SYMBOL INDEX (360 symbols across 32 files)

FILE: generate.py
  function _validate_args (line 25) | def _validate_args(args):
  function _parse_args (line 55) | def _parse_args():
  function _init_logging (line 215) | def _init_logging(rank):
  function generate (line 227) | def generate(args):

FILE: gradio/fl2v_14B_singleGPU.py
  function load_model (line 27) | def load_model(value):
  function prompt_enc (line 59) | def prompt_enc(prompt, img_first, img_last, tar_lang):
  function flf2v_generation (line 73) | def flf2v_generation(flf2vid_prompt, flf2vid_image_first, flf2vid_image_...
  function gradio_interface (line 113) | def gradio_interface():
  function _parse_args (line 212) | def _parse_args():

FILE: gradio/i2v_14B_singleGPU.py
  function load_model (line 28) | def load_model(value):
  function prompt_enc (line 87) | def prompt_enc(prompt, img, tar_lang):
  function i2v_generation (line 101) | def i2v_generation(img2vid_prompt, img2vid_image, resolution, sd_steps,
  function gradio_interface (line 149) | def gradio_interface():
  function _parse_args (line 241) | def _parse_args():

FILE: gradio/t2i_14B_singleGPU.py
  function prompt_enc (line 26) | def prompt_enc(prompt, tar_lang):
  function t2i_generation (line 35) | def t2i_generation(txt2img_prompt, resolution, sd_steps, guide_scale,
  function gradio_interface (line 64) | def gradio_interface():
  function _parse_args (line 152) | def _parse_args():

FILE: gradio/t2v_1.3B_singleGPU.py
  function prompt_enc (line 26) | def prompt_enc(prompt, tar_lang):
  function t2v_generation (line 35) | def t2v_generation(txt2vid_prompt, resolution, sd_steps, guide_scale,
  function gradio_interface (line 64) | def gradio_interface():
  function _parse_args (line 154) | def _parse_args():

FILE: gradio/t2v_14B_singleGPU.py
  function prompt_enc (line 26) | def prompt_enc(prompt, tar_lang):
  function t2v_generation (line 35) | def t2v_generation(txt2vid_prompt, resolution, sd_steps, guide_scale,
  function gradio_interface (line 64) | def gradio_interface():
  function _parse_args (line 152) | def _parse_args():

FILE: gradio/vace.py
  class FixedSizeQueue (line 22) | class FixedSizeQueue:
    method __init__ (line 24) | def __init__(self, max_size):
    method add (line 28) | def add(self, item):
    method get (line 33) | def get(self):
    method __repr__ (line 36) | def __repr__(self):
  class VACEInference (line 40) | class VACEInference:
    method __init__ (line 42) | def __init__(self,
    method create_ui (line 70) | def create_ui(self, *args, **kwargs):
    method generate (line 211) | def generate(self, output_gallery, src_video, src_mask, src_ref_image_1,
    method set_callbacks (line 267) | def set_callbacks(self, **kwargs):

FILE: tools/get_track_from_videos.py
  function array_to_npz_bytes (line 12) | def array_to_npz_bytes(arr, path, compressed=True, quant_multi=QUANT_MUL...
  function parse_to_list (line 23) | def parse_to_list(text: str) -> List[List[int]]:
  function load_video_to_frames (line 42) | def load_video_to_frames(
  function sample_grid_points (line 108) | def sample_grid_points(bbox, N):
  function resize_images_to_size (line 141) | def resize_images_to_size(image_list, size=1024):
  function resize_box (line 157) | def resize_box(box, ratios):
  class TrackAnyPoint (line 161) | class TrackAnyPoint():
    method __init__ (line 162) | def __init__(self, n_points=60):
    method __call__ (line 170) | def __call__(self, video_frames: List[Image.Image]):
    method inference (line 186) | def inference(self, frames: np.ndarray, w_ori, h_ori, tracks) -> np.nd...
  function convert_grid_coordinates (line 199) | def convert_grid_coordinates(
  function save_frames_to_mp4 (line 256) | def save_frames_to_mp4(frames, output_path, fps=24, codec='mp4v'):
  function save_yaml (line 291) | def save_yaml(

FILE: tools/plot_user_inputs.py
  function plot_tracks (line 24) | def plot_tracks(
  function unzip_to_array (line 125) | def unzip_to_array(
  function main (line 142) | def main():

