SYMBOL INDEX (655 symbols across 42 files)

FILE: generate.py
  function _validate_args (line 62) | def _validate_args(args):
  function _parse_args (line 105) | def _parse_args():
  function _init_logging (line 303) | def _init_logging(rank):
  function generate (line 315) | def generate(args):

FILE: wan/animate.py
  class WanAnimate (line 36) | class WanAnimate:
    method __init__ (line 38) | def __init__(
    method _configure_model (line 144) | def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
    method inputs_padding (line 201) | def inputs_padding(self, array, target_len):
    method get_valid_len (line 215) | def get_valid_len(self, real_len, clip_len=81, overlap=1):
    method get_i2v_mask (line 226) | def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_val...
    method padding_resize (line 237) | def padding_resize(self, img_ori, height=512, width=512, padding_color...
    method prepare_source (line 269) | def prepare_source(self, src_pose_path, src_face_path, src_ref_path):
    method prepare_source_for_replace (line 284) | def prepare_source_for_replace(self, src_bg_path, src_mask_path):
    method generate (line 297) | def generate(

FILE: wan/distributed/fsdp.py
  function shard_model (line 12) | def shard_model(
  function free_model (line 39) | def free_model(model):

FILE: wan/distributed/sequence_parallel.py
  function pad_freqs (line 10) | def pad_freqs(original_tensor, target_len):
  function rope_apply (line 24) | def rope_apply(x, grid_sizes, freqs):
  function sp_dit_forward (line 64) | def sp_dit_forward(
  function sp_attn_forward (line 147) | def sp_attn_forward(self, x, seq_lens, grid_sizes, freqs, dtype=torch.bf...

FILE: wan/distributed/ulysses.py
  function distributed_attention (line 9) | def distributed_attention(

FILE: wan/distributed/util.py
  function init_distributed_group (line 6) | def init_distributed_group():
  function get_rank (line 13) | def get_rank():
  function get_world_size (line 17) | def get_world_size():
  function all_to_all (line 21) | def all_to_all(x, scatter_dim, gather_dim, group=None, **kwargs):
  function all_gather (line 34) | def all_gather(tensor):
  function gather_forward (line 43) | def gather_forward(input, dim):

FILE: wan/image2video.py
  class WanI2V (line 33) | class WanI2V:
    method __init__ (line 35) | def __init__(
    method _configure_model (line 128) | def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
    method _prepare_model_for_timestep (line 172) | def _prepare_model_for_timestep(self, t, boundary, offload_model):
    method generate (line 206) | def generate(self,

FILE: wan/modules/animate/animate_utils.py
  function get_loraconfig (line 7) | def get_loraconfig(transformer, rank=128, alpha=128, init_lora_weights="...
  class TensorList (line 23) | class TensorList(object):
    method __init__ (line 25) | def __init__(self, tensors):
    method to (line 36) | def to(self, *args, **kwargs):
    method size (line 39) | def size(self, dim):
    method pow (line 43) | def pow(self, *args, **kwargs):
    method squeeze (line 46) | def squeeze(self, dim):
    method type (line 52) | def type(self, *args, **kwargs):
    method type_as (line 55) | def type_as(self, other):
    method dtype (line 63) | def dtype(self):
    method device (line 67) | def device(self):
    method ndim (line 71) | def ndim(self):
    method __getitem__ (line 74) | def __getitem__(self, index):
    method __len__ (line 77) | def __len__(self):
    method __add__ (line 80) | def __add__(self, other):
    method __radd__ (line 83) | def __radd__(self, other):
    method __sub__ (line 86) | def __sub__(self, other):
    method __rsub__ (line 89) | def __rsub__(self, other):
    method __mul__ (line 92) | def __mul__(self, other):
    method __rmul__ (line 95) | def __rmul__(self, other):
    method __floordiv__ (line 98) | def __floordiv__(self, other):
    method __truediv__ (line 101) | def __truediv__(self, other):
    method __rfloordiv__ (line 104) | def __rfloordiv__(self, other):
    method __rtruediv__ (line 107) | def __rtruediv__(self, other):
    method __pow__ (line 110) | def __pow__(self, other):
    method __rpow__ (line 113) | def __rpow__(self, other):
    method __neg__ (line 116) | def __neg__(self):
    method __iter__ (line 119) | def __iter__(self):
    method __repr__ (line 123) | def __repr__(self):
    method _apply (line 126) | def _apply(self, other, op):

