torch.Tensor type) Parameters: video - video to process (torch.
wan/utils/vace_processor.py:113
↓ 1 callersFunctionrotate_half
(x)
wan/utils/multitalk_utils.py:147
↓ 1 callersFunctionrun_graio_demo
(args)
app.py:431
↓ 1 callersFunctionsave_video
(frames, save_path, fps, quality=9, ffmpeg_params=None)
wan/utils/multitalk_utils.py:239
↓ 1 callersFunctionseg_video
(video_path, scene_list, output_dir)
wan/utils/segvideo.py:12
↓ 1 callersMethodset_area
(self, area)
wan/utils/vace_processor.py:105
↓ 1 callersFunctionshot_detect
(video_path, output_dir)
wan/utils/segvideo.py:27
↓ 1 callersFunctionsinusoidal_embedding_1d
(dim, position)
wan/modules/multitalk_model.py:28
↓ 1 callersMethodstep
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.
wan/utils/fm_solvers_unipc.py:657
↓ 1 callersMethodteacache_init
( self, use_ret_steps=True, teacache_thresh=0.2, sample_steps=40, mode
wan/modules/multitalk_model.py:557
↓ 1 callersMethodtime_shift
(self, mu: float, sigma: float, t: torch.Tensor)
wan/utils/fm_solvers.py:339
↓ 1 callersMethodtime_shift
(self, mu: float, sigma: float, t: torch.Tensor)
wan/utils/fm_solvers_unipc.py:278
↓ 1 callersFunctiontimestep_transform
( t, shift=5.0, num_timesteps=1000, )
wan/multitalk.py:95
↓ 1 callersFunctionto_param_dtype_fp32only
(model, param_dtype)
wan/multitalk.py:45
↓ 1 callersMethodtransform
Forward STFT => returns magnitude, phase Output shape => (batch, freq_bins, frames)
kokoro/custom_stft.py:103
↓ 1 callersMethodunpatchify
r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features
wan/modules/multitalk_model.py:777
↓ 1 callersMethodwaterfall_last
( tokens: List[en.MToken], next_count: int, waterfall: List[str] = ['!.?…', ':;', ',—'
kokoro/pipeline.py:187
FunctionGET_DTYPE
()
wan/wan_lora.py:10
Method__call__
(self, sequence, **kwargs)
wan/modules/tokenizers.py:49
Method__call__
(self, texts, device)
wan/modules/t5.py:528
Method__call__
(self, prompt, system_prompt=None, tar_lang="zh",
wan/utils/prompt_extend.py:189
Method__call__
( self, text: Union[str, List[str]], voice: Optional[str] = None, speed: Union
kokoro/pipeline.py:364
Method__getitem__
(self, index)
kokoro/pipeline.py:357
Method__init__
(self, yaml_path)
tools/convert_img_to_video.py:7
Method__init__
r""" Initializes the image-to-video generation model components. Args: config (EasyDict): Object containi
wan/first_last_frame2video.py:34
Method__init__
r""" Initializes the image-to-video generation model components. Args: config (EasyDict): Object containi
wan/image2video.py:34
Method__init__
(self, wan_model)
wan/wan_lora.py:15
Method__init__
r""" Initializes the Wan text-to-video generation model components. Args: config (EasyDict): Object conta
wan/text2video.py:31
Method__init__
r""" Initializes the Wan text-to-video generation model components. Args: config (EasyDict): Object conta
wan/vace.py:39
Method__init__
(self, config, checkpoint_dir, use_usp=False,
wan/vace.py:480
Method__init__
r""" Initializes the image-to-video generation model components. Args: config (EasyDict): Object containi
wan/multitalk.py:110
Method__init__
(self, name, seq_len=None, clean=None, **kwargs)
wan/modules/tokenizers.py:39
Method__init__
(self, dim, num_heads, causal=False, attn_
wan/modules/clip.py:55
Method__init__
(self, dim, mlp_ratio, num_heads, post_nor
wan/modules/clip.py:114
Method__init__
(self, dim, mlp_ratio, num_heads, activati
wan/modules/clip.py:158
Method__init__
(self, image_size=224, patch_size=16, dim=768,
wan/modules/clip.py:211
Method__init__
(self, **kwargs)
wan/modules/clip.py:305
Method__init__
(self, embed_dim=1024, image_size=224, patch_size=14,
wan/modules/clip.py:330
Method__init__
(self, dtype, device, checkpoint_path, tokenizer_path)
wan/modules/clip.py:503
Method__init__
(self, dim, eps=1e-6)
wan/modules/t5.py:60
Method__init__
(self, dim, dim_attn, num_heads, dropout=0.1)
wan/modules/t5.py:76
Method__init__
(self, dim, dim_ffn, dropout=0.1)
wan/modules/t5.py:130
Method__init__
(self, dim, dim_attn, dim_ffn, num_heads,
wan/modules/t5.py:151
Method__init__
(self, dim, dim_attn, dim_ffn, num_heads,
wan/modules/t5.py:185
Method__init__
(self, num_buckets, num_heads, bidirectional, max_dist=128)
wan/modules/t5.py:228
Method__init__
(self, vocab, dim, dim_attn, dim_ffn,
wan/modules/t5.py:274
Method__init__
(self, vocab, dim, dim_attn, dim_ffn,
wan/modules/t5.py:322
Method__init__
( self, text_len, dtype=torch.bfloat16, device=torch.cuda.current_device(),
wan/modules/t5.py:479
Method__init__
(self, *args, **kwargs)
wan/modules/vae.py:22
Method__init__