FILE: tools/trajectory_editor/app.py
  function array_to_npz_bytes (line 55) | def array_to_npz_bytes(arr, path, compressed=True, quant_multi=QUANT_MUL...
  function load_existing_tracks (line 66) | def load_existing_tracks(path):
  function index (line 76) | def index():
  function upload_image (line 81) | def upload_image():
  function store_tracks (line 103) | def store_tracks():
  function ensure_localhost (line 209) | def ensure_localhost():

FILE: tools/visualize_trajectory.py
  function unzip_to_array (line 30) | def unzip_to_array(
  function get_colors (line 48) | def get_colors(num_colors: int) -> List[Tuple[int, int, int]]:
  function age_to_bgr (line 63) | def age_to_bgr(ratio: float) -> Tuple[int,int,int]:
  function paint_point_track (line 89) | def paint_point_track(

FILE: wan/distributed/fsdp.py
  function shard_model (line 12) | def shard_model(
  function free_model (line 37) | def free_model(model):

FILE: wan/distributed/xdit_context_parallel.py
  function pad_freqs (line 14) | def pad_freqs(original_tensor, target_len):
  function rope_apply (line 28) | def rope_apply(x, grid_sizes, freqs):
  function usp_dit_forward_vace (line 68) | def usp_dit_forward_vace(self, x, vace_context, seq_len, kwargs):
  function usp_dit_forward (line 93) | def usp_dit_forward(
  function usp_attn_forward (line 183) | def usp_attn_forward(self,

FILE: wan/first_last_frame2video.py
  class WanFLF2V (line 32) | class WanFLF2V:
    method __init__ (line 34) | def __init__(
    method generate (line 133) | def generate(self,

FILE: wan/image2video.py
  class WanATI (line 35) | class WanATI:
    method __init__ (line 36) | def __init__(
    method generate (line 135) | def generate(self,

FILE: wan/modules/attention.py
  function flash_attention (line 31) | def flash_attention(
  function attention (line 140) | def attention(

FILE: wan/modules/clip.py
  function pos_interpolate (line 22) | def pos_interpolate(pos, seq_len):
  class QuickGELU (line 41) | class QuickGELU(nn.Module):
    method forward (line 43) | def forward(self, x):
  class LayerNorm (line 47) | class LayerNorm(nn.LayerNorm):
    method forward (line 49) | def forward(self, x):
  class SelfAttention (line 53) | class SelfAttention(nn.Module):
    method __init__ (line 55) | def __init__(self,
    method forward (line 74) | def forward(self, x):
  class SwiGLU (line 94) | class SwiGLU(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, mid_dim):
    method forward (line 106) | def forward(self, x):
  class AttentionBlock (line 112) | class AttentionBlock(nn.Module):
    method __init__ (line 114) | def __init__(self,
    method forward (line 146) | def forward(self, x):
  class AttentionPool (line 156) | class AttentionPool(nn.Module):
    method __init__ (line 158) | def __init__(self,
    method forward (line 186) | def forward(self, x):
  class VisionTransformer (line 209) | class VisionTransformer(nn.Module):
    method __init__ (line 211) | def __init__(self,
    method forward (line 279) | def forward(self, x, interpolation=False, use_31_block=False):
  class XLMRobertaWithHead (line 303) | class XLMRobertaWithHead(XLMRoberta):
    method __init__ (line 305) | def __init__(self, **kwargs):
    method forward (line 315) | def forward(self, ids):
  class XLMRobertaCLIP (line 328) | class XLMRobertaCLIP(nn.Module):
    method __init__ (line 330) | def __init__(self,
    method forward (line 406) | def forward(self, imgs, txt_ids):
    method param_groups (line 418) | def param_groups(self):
  function _clip (line 434) | def _clip(pretrained=False,
  function clip_xlm_roberta_vit_h_14 (line 471) | def clip_xlm_roberta_vit_h_14(
  class CLIPModel (line 501) | class CLIPModel:
    method __init__ (line 503) | def __init__(self, dtype, device, checkpoint_path, tokenizer_path):
    method visual (line 527) | def visual(self, videos):