FILE: wan/modules/animate/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/animate/face_blocks.py
  function attention (line 32) | def attention(
  class CausalConv1d (line 112) | class CausalConv1d(nn.Module):
    method __init__ (line 114) | def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilatio...
    method forward (line 123) | def forward(self, x):
  class FaceEncoder (line 129) | class FaceEncoder(nn.Module):
    method __init__ (line 130) | def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=...
    method forward (line 150) | def forward(self, x):
  class RMSNorm (line 180) | class RMSNorm(nn.Module):
    method __init__ (line 181) | def __init__(
    method _norm (line 207) | def _norm(self, x):
    method forward (line 220) | def forward(self, x):
  function get_norm_layer (line 237) | def get_norm_layer(norm_layer):
  class FaceAdapter (line 255) | class FaceAdapter(nn.Module):
    method __init__ (line 256) | def __init__(
    method forward (line 284) | def forward(
  class FaceBlock (line 297) | class FaceBlock(nn.Module):
    method __init__ (line 298) | def __init__(
    method forward (line 334) | def forward(

FILE: wan/modules/animate/model_animate.py
  class HeadAnimate (line 39) | class HeadAnimate(Head):
    method forward (line 41) | def forward(self, x, e):
  class WanAnimateSelfAttention (line 54) | class WanAnimateSelfAttention(WanSelfAttention):
    method forward (line 56) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class WanAnimateCrossAttention (line 88) | class WanAnimateCrossAttention(WanSelfAttention):
    method __init__ (line 89) | def __init__(
    method forward (line 112) | def forward(self, x, context, context_lens):
  class WanAnimateAttentionBlock (line 149) | class WanAnimateAttentionBlock(nn.Module):
    method __init__ (line 150) | def __init__(self,
    method forward (line 188) | def forward(
  class MLPProj (line 230) | class MLPProj(torch.nn.Module):
    method __init__ (line 231) | def __init__(self, in_dim, out_dim):
    method forward (line 242) | def forward(self, image_embeds):
  class WanAnimateModel (line 246) | class WanAnimateModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    method __init__ (line 250) | def __init__(self,
    method after_patch_embedding (line 340) | def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, f...
    method after_transformer_block (line 364) | def after_transformer_block(self, block_idx, x, motion_vec, motion_mas...
    method forward (line 372) | def forward(
    method unpatchify (line 453) | def unpatchify(self, x, grid_sizes):
    method init_weights (line 478) | def init_weights(self):

FILE: wan/modules/animate/motion_encoder.py
  function custom_qr (line 8) | def custom_qr(input_tensor):
  function fused_leaky_relu (line 15) | def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
  function upfirdn2d_native (line 19) | def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, ...
  function upfirdn2d (line 39) | def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
  function make_kernel (line 43) | def make_kernel(k):
  class FusedLeakyReLU (line 51) | class FusedLeakyReLU(nn.Module):
    method __init__ (line 52) | def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
    method forward (line 58) | def forward(self, input):
  class Blur (line 63) | class Blur(nn.Module):
    method __init__ (line 64) | def __init__(self, kernel, pad, upsample_factor=1):
    method forward (line 76) | def forward(self, input):
  class ScaledLeakyReLU (line 80) | class ScaledLeakyReLU(nn.Module):
    method __init__ (line 81) | def __init__(self, negative_slope=0.2):
    method forward (line 86) | def forward(self, input):
  class EqualConv2d (line 90) | class EqualConv2d(nn.Module):
    method __init__ (line 91) | def __init__(self, in_channel, out_channel, kernel_size, stride=1, pad...
    method forward (line 105) | def forward(self, input):
    method __repr__ (line 109) | def __repr__(self):
  class EqualLinear (line 116) | class EqualLinear(nn.Module):
    method __init__ (line 117) | def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, ...
    method forward (line 132) | def forward(self, input):
    method __repr__ (line 142) | def __repr__(self):
  class ConvLayer (line 146) | class ConvLayer(nn.Sequential):
    method __init__ (line 147) | def __init__(
  class ResBlock (line 186) | class ResBlock(nn.Module):
    method __init__ (line 187) | def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
    method forward (line 195) | def forward(self, input):
  class EncoderApp (line 205) | class EncoderApp(nn.Module):
    method __init__ (line 206) | def __init__(self, size, w_dim=512):
    method forward (line 235) | def forward(self, x):
  class Encoder (line 246) | class Encoder(nn.Module):
    method __init__ (line 247) | def __init__(self, size, dim=512, dim_motion=20):
    method enc_app (line 261) | def enc_app(self, x):
    method enc_motion (line 265) | def enc_motion(self, x):
  class Direction (line 271) | class Direction(nn.Module):
    method __init__ (line 272) | def __init__(self, motion_dim):
    method forward (line 276) | def forward(self, input):
  class Synthesis (line 289) | class Synthesis(nn.Module):
    method __init__ (line 290) | def __init__(self, motion_dim):
  class Generator (line 295) | class Generator(nn.Module):
    method __init__ (line 296) | def __init__(self, size, style_dim=512, motion_dim=20):
    method get_motion (line 302) | def get_motion(self, img):

