(input_sizes, kernel_sizes, stride, padding)

(self, height, width)

( self, latents, timestep, add_time_id, cfg_scales, image_emb_

(text, keep_punctuation_exact_string=None)

(self, dit: WanModel, x, t_mod)

(self, dit: FluxDiT, hidden_states, conditioning)

(self, dit: WanModel, x, t_mod)

(video_guide, video_guide_folder, video_style, video_style_folder)

(frames_path, keyframes_path)

( pretrained=False, pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', **kwarg

(self, max_frame_length)

(weight: torch.Tensor)

(self, entity_masks, prompt_seq_len, image_seq_len)

(self)

(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16)

(state_dict, with_shape=True)

(self, state_dict, lora_prefix="lora_unet_", alpha=1.0)

(state_dict: dict)

(self, attention_mask, num_tokens_for_output_images)

( model_name: str, pretrained: Optional[str] = None, precision: str = 'fp32',

(self, attention_mask, num_tokens_for_output_images)

(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0)

(image, height, width)

(self, image)

(pil_image, max_image_size)

(self, attention_mask, num_tokens_for_img)

(self, position_ids, num_tokens_for_img)

(self, height, width)

(args)

(self, tokens)

(self, latent, tiled=False, tile_size=64, tile_stride=32)

(self, latent, tiled=False, tile_size=64, tile_stride=32)

(self, latent, tiled=False, tile_size=64, tile_stride=32)

(self, latent, tiled=False, tile_size=64, tile_stride=32)

(self, latent, tiled=False, tile_size=64, tile_stride=32)

(self, images)

(self, tokens: List[str])

(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16))

(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16))

(val, d)

()

(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev)

( model_id_list: List[Preset_model_id] = [], downloading_priority: List[Preset_model_website] = ["Mode

( cfg: Dict, force_hf_hub: bool = False, cache_dir: Union[str, None] = None, )

(self)

(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: di

(self, text)

(self, image, num_frames, height, width)

(self, image, tiled=False, tile_size=64, tile_stride=32)

(self, image, tiled=False, tile_size=64, tile_stride=32)

(self, image, num_frames, height, width)

(self, image, normalize: bool = False)

(self, image, normalize: bool = False)

(self, image)

(self, image, noise_aug_strength, seed=None)

(self, images, tiled=False, tile_size=64, tile_stride=32)

(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device)

(self, prompt, text_encoder, tokenizer, max_length, device)

(self, prompt, max_length, device)

(self, prompt, max_length, device)

(self, prompt, max_length, device)

(self, prompt, max_length,

(self, prompt, images,

(self, prompt, text_encoder, tokenizer, max_length, device)

(self, prompt, text_encoder, tokenizer, max_length, device)

(self, prompt, text_encoder, tokenizer, max_length, device)

(self, text, normalize: bool = False)

(self, text, normalize: bool = False)

(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16))

(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x)

(self, prompt, local_prompts, masks, mask_scales)

(self, file_name)

(self, extrinsic, color_map='red', hw_ratio=9/16, base_xval=1, zval=3)

(self, state_dict)

(self, model_manager: ModelManager)

(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_cla

(self, model_manager: ModelManager)

(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_cla

(self, model_manager: ModelManager)

(self, model_manager: ModelManager)

(self, model_manager: ModelManager, prompt_refiner_classes=[])

(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_cla

(self, model_manager: ModelManager, prompt_refiner_classes=[])

(self, model_manager: ModelManager, prompt_refiner_classes=[])

(self, model_manager: ModelManager, prompt_refiner_classes=[])

(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_cla

(self, model_manager: ModelManager)

(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_cla

(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True, return_attn_probs=False, tp_grou

(self, text_logits, labels)

(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_lat

(self, latents)

(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs)

(self, inputs: torch.Tensor, xq: torch.Tensor)

(self, sample)

(self, sample)

(self, input_ids, attention_mask, hidden_state_skip_layer=2)