FILE: wan/modules/model.py
  function sinusoidal_embedding_1d (line 18) | def sinusoidal_embedding_1d(dim, position):
  function rope_params (line 32) | def rope_params(max_seq_len, dim, theta=10000):
  function rope_apply (line 43) | def rope_apply(x, grid_sizes, freqs):
  class WanRMSNorm (line 73) | class WanRMSNorm(nn.Module):
    method __init__ (line 75) | def __init__(self, dim, eps=1e-5):
    method forward (line 81) | def forward(self, x):
    method _norm (line 88) | def _norm(self, x):
  class WanLayerNorm (line 92) | class WanLayerNorm(nn.LayerNorm):
    method __init__ (line 94) | def __init__(self, dim, eps=1e-6, elementwise_affine=False):
    method forward (line 97) | def forward(self, x):
  class WanSelfAttention (line 105) | class WanSelfAttention(nn.Module):
    method __init__ (line 107) | def __init__(self,
    method forward (line 130) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class WanT2VCrossAttention (line 162) | class WanT2VCrossAttention(WanSelfAttention):
    method forward (line 164) | def forward(self, x, context, context_lens):
  class WanI2VCrossAttention (line 187) | class WanI2VCrossAttention(WanSelfAttention):
    method __init__ (line 189) | def __init__(self,
    method forward (line 202) | def forward(self, x, context, context_lens):
  class WanAttentionBlock (line 238) | class WanAttentionBlock(nn.Module):
    method __init__ (line 240) | def __init__(self,
    method forward (line 278) | def forward(
  class Head (line 320) | class Head(nn.Module):
    method __init__ (line 322) | def __init__(self, dim, out_dim, patch_size, eps=1e-6):
    method forward (line 337) | def forward(self, x, e):
  class MLPProj (line 350) | class MLPProj(torch.nn.Module):
    method __init__ (line 352) | def __init__(self, in_dim, out_dim, flf_pos_emb=False):
    method forward (line 363) | def forward(self, image_embeds):
  class WanModel (line 372) | class WanModel(ModelMixin, ConfigMixin):
    method __init__ (line 383) | def __init__(self,
    method forward (line 493) | def forward(
    method unpatchify (line 584) | def unpatchify(self, x, grid_sizes):
    method init_weights (line 609) | def init_weights(self):

FILE: wan/modules/motion_patch.py
  function ind_sel (line 20) | def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1):
  function merge_final (line 51) | def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assi...
  function patch_motion (line 77) | def patch_motion(

FILE: wan/modules/t5.py
  function fp16_clamp (line 20) | def fp16_clamp(x):
  function init_weights (line 27) | def init_weights(m):
  class GELU (line 46) | class GELU(nn.Module):
    method forward (line 48) | def forward(self, x):
  class T5LayerNorm (line 53) | class T5LayerNorm(nn.Module):
    method __init__ (line 55) | def __init__(self, dim, eps=1e-6):
    method forward (line 61) | def forward(self, x):
  class T5Attention (line 69) | class T5Attention(nn.Module):
    method __init__ (line 71) | def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
    method forward (line 86) | def forward(self, x, context=None, mask=None, pos_bias=None):
  class T5FeedForward (line 123) | class T5FeedForward(nn.Module):
    method __init__ (line 125) | def __init__(self, dim, dim_ffn, dropout=0.1):
    method forward (line 136) | def forward(self, x):
  class T5SelfAttention (line 144) | class T5SelfAttention(nn.Module):
    method __init__ (line 146) | def __init__(self,
    method forward (line 170) | def forward(self, x, mask=None, pos_bias=None):
  class T5CrossAttention (line 178) | class T5CrossAttention(nn.Module):
    method __init__ (line 180) | def __init__(self,
    method forward (line 206) | def forward(self,
  class T5RelativeEmbedding (line 221) | class T5RelativeEmbedding(nn.Module):
    method __init__ (line 223) | def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
    method forward (line 233) | def forward(self, lq, lk):
    method _relative_position_bucket (line 245) | def _relative_position_bucket(self, rel_pos):
  class T5Encoder (line 267) | class T5Encoder(nn.Module):
    method __init__ (line 269) | def __init__(self,
    method forward (line 303) | def forward(self, ids, mask=None):
  class T5Decoder (line 315) | class T5Decoder(nn.Module):
    method __init__ (line 317) | def __init__(self,
    method forward (line 351) | def forward(self, ids, mask=None, encoder_states=None, encoder_mask=No...
  class T5Model (line 372) | class T5Model(nn.Module):
    method __init__ (line 374) | def __init__(self,
    method forward (line 408) | def forward(self, encoder_ids, encoder_mask, decoder_ids, decoder_mask):
  function _t5 (line 415) | def _t5(name,
  function umt5_xxl (line 456) | def umt5_xxl(**kwargs):
  class T5EncoderModel (line 472) | class T5EncoderModel:
    method __init__ (line 474) | def __init__(
    method __call__ (line 506) | def __call__(self, texts, device):