FILE: wan/modules/animate/preprocess/human_visualization.py
  function draw_handpose (line 14) | def draw_handpose(canvas, keypoints, hand_score_th=0.6):
  function draw_handpose_new (line 93) | def draw_handpose_new(canvas, keypoints, stickwidth_type='v2', hand_scor...
  function draw_ellipse_by_2kp (line 175) | def draw_ellipse_by_2kp(img, keypoint1, keypoint2, color, threshold=0.6):
  function split_pose2d_kps_to_aa (line 193) | def split_pose2d_kps_to_aa(kp2ds: np.ndarray) -> List[np.ndarray]:
  function draw_aapose_by_meta (line 211) | def draw_aapose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_widt...
  function draw_aapose_by_meta_new (line 218) | def draw_aapose_by_meta_new(img, meta: AAPoseMeta, threshold=0.5, stickw...
  function draw_hand_by_meta (line 226) | def draw_hand_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_width_...
  function draw_aaface_by_meta (line 234) | def draw_aaface_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_widt...
  function draw_aanose_by_meta (line 242) | def draw_aanose_by_meta(img, meta: AAPoseMeta, threshold=0.5, stick_widt...
  function gen_face_motion_seq (line 250) | def gen_face_motion_seq(img, metas: List[AAPoseMeta], threshold=0.5, sti...
  function draw_M (line 255) | def draw_M(
  function draw_nose (line 422) | def draw_nose(
  function draw_aapose (line 586) | def draw_aapose(
  function draw_aapose_new (line 750) | def draw_aapose_new(
  function draw_bbox (line 921) | def draw_bbox(img, bbox, color=(255, 0, 0)):
  function draw_kp2ds (line 928) | def draw_kp2ds(img, kp2ds, threshold=0, color=(255, 0, 0), skeleton=None...
  function draw_mask (line 1022) | def draw_mask(img, mask, background=0, return_rgba=False):
  function draw_pcd (line 1032) | def draw_pcd(pcd_list, save_path=None):
  function load_image (line 1051) | def load_image(img, reverse=False):
  function draw_skeleten (line 1061) | def draw_skeleten(meta):
  function draw_skeleten_with_pncc (line 1085) | def draw_skeleten_with_pncc(pncc: np.ndarray, meta: Dict) -> np.ndarray:
  function draw_face_kp (line 1147) | def draw_face_kp(img, kps, thickness=2, style=FACE_CUSTOM_STYLE):
  function draw_traj (line 1168) | def draw_traj(metas: List[AAPoseMeta], threshold=0.6):

FILE: wan/modules/animate/preprocess/pose2d.py
  class SimpleOnnxInference (line 20) | class SimpleOnnxInference(object):
    method __init__ (line 21) | def __init__(self, checkpoint, device='cuda', reverse_input=False, **k...
    method __call__ (line 45) | def __call__(self, *args, **kwargs):
    method get_output_names (line 49) | def get_output_names(self):
    method set_device (line 56) | def set_device(self, device):
  class Yolo (line 68) | class Yolo(SimpleOnnxInference):
    method __init__ (line 69) | def __init__(self, checkpoint, device='cuda', threshold_conf=0.05, thr...
    method preprocess (line 89) | def preprocess(self, input_image):
    method postprocess (line 112) | def postprocess(self, output, shape_raw, cat_id=[1]):
    method process_results (line 207) | def process_results(self, results, shape_raw, cat_id=[1], single_perso...
    method postprocess_threading (line 283) | def postprocess_threading(self, outputs, shape_raw, person_results, i,...
    method forward (line 290) | def forward(self, img, shape_raw, **kwargs):
  class ViTPose (line 309) | class ViTPose(SimpleOnnxInference):
    method __init__ (line 310) | def __init__(self, checkpoint, device='cuda', **kwargs):
    method forward (line 313) | def forward(self, img, center, scale, **kwargs):
    method preprocess (line 324) | def preprocess(img, bbox=None, input_resolution=(256, 192), rescale=1....
  class Pose2d (line 346) | class Pose2d:
    method __init__ (line 347) | def __init__(self, checkpoint, detector_checkpoint=None, device='cuda'...
    method load_images (line 357) | def load_images(self, inputs):
    method __call__ (line 396) | def __call__(

