↓ 4 callersMethodprepare_vae_latent(self, curr_kvlens, curr_rope, image_sizes, new_token_ids, seed=None)
lightx2v/models/schedulers/bagel/scheduler.py:51
↓ 4 callersFunctionresize_by_area(image, target_area, keep_aspect_ratio=True, divisor=64, padding_color=(0, 0, 0))
tools/preprocess/utils.py:105
↓ 3 callersMethod__init__(self, block_index, block_prefix, task, mm_type, config)
lightx2v/models/networks/wan/weights/matrix_game2/transformer_weights.py:92
↓ 3 callersMethod__init__(
self,
block_name: str,
*,
mm_type: str,
ln_type: str,
qk_nor
lightx2v/models/networks/worldmirror/weights/transformer_weights.py:54
↓ 3 callersMethod__init__(
self,
block_index,
task,
mm_type,
config,
subname="mlp",
lightx2v/models/networks/bagel/weights/transformer_weights.py:93
↓ 3 callersMethod__init__(self, prefix, mm_type, create_cuda_buffer=False, create_cpu_buffer=False, lazy_load=False, lazy_load_file=Non
lightx2v/models/networks/cosmos3/weights/transformer_weights.py:347
↓ 3 callersMethod__init__(
self,
version=None,
config=None,
use_cls_token=True,
image_size=224,
lightx2v/models/input_encoders/hf/hunyuan3d/conditioner.py:59
↓ 3 callersMethod_apply_prope Apply Projective Positional Encoding to Q, K, V. Args: q: Query tensor [B, L, H, D] or [B, H, L, D] k: Key t
lightx2v/models/networks/worldplay/infer/ar_transformer_infer.py:394
↓ 3 callersMethod_embed_timestep(self, weights, timestep, length, device, dtype)
lightx2v/models/networks/cosmos3/infer/pre_infer.py:64
↓ 3 callersMethod_ensure_mxfp8_quant_fuse_ready(self, phase, *tensors, module_names=(), required_module_attrs=("weight", "weight_scale", "alpha"))
lightx2v/models/networks/wan/infer/mxfp8_fuse.py:49