FILE: wan/modules/tokenizers.py
  function basic_clean (line 12) | def basic_clean(text):
  function whitespace_clean (line 18) | def whitespace_clean(text):
  function canonicalize (line 24) | def canonicalize(text, keep_punctuation_exact_string=None):
  class HuggingfaceTokenizer (line 37) | class HuggingfaceTokenizer:
    method __init__ (line 39) | def __init__(self, name, seq_len=None, clean=None, **kwargs):
    method __call__ (line 49) | def __call__(self, sequence, **kwargs):
    method _clean (line 75) | def _clean(self, text):

FILE: wan/modules/vace_model.py
  class VaceWanAttentionBlock (line 10) | class VaceWanAttentionBlock(WanAttentionBlock):
    method __init__ (line 12) | def __init__(self,
    method forward (line 33) | def forward(self, c, x, **kwargs):
  class BaseWanAttentionBlock (line 42) | class BaseWanAttentionBlock(WanAttentionBlock):
    method __init__ (line 44) | def __init__(self,
    method forward (line 58) | def forward(self, x, hints, context_scale=1.0, **kwargs):
  class VaceWanModel (line 65) | class VaceWanModel(WanModel):
    method __init__ (line 68) | def __init__(self,
    method forward_vace (line 136) | def forward_vace(self, x, vace_context, seq_len, kwargs):
    method forward (line 155) | def forward(

FILE: wan/modules/vae.py
  class CausalConv3d (line 17) | class CausalConv3d(nn.Conv3d):
    method __init__ (line 22) | def __init__(self, *args, **kwargs):
    method forward (line 28) | def forward(self, x, cache_x=None):
  class RMS_norm (line 39) | class RMS_norm(nn.Module):
    method __init__ (line 41) | def __init__(self, dim, channel_first=True, images=True, bias=False):
    method forward (line 51) | def forward(self, x):
  class Upsample (line 57) | class Upsample(nn.Upsample):
    method forward (line 59) | def forward(self, x):
  class Resample (line 66) | class Resample(nn.Module):
    method __init__ (line 68) | def __init__(self, dim, mode):
    method forward (line 101) | def forward(self, x, feat_cache=None, feat_idx=[0]):
    method init_weight (line 162) | def init_weight(self, conv):
    method init_weight2 (line 174) | def init_weight2(self, conv):
  class ResidualBlock (line 186) | class ResidualBlock(nn.Module):
    method __init__ (line 188) | def __init__(self, in_dim, out_dim, dropout=0.0):
    method forward (line 202) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class AttentionBlock (line 223) | class AttentionBlock(nn.Module):
    method __init__ (line 228) | def __init__(self, dim):
    method forward (line 240) | def forward(self, x):
  class Encoder3d (line 265) | class Encoder3d(nn.Module):
    method __init__ (line 267) | def __init__(self,
    method forward (line 318) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Decoder3d (line 369) | class Decoder3d(nn.Module):
    method __init__ (line 371) | def __init__(self,
    method forward (line 423) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  function count_conv3d (line 475) | def count_conv3d(model):
  class WanVAE_ (line 483) | class WanVAE_(nn.Module):
    method __init__ (line 485) | def __init__(self,
    method forward (line 510) | def forward(self, x):
    method encode (line 516) | def encode(self, x, scale):
    method decode (line 544) | def decode(self, z, scale):
    method reparameterize (line 570) | def reparameterize(self, mu, log_var):
    method sample (line 575) | def sample(self, imgs, deterministic=False):
    method clear_cache (line 582) | def clear_cache(self):
  function _video_vae (line 592) | def _video_vae(pretrained_path=None, z_dim=None, device='cpu', **kwargs):
  class WanVAE (line 619) | class WanVAE:
    method __init__ (line 621) | def __init__(self,
    method encode (line 647) | def encode(self, videos):
    method decode (line 657) | def decode(self, zs):

FILE: wan/modules/xlm_roberta.py
  class SelfAttention (line 10) | class SelfAttention(nn.Module):
    method __init__ (line 12) | def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5):
    method forward (line 27) | def forward(self, x, mask):
  class AttentionBlock (line 49) | class AttentionBlock(nn.Module):
    method __init__ (line 51) | def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5):
    method forward (line 66) | def forward(self, x, mask):
  class XLMRoberta (line 76) | class XLMRoberta(nn.Module):
    method __init__ (line 81) | def __init__(self,
    method forward (line 118) | def forward(self, ids):
  function xlm_roberta_large (line 146) | def xlm_roberta_large(pretrained=False,