FILE: wan/modules/animate/preprocess/pose2d_utils.py
  function box_convert_simple (line 9) | def box_convert_simple(box, convert_type='xyxy2xywh'):
  function read_img (line 19) | def read_img(image, convert='RGB', check_exist=False):
  class AAPoseMeta (line 39) | class AAPoseMeta:
    method __init__ (line 40) | def __init__(self, meta=None, kp2ds=None):
    method is_valid (line 60) | def is_valid(self, kp, p, threshold):
    method get_bbox (line 67) | def get_bbox(self, kp, kp_p, threshold=0.5):
    method crop (line 75) | def crop(self, x0, y0, x1, y1):
    method resize (line 85) | def resize(self, width, height):
    method get_kps_body_with_p (line 98) | def get_kps_body_with_p(self, normalize=False):
    method from_kps_face (line 106) | def from_kps_face(kps_face: np.ndarray, height: int, width: int):
    method from_kps_body (line 119) | def from_kps_body(kps_body: np.ndarray, height: int, width: int):
    method from_humanapi_meta (line 128) | def from_humanapi_meta(meta):
    method load_from_meta (line 144) | def load_from_meta(self, meta, norm_body=True, norm_hand=False):
    method load_from_kp2ds (line 170) | def load_from_kp2ds(kp2ds: List[np.ndarray], width: int, height: int):
    method from_dwpose (line 199) | def from_dwpose(dwpose_det_res, height, width):
    method save_json (line 219) | def save_json(self):
    method draw_aapose (line 222) | def draw_aapose(self, img, threshold=0.5, stick_width_norm=200, draw_h...
    method translate (line 227) | def translate(self, x0, y0):
    method scale (line 234) | def scale(self, sx, sy):
    method padding_resize2 (line 241) | def padding_resize2(self, height=512, width=512):
  function transform_preds (line 279) | def transform_preds(coords, center, scale, output_size, use_udp=False):
  function _calc_distances (line 326) | def _calc_distances(preds, targets, mask, normalize):
  function _distance_acc (line 358) | def _distance_acc(distances, thr=0.5):
  function _get_max_preds (line 379) | def _get_max_preds(heatmaps):
  function _get_max_preds_3d (line 414) | def _get_max_preds_3d(heatmaps):
  function pose_pck_accuracy (line 452) | def pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None):
  function keypoint_pck_accuracy (line 495) | def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):
  function keypoint_auc (line 534) | def keypoint_auc(pred, gt, mask, normalize, num_step=20):
  function keypoint_nme (line 566) | def keypoint_nme(pred, gt, mask, normalize_factor):
  function keypoint_epe (line 589) | def keypoint_epe(pred, gt, mask):
  function _taylor (line 614) | def _taylor(heatmap, coord):
  function post_dark_udp (line 651) | def post_dark_udp(coords, batch_heatmaps, kernel=3):
  function _gaussian_blur (line 715) | def _gaussian_blur(heatmaps, kernel=11):
  function keypoints_from_regression (line 757) | def keypoints_from_regression(regression_preds, center, scale, img_size):
  function keypoints_from_heatmaps (line 790) | def keypoints_from_heatmaps(heatmaps,
  function keypoints_from_heatmaps3d (line 941) | def keypoints_from_heatmaps3d(heatmaps, center, scale):
  function multilabel_classification_accuracy (line 974) | def multilabel_classification_accuracy(pred, gt, mask, thr=0.5):
  function get_transform (line 1004) | def get_transform(center, scale, res, rot=0):
  function transform (line 1034) | def transform(pt, center, scale, res, invert=0, rot=0):
  function bbox_from_detector (line 1044) | def bbox_from_detector(bbox, input_resolution=(224, 224), rescale=1.25):
  function crop (line 1069) | def crop(img, center, scale, res):
  function split_kp2ds_for_aa (line 1102) | def split_kp2ds_for_aa(kp2ds, ret_face=False):
  function load_pose_metas_from_kp2ds_seq_list (line 1111) | def load_pose_metas_from_kp2ds_seq_list(kp2ds_seq, width, height):
  function load_pose_metas_from_kp2ds_seq (line 1137) | def load_pose_metas_from_kp2ds_seq(kp2ds_seq, width, height):

FILE: wan/modules/animate/preprocess/preprocess_data.py
  function _parse_args (line 7) | def _parse_args():

FILE: wan/modules/animate/preprocess/process_pipepline.py
  class ProcessPipeline (line 28) | class ProcessPipeline():
    method __init__ (line 29) | def __init__(self, det_checkpoint_path, pose2d_checkpoint_path, sam_ch...
    method __call__ (line 38) | def __call__(self, video_path, refer_image_path, output_path, resoluti...
    method get_editing_prompts (line 237) | def get_editing_prompts(self, tpl_pose_metas, refer_pose_meta):
    method get_mask (line 280) | def get_mask(self, frames, th_step, kp2ds_all):
    method convert_list_to_array (line 345) | def convert_list_to_array(self, metas):