(self, dim, channel_first=True, images=True, bias=False)
wan/modules/vae.py:41
Method__init__
(self, dim, mode)
wan/modules/vae.py:68
Method__init__
(self, in_dim, out_dim, dropout=0.0)
wan/modules/vae.py:188
Method__init__
(self, dim)
wan/modules/vae.py:228
Method__init__
(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4],
wan/modules/vae.py:267
Method__init__
(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4],
wan/modules/vae.py:371
Method__init__
(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4],
wan/modules/vae.py:485
Method__init__
(self, dim, eps=1e-5)
wan/modules/multitalk_model.py:81
Method__init__
(self, dim, eps=1e-6, elementwise_affine=False)
wan/modules/multitalk_model.py:100
Method__init__
(self, dim, num_heads, window_size=(-1, -1),
wan/modules/multitalk_model.py:117
Method__init__
(self, dim, num_heads, window_size=(-1, -1),
wan/modules/multitalk_model.py:177
Method__init__
(self, cross_attn_type, dim, ffn_dim, num_
wan/modules/multitalk_model.py:218
Method__init__
(self, in_dim, out_dim)
wan/modules/multitalk_model.py:353
Method__init__
( self, seq_len=5, seq_len_vf=12, blocks=12, channels=768,
wan/modules/multitalk_model.py:367
Method__init__
(self, model_type='i2v', patch_size=(1, 2, 2), text_len=512
wan/modules/multitalk_model.py:443
Method__init__
(self, cross_attn_type, dim, ffn_dim, num_
wan/modules/vace_model.py:12
Method__init__
(self, cross_attn_type, dim, ffn_dim, num_
wan/modules/vace_model.py:44
Method__init__
( self, dim: int, encoder_hidden_states_dim: int, num_heads: int, qkv_
wan/modules/attention.py:283
Method__init__
(self, dim, num_heads, dropout=0.1, eps=1e-5)
wan/modules/xlm_roberta.py:12
Method__init__
(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5)
wan/modules/xlm_roberta.py:51
Method__init__
(self, dim, eps=1e-5)
wan/modules/model.py:75
Method__init__
(self, dim, eps=1e-6, elementwise_affine=False)
wan/modules/model.py:94
Method__init__
(self, dim, num_heads, window_size=(-1, -1),
wan/modules/model.py:107
Method__init__
(self, dim, num_heads, window_size=(-1, -1),
wan/modules/model.py:189
Method__init__
(self, cross_attn_type, dim, ffn_dim, num_
wan/modules/model.py:240
Method__init__
(self, in_dim, out_dim, flf_pos_emb=False)
wan/modules/model.py:352
Method__init__
r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Mo
wan/modules/model.py:383
Method__init__
(self, head_dim, )
wan/utils/multitalk_utils.py:156
Method__init__
Args: api_key: The API key for Dash Scope authentication and access to related services. model_name: Model name, 'qwe
wan/utils/prompt_extend.py:215
Method__init__
Args: model_name: Use predefined model names such as 'QwenVL2.5_7B' and 'Qwen2.5_14B', which are specific version
wan/utils/prompt_extend.py:373
Method__init__
(self, downsample=None, seq_len=None)
wan/utils/vace_processor.py:11
Method__init__
(self, downsample, min_area, max_area, min_fps, max_fps, zero_start, seq_len, keep_last, **kw
wan/utils/vace_processor.py:93
Method__init__
( self, num_train_timesteps: int = 1000, solver_order: int = 2, prediction_typ
wan/utils/fm_solvers.py:131
Method__init__
( self, num_train_timesteps: int = 1000, solver_order: int = 2,
wan/utils/fm_solvers_unipc.py:79
Method__init__
Initialize a KPipeline. Args: lang_code: Language code for G2P processing model: KModel instance, True to cre
kokoro/pipeline.py:64
Method__init__
(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64)
kokoro/istftnet.py:35
Method__init__
(self, filter_length=800, hop_length=200, win_length=800, window='hann')
kokoro/istftnet.py:81
Method__init__
(self, samp_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, noise_std=0.003,
kokoro/istftnet.py:123
Method__init__
(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voic
kokoro/istftnet.py:229
Method__init__
(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, up
kokoro/istftnet.py:258
Method__init__
(self, layer_type)
kokoro/istftnet.py:329
Method__init__
(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), upsample='none', dropout_p=0.0)
kokoro/istftnet.py:341
Method__init__
(self, dim_in, style_dim, dim_out, resblock_kernel_sizes, upsample_rates,
kokoro/istftnet.py:385
Method__init__
( self, filter_length=800, hop_length=200, win_length=800, window="han
kokoro/custom_stft.py:18
Method__init__
(self, in_dim, out_dim, bias=True, w_init_gain='linear')
kokoro/modules.py:12
Method__init__
(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2))
kokoro/modules.py:36
Method__init__
(self, style_dim, channels, eps=1e-5)
kokoro/modules.py:73
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