FILE: wan/utils/fm_solvers.py
  function get_sampling_sigmas (line 24) | def get_sampling_sigmas(sampling_steps, shift):
  function retrieve_timesteps (line 31) | def retrieve_timesteps(
  class FlowDPMSolverMultistepScheduler (line 71) | class FlowDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
    method __init__ (line 131) | def __init__(
    method step_index (line 204) | def step_index(self):
    method begin_index (line 211) | def begin_index(self):
    method set_begin_index (line 218) | def set_begin_index(self, begin_index: int = 0):
    method set_timesteps (line 228) | def set_timesteps(
    method _threshold_sample (line 294) | def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
    method _sigma_to_t (line 332) | def _sigma_to_t(self, sigma):
    method _sigma_to_alpha_sigma_t (line 335) | def _sigma_to_alpha_sigma_t(self, sigma):
    method time_shift (line 339) | def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
    method convert_model_output (line 343) | def convert_model_output(
    method dpm_solver_first_order_update (line 417) | def dpm_solver_first_order_update(
    method multistep_dpm_solver_second_order_update (line 488) | def multistep_dpm_solver_second_order_update(
    method multistep_dpm_solver_third_order_update (line 598) | def multistep_dpm_solver_third_order_update(
    method index_for_timestep (line 681) | def index_for_timestep(self, timestep, schedule_timesteps=None):
    method _init_step_index (line 695) | def _init_step_index(self, timestep):
    method step (line 708) | def step(
    method scale_model_input (line 802) | def scale_model_input(self, sample: torch.Tensor, *args,
    method add_noise (line 817) | def add_noise(
    method __len__ (line 858) | def __len__(self):

FILE: wan/utils/fm_solvers_unipc.py
  class FlowUniPCMultistepScheduler (line 22) | class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
    method __init__ (line 79) | def __init__(
    method step_index (line 137) | def step_index(self):
    method begin_index (line 144) | def begin_index(self):
    method set_begin_index (line 151) | def set_begin_index(self, begin_index: int = 0):
    method set_timesteps (line 162) | def set_timesteps(
    method _threshold_sample (line 232) | def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
    method _sigma_to_t (line 271) | def _sigma_to_t(self, sigma):
    method _sigma_to_alpha_sigma_t (line 274) | def _sigma_to_alpha_sigma_t(self, sigma):
    method time_shift (line 278) | def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
    method convert_model_output (line 281) | def convert_model_output(
    method multistep_uni_p_bh_update (line 352) | def multistep_uni_p_bh_update(
    method multistep_uni_c_bh_update (line 488) | def multistep_uni_c_bh_update(
    method index_for_timestep (line 630) | def index_for_timestep(self, timestep, schedule_timesteps=None):
    method _init_step_index (line 645) | def _init_step_index(self, timestep):
    method step (line 657) | def step(self,
    method scale_model_input (line 743) | def scale_model_input(self, sample: torch.Tensor, *args,
    method add_noise (line 760) | def add_noise(
    method __len__ (line 801) | def __len__(self):

FILE: wan/utils/motion.py
  function get_tracks_inference (line 23) | def get_tracks_inference(tracks, height, width, quant_multi: Optional[in...
  function unzip_to_array (line 36) | def unzip_to_array(
  function process_tracks (line 53) | def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], q...

FILE: wan/utils/prompt_extend.py
  class PromptOutput (line 153) | class PromptOutput(object):
    method add_custom_field (line 160) | def add_custom_field(self, key: str, value) -> None:
  class PromptExpander (line 164) | class PromptExpander:
    method __init__ (line 166) | def __init__(self, model_name, is_vl=False, device=0, **kwargs):
    method extend_with_img (line 171) | def extend_with_img(self,
    method extend (line 180) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method decide_system_prompt (line 183) | def decide_system_prompt(self, tar_lang="zh", multi_images_input=False):
    method __call__ (line 189) | def __call__(self,
  class DashScopePromptExpander (line 213) | class DashScopePromptExpander(PromptExpander):
    method __init__ (line 215) | def __init__(self,
    method extend (line 252) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method extend_with_img (line 288) | def extend_with_img(self,
  class QwenPromptExpander (line 364) | class QwenPromptExpander(PromptExpander):
    method __init__ (line 373) | def __init__(self, model_name=None, device=0, is_vl=False, **kwargs):
    method extend (line 433) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method extend_with_img (line 464) | def extend_with_img(self,