FILE: wan/modules/animate/preprocess/retarget_pose.py
  class Keypoint (line 51) | class Keypoint(NamedTuple):
  function get_length (line 60) | def get_length(skeleton, limb):
  function get_handpose_meta (line 80) | def get_handpose_meta(keypoints, delta, src_H, src_W):
  function deal_hand_keypoints (line 106) | def deal_hand_keypoints(hand_res, r_ratio, l_ratio, hand_score_th = 0.5):
  function get_scaled_pose (line 159) | def get_scaled_pose(canvas, src_canvas, keypoints, keypoints_hand, bone_...
  function rescale_skeleton (line 309) | def rescale_skeleton(H, W, keypoints, bone_ratio_list):
  function fix_lack_keypoints_use_sym (line 369) | def fix_lack_keypoints_use_sym(skeleton):
  function rescale_shorten_skeleton (line 450) | def rescale_shorten_skeleton(ratio_list, src_length_list, dst_length_list):
  function check_full_body (line 481) | def check_full_body(keypoints, threshold = 0.4):
  function check_full_body_both (line 501) | def check_full_body_both(flag1, flag2):
  function write_to_poses (line 520) | def write_to_poses(data_to_json, none_idx, dst_shape, bone_ratio_list, d...
  function calculate_scale_ratio (line 551) | def calculate_scale_ratio(skeleton, skeleton_edit, scale_ratio_flag):
  function retarget_pose (line 571) | def retarget_pose(src_skeleton, dst_skeleton, all_src_skeleton, src_skel...
  function get_retarget_pose (line 760) | def get_retarget_pose(tpl_pose_meta0, refer_pose_meta, tpl_pose_metas, t...

FILE: wan/modules/animate/preprocess/sam_utils.py
  function _load_img_v2_as_tensor (line 23) | def _load_img_v2_as_tensor(img, image_size):
  function load_video_frames (line 34) | def load_video_frames(
  function load_video_frames_v2 (line 89) | def load_video_frames_v2(
  function build_sam2_video_predictor (line 122) | def build_sam2_video_predictor(

FILE: wan/modules/animate/preprocess/utils.py
  function get_mask_boxes (line 8) | def get_mask_boxes(mask):
  function get_aug_mask (line 25) | def get_aug_mask(body_mask, w_len=10, h_len=20):
  function get_mask_body_img (line 44) | def get_mask_body_img(img_copy, hand_mask, k=7, iterations=1):
  function get_face_bboxes (line 52) | def get_face_bboxes(kp2ds, scale, image_shape, ratio_aug):
  function calculate_new_size (line 87) | def calculate_new_size(orig_w, orig_h, target_area, divisor=64):
  function resize_by_area (line 136) | def resize_by_area(image, target_area, keep_aspect_ratio=True, divisor=6...
  function padding_resize (line 158) | def padding_resize(img_ori, height=512, width=512, padding_color=(0, 0, ...
  function get_frame_indices (line 191) | def get_frame_indices(frame_num, video_fps, clip_length, train_fps):
  function get_face_bboxes (line 201) | def get_face_bboxes(kp2ds, scale, image_shape):

FILE: wan/modules/animate/preprocess/video_predictor.py
  class SAM2VideoPredictor (line 14) | class SAM2VideoPredictor(_SAM2VideoPredictor):
    method __init__ (line 15) | def __init__(self, *args, **kwargs):
    method init_state (line 20) | def init_state(
    method init_state_v2 (line 90) | def init_state_v2(

FILE: wan/modules/animate/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/modules/attention.py
  function flash_attention (line 24) | def flash_attention(
  function attention (line 133) | def attention(

FILE: wan/modules/model.py
  function sinusoidal_embedding_1d (line 14) | def sinusoidal_embedding_1d(dim, position):
  function rope_params (line 28) | def rope_params(max_seq_len, dim, theta=10000):
  function rope_apply (line 39) | def rope_apply(x, grid_sizes, freqs):
  class WanRMSNorm (line 69) | class WanRMSNorm(nn.Module):
    method __init__ (line 71) | def __init__(self, dim, eps=1e-5):
    method forward (line 77) | def forward(self, x):
    method _norm (line 84) | def _norm(self, x):
  class WanLayerNorm (line 88) | class WanLayerNorm(nn.LayerNorm):
    method __init__ (line 90) | def __init__(self, dim, eps=1e-6, elementwise_affine=False):
    method forward (line 93) | def forward(self, x):
  class WanSelfAttention (line 101) | class WanSelfAttention(nn.Module):
    method __init__ (line 103) | def __init__(self,
    method forward (line 126) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class WanCrossAttention (line 158) | class WanCrossAttention(WanSelfAttention):
    method forward (line 160) | def forward(self, x, context, context_lens):
  class WanAttentionBlock (line 183) | class WanAttentionBlock(nn.Module):
    method __init__ (line 185) | def __init__(self,
    method forward (line 219) | def forward(
  class Head (line 262) | class Head(nn.Module):
    method __init__ (line 264) | def __init__(self, dim, out_dim, patch_size, eps=1e-6):
    method forward (line 279) | def forward(self, x, e):
  class WanModel (line 294) | class WanModel(ModelMixin, ConfigMixin):
    method __init__ (line 305) | def __init__(self,
    method forward (line 410) | def forward(
    method unpatchify (line 499) | def unpatchify(self, x, grid_sizes):
    method init_weights (line 524) | def init_weights(self):

FILE: wan/modules/s2v/audio_encoder.py
  function get_sample_indices (line 11) | def get_sample_indices(original_fps,
  function linear_interpolation (line 38) | def linear_interpolation(features, input_fps, output_fps, output_len=None):
  class AudioEncoder (line 55) | class AudioEncoder():
    method __init__ (line 57) | def __init__(self, device='cpu', model_id="facebook/wav2vec2-base-960h"):
    method extract_audio_feat (line 66) | def extract_audio_feat(self,
    method get_audio_embed_bucket (line 91) | def get_audio_embed_bucket(self,
    method get_audio_embed_bucket_fps (line 136) | def get_audio_embed_bucket_fps(self,

FILE: wan/modules/s2v/audio_utils.py
  class CausalAudioEncoder (line 14) | class CausalAudioEncoder(nn.Module):
    method __init__ (line 16) | def __init__(self,
    method forward (line 34) | def forward(self, features):
  class AudioCrossAttention (line 47) | class AudioCrossAttention(WanCrossAttention):
    method __init__ (line 49) | def __init__(self, *args, **kwargs):
  class AudioInjector_WAN (line 53) | class AudioInjector_WAN(nn.Module):
    method __init__ (line 55) | def __init__(self,

FILE: wan/modules/s2v/auxi_blocks.py
  function attention (line 35) | def attention(
  class CausalConv1d (line 121) | class CausalConv1d(nn.Module):
    method __init__ (line 123) | def __init__(self,
    method forward (line 145) | def forward(self, x):
  class MotionEncoder_tc (line 150) | class MotionEncoder_tc(nn.Module):
    method __init__ (line 152) | def __init__(self,
    method forward (line 199) | def forward(self, x):

FILE: wan/modules/s2v/model_s2v.py
  function zero_module (line 35) | def zero_module(module):
  function torch_dfs (line 44) | def torch_dfs(model: nn.Module, parent_name='root'):
  function rope_apply (line 62) | def rope_apply(x, grid_sizes, freqs, start=None):
  function rope_apply_usp (line 80) | def rope_apply_usp(x, grid_sizes, freqs):
  function sp_attn_forward_s2v (line 98) | def sp_attn_forward_s2v(self,
  class Head_S2V (line 135) | class Head_S2V(Head):
    method forward (line 137) | def forward(self, x, e):
  class WanS2VSelfAttention (line 150) | class WanS2VSelfAttention(WanSelfAttention):
    method forward (line 152) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class WanS2VAttentionBlock (line 184) | class WanS2VAttentionBlock(WanAttentionBlock):
    method __init__ (line 186) | def __init__(self,
    method forward (line 199) | def forward(self, x, e, seq_lens, grid_sizes, freqs, context, context_...
  class WanModel_S2V (line 247) | class WanModel_S2V(ModelMixin, ConfigMixin):
    method __init__ (line 255) | def __init__(
    method zero_init_weights (line 441) | def zero_init_weights(self):
    method process_motion (line 455) | def process_motion(self, motion_latents, drop_motion_frames=False):
    method process_motion_frame_pack (line 484) | def process_motion_frame_pack(self,
    method process_motion_transformer_motioner (line 496) | def process_motion_transformer_motioner(self,
    method inject_motion (line 561) | def inject_motion(self,
    method after_transformer_block (line 601) | def after_transformer_block(self, block_idx, hidden_states):
    method forward (line 650) | def forward(
    method unpatchify (line 859) | def unpatchify(self, x, grid_sizes):
    method init_weights (line 884) | def init_weights(self):