FILE: wan/utils/qwen_vl_utils.py
  function round_by_factor (line 39) | def round_by_factor(number: int, factor: int) -> int:
  function ceil_by_factor (line 44) | def ceil_by_factor(number: int, factor: int) -> int:
  function floor_by_factor (line 49) | def floor_by_factor(number: int, factor: int) -> int:
  function smart_resize (line 54) | def smart_resize(height: int,
  function fetch_image (line 85) | def fetch_image(ele: dict[str, str | Image.Image],
  function smart_nframes (line 133) | def smart_nframes(
  function _read_video_torchvision (line 177) | def _read_video_torchvision(ele: dict,) -> torch.Tensor:
  function is_decord_available (line 215) | def is_decord_available() -> bool:
  function _read_video_decord (line 221) | def _read_video_decord(ele: dict,) -> torch.Tensor:
  function get_video_reader_backend (line 261) | def get_video_reader_backend() -> str:
  function fetch_video (line 274) | def fetch_video(
  function extract_vision_info (line 328) | def extract_vision_info(
  function process_vision_info (line 344) | def process_vision_info(

FILE: wan/utils/utils.py
  function rand_name (line 14) | def rand_name(length=8, suffix=''):
  function cache_video (line 23) | def cache_video(tensor,
  function cache_image (line 64) | def cache_image(tensor,
  function str2bool (line 94) | def str2bool(v):

FILE: wan/utils/vace_processor.py
  class VaceImageProcessor (line 9) | class VaceImageProcessor(object):
    method __init__ (line 11) | def __init__(self, downsample=None, seq_len=None):
    method _pillow_convert (line 15) | def _pillow_convert(self, image, cvt_type='RGB'):
    method _load_image (line 30) | def _load_image(self, img_path):
    method _resize_crop (line 37) | def _resize_crop(self, img, oh, ow, normalize=True):
    method _image_preprocess (line 60) | def _image_preprocess(self, img, oh, ow, normalize=True, **kwargs):
    method load_image (line 63) | def load_image(self, data_key, **kwargs):
    method load_image_pair (line 66) | def load_image_pair(self, data_key, data_key2, **kwargs):
    method load_image_batch (line 69) | def load_image_batch(self,
  class VaceVideoProcessor (line 91) | class VaceVideoProcessor(object):
    method __init__ (line 93) | def __init__(self, downsample, min_area, max_area, min_fps, max_fps,
    method set_area (line 105) | def set_area(self, area):
    method set_seq_len (line 109) | def set_seq_len(self, seq_len):
    method resize_crop (line 113) | def resize_crop(video: torch.Tensor, oh: int, ow: int):
    method _video_preprocess (line 151) | def _video_preprocess(self, video, oh, ow):
    method _get_frameid_bbox_default (line 154) | def _get_frameid_bbox_default(self, fps, frame_timestamps, h, w, crop_...
    method _get_frameid_bbox_adjust_last (line 187) | def _get_frameid_bbox_adjust_last(self, fps, frame_timestamps, h, w,
    method _get_frameid_bbox (line 219) | def _get_frameid_bbox(self, fps, frame_timestamps, h, w, crop_box, rng):
    method load_video (line 227) | def load_video(self, data_key, crop_box=None, seed=2024, **kwargs):
    method load_video_pair (line 231) | def load_video_pair(self,
    method load_video_batch (line 240) | def load_video_batch(self,
  function prepare_source (line 274) | def prepare_source(src_video, src_mask, src_ref_images, num_frames, imag...

FILE: wan/vace.py
  class WanVace (line 37) | class WanVace(WanT2V):
    method __init__ (line 39) | def __init__(
    method vace_encode_frames (line 139) | def vace_encode_frames(self, frames, ref_images, masks=None, vae=None):
    method vace_encode_masks (line 174) | def vace_encode_masks(self, masks, ref_images=None, vae_stride=None):
    method vace_latent (line 209) | def vace_latent(self, z, m):
    method prepare_source (line 212) | def prepare_source(self, src_video, src_mask, src_ref_images, num_frames,
    method decode_latent (line 280) | def decode_latent(self, zs, ref_images=None, vae=None):
    method generate (line 295) | def generate(self,
  class WanVaceMP (line 478) | class WanVaceMP(WanVace):
    method __init__ (line 480) | def __init__(self,
    method dynamic_load (line 512) | def dynamic_load(self):
    method transfer_data_to_cuda (line 544) | def transfer_data_to_cuda(self, data, device):
    method mp_worker (line 562) | def mp_worker(self, gpu, gpu_infer, pmi_rank, pmi_world_size, in_q_list,
    method generate (line 773) | def generate(self,