FILE: wan/modules/s2v/motioner.py
  function sinusoidal_embedding_1d (line 17) | def sinusoidal_embedding_1d(dim, position):
  function rope_params (line 31) | def rope_params(max_seq_len, dim, theta=10000):
  function rope_apply (line 42) | def rope_apply(x, grid_sizes, freqs, start=None):
  class RMSNorm (line 114) | class RMSNorm(nn.Module):
    method __init__ (line 116) | def __init__(self, dim, eps=1e-5):
    method forward (line 122) | def forward(self, x):
    method _norm (line 125) | def _norm(self, x):
  class LayerNorm (line 129) | class LayerNorm(nn.LayerNorm):
    method __init__ (line 131) | def __init__(self, dim, eps=1e-6, elementwise_affine=False):
    method forward (line 134) | def forward(self, x):
  class SelfAttention (line 138) | class SelfAttention(nn.Module):
    method __init__ (line 140) | def __init__(self,
    method forward (line 163) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class SwinSelfAttention (line 188) | class SwinSelfAttention(SelfAttention):
    method forward (line 190) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class CasualSelfAttention (line 246) | class CasualSelfAttention(SelfAttention):
    method forward (line 248) | def forward(self, x, seq_lens, grid_sizes, freqs):
  class MotionerAttentionBlock (line 328) | class MotionerAttentionBlock(nn.Module):
    method __init__ (line 330) | def __init__(self,
    method forward (line 365) | def forward(
  class Head (line 380) | class Head(nn.Module):
    method __init__ (line 382) | def __init__(self, dim, out_dim, patch_size, eps=1e-6):
    method forward (line 394) | def forward(self, x):
  class MotionerTransformers (line 399) | class MotionerTransformers(nn.Module, PeftAdapterMixin):
    method __init__ (line 401) | def __init__(
    method after_patch_embedding (line 492) | def after_patch_embedding(self, x):
    method forward (line 495) | def forward(
    method unpatchify (line 621) | def unpatchify(self, x, grid_sizes):
    method init_weights (line 631) | def init_weights(self):
  class FramePackMotioner (line 643) | class FramePackMotioner(nn.Module):
    method __init__ (line 645) | def __init__(
    method forward (line 679) | def forward(self, motion_latents, add_last_motion=2):
  function sample_indices (line 765) | def sample_indices(N, stride, expand_ratio, c):

FILE: wan/modules/s2v/s2v_utils.py
  function rope_precompute (line 6) | def rope_precompute(x, grid_sizes, freqs, start=None):

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/vae2_1.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 Wan2_1_VAE (line 619) | class Wan2_1_VAE:
    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/vae2_2.py
  class CausalConv3d (line 17) | class CausalConv3d(nn.Conv3d):
    method __init__ (line 22) | def __init__(self, *args, **kwargs):
    method forward (line 34) | def forward(self, x, cache_x=None):
  class RMS_norm (line 45) | class RMS_norm(nn.Module):
    method __init__ (line 47) | def __init__(self, dim, channel_first=True, images=True, bias=False):
    method forward (line 57) | def forward(self, x):
  class Upsample (line 62) | class Upsample(nn.Upsample):
    method forward (line 64) | def forward(self, x):
  class Resample (line 71) | class Resample(nn.Module):
    method __init__ (line 73) | def __init__(self, dim, mode):
    method forward (line 112) | def forward(self, x, feat_cache=None, feat_idx=[0]):
    method init_weight (line 171) | def init_weight(self, conv):
    method init_weight2 (line 182) | def init_weight2(self, conv):
  class ResidualBlock (line 193) | class ResidualBlock(nn.Module):
    method __init__ (line 195) | def __init__(self, in_dim, out_dim, dropout=0.0):
    method forward (line 214) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class AttentionBlock (line 238) | class AttentionBlock(nn.Module):
    method __init__ (line 243) | def __init__(self, dim):
    method forward (line 255) | def forward(self, x):
  function patchify (line 280) | def patchify(x, patch_size):
  function unpatchify (line 299) | def unpatchify(x, patch_size):
  class AvgDown3D (line 316) | class AvgDown3D(nn.Module):
    method __init__ (line 318) | def __init__(
    method forward (line 335) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DupUp3D (line 370) | class DupUp3D(nn.Module):
    method __init__ (line 372) | def __init__(
    method forward (line 390) | def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
  class Down_ResidualBlock (line 415) | class Down_ResidualBlock(nn.Module):
    method __init__ (line 417) | def __init__(self,
    method forward (line 447) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Up_ResidualBlock (line 455) | class Up_ResidualBlock(nn.Module):
    method __init__ (line 457) | def __init__(self,
    method forward (line 489) | def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
  class Encoder3d (line 500) | class Encoder3d(nn.Module):
    method __init__ (line 502) | def __init__(
    method forward (line 559) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Decoder3d (line 616) | class Decoder3d(nn.Module):
    method __init__ (line 618) | def __init__(
    method forward (line 672) | def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
  function count_conv3d (line 726) | def count_conv3d(model):
  class WanVAE_ (line 734) | class WanVAE_(nn.Module):
    method __init__ (line 736) | def __init__(
    method forward (line 778) | def forward(self, x, scale=[0, 1]):
    method encode (line 783) | def encode(self, x, scale):
    method decode (line 812) | def decode(self, z, scale):
    method reparameterize (line 841) | def reparameterize(self, mu, log_var):
    method sample (line 846) | def sample(self, imgs, deterministic=False):
    method clear_cache (line 853) | def clear_cache(self):
  function _video_vae (line 863) | def _video_vae(pretrained_path=None, z_dim=16, dim=160, device="cpu", **...
  class Wan2_2_VAE (line 888) | class Wan2_2_VAE:
    method __init__ (line 890) | def __init__(
    method encode (line 1024) | def encode(self, videos):
    method decode (line 1038) | def decode(self, zs):

FILE: wan/speech2video.py
  function load_safetensors (line 39) | def load_safetensors(path):
  class WanS2V (line 47) | class WanS2V:
    method __init__ (line 49) | def __init__(
    method _configure_model (line 146) | def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
    method get_size_less_than_area (line 189) | def get_size_less_than_area(self,
    method prepare_default_cond_input (line 252) | def prepare_default_cond_input(self,
    method encode_audio (line 283) | def encode_audio(self, audio_path, infer_frames):
    method read_last_n_frames (line 297) | def read_last_n_frames(self,
    method load_pose_cond (line 336) | def load_pose_cond(self, pose_video, num_repeat, infer_frames, size):
    method get_gen_size (line 378) | def get_gen_size(self, size, max_area, ref_image_path, pre_video_path):
    method generate (line 392) | def generate(
    method tts (line 681) | def tts(self, tts_prompt_audio, tts_prompt_text, tts_text):
    method load_tts (line 697) | def load_tts(self):

FILE: wan/text2video.py
  class WanT2V (line 31) | class WanT2V:
    method __init__ (line 33) | def __init__(
    method _configure_model (line 125) | def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
    method _prepare_model_for_timestep (line 169) | def _prepare_model_for_timestep(self, t, boundary, offload_model):
    method generate (line 203) | def generate(self,

FILE: wan/textimage2video.py
  class WanTI2V (line 34) | class WanTI2V:
    method __init__ (line 36) | def __init__(
    method _configure_model (line 118) | def _configure_model(self, model, use_sp, dit_fsdp, shard_fn,
    method generate (line 162) | def generate(self,
    method t2v (line 239) | def t2v(self,
    method i2v (line 413) | def i2v(self,

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/prompt_extend.py
  class PromptOutput (line 53) | class PromptOutput(object):
    method add_custom_field (line 60) | def add_custom_field(self, key: str, value) -> None:
  class PromptExpander (line 64) | class PromptExpander:
    method __init__ (line 66) | def __init__(self, model_name, task, is_vl=False, device=0, **kwargs):
    method extend_with_img (line 72) | def extend_with_img(self,
    method extend (line 81) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method decide_system_prompt (line 84) | def decide_system_prompt(self, tar_lang="zh", prompt=None):
    method __call__ (line 95) | def __call__(self,
  class DashScopePromptExpander (line 117) | class DashScopePromptExpander(PromptExpander):
    method __init__ (line 119) | def __init__(self,
    method extend (line 158) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method extend_with_img (line 194) | def extend_with_img(self,
  class QwenPromptExpander (line 262) | class QwenPromptExpander(PromptExpander):
    method __init__ (line 271) | def __init__(self,
    method extend (line 337) | def extend(self, prompt, system_prompt, seed=-1, *args, **kwargs):
    method extend_with_img (line 368) | def extend_with_img(self,
  function test (line 441) | def test(method,

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 17) | def rand_name(length=8, suffix=''):
  function merge_video_audio (line 26) | def merge_video_audio(video_path: str, audio_path: str):
  function save_video (line 90) | def save_video(tensor,
  function save_image (line 123) | def save_image(tensor, save_file, nrow=8, normalize=True, value_range=(-...
  function str2bool (line 145) | def str2bool(v):
  function masks_like (line 172) | def masks_like(tensor, zero=False, generator=None, p=0.2):
  function best_output_size (line 202) | def best_output_size(w, h, dw, dh, expected_area):
  function download_cosyvoice_repo (line 228) | def download_cosyvoice_repo(repo_path):
  function download_cosyvoice_model (line 236) | def download_cosyvoice_model(model_name, model_path):