| import torch
|
| from shared.utils.hf import build_hf_url
|
|
|
| class family_handler():
|
|
|
| @staticmethod
|
| def set_cache_parameters(cache_type, base_model_type, model_def, inputs, skip_steps_cache):
|
| if base_model_type == "sky_df_1.3B":
|
| coefficients= [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01]
|
| else:
|
| coefficients= [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404]
|
|
|
| skip_steps_cache.coefficients = coefficients
|
|
|
| @staticmethod
|
| def query_model_def(base_model_type, model_def):
|
| extra_model_def = {}
|
| text_encoder_folder = "umt5-xxl"
|
| extra_model_def["text_encoder_URLs"] = [
|
| build_hf_url("DeepBeepMeep/Wan2.1", text_encoder_folder, "models_t5_umt5-xxl-enc-bf16.safetensors"),
|
| build_hf_url("DeepBeepMeep/Wan2.1", text_encoder_folder, "models_t5_umt5-xxl-enc-quanto_int8.safetensors"),
|
| ]
|
| extra_model_def["text_encoder_folder"] = text_encoder_folder
|
| if base_model_type in ["sky_df_14B"]:
|
| fps = 24
|
| else:
|
| fps = 16
|
| extra_model_def["fps"] =fps
|
| extra_model_def["frames_minimum"] = 17
|
| extra_model_def["frames_steps"] = 20
|
| extra_model_def["latent_size"] = 4
|
| extra_model_def["sliding_window"] = True
|
| extra_model_def["perturbation"] = True
|
| extra_model_def["tea_cache"] = True
|
| extra_model_def["guidance_max_phases"] = 1
|
| extra_model_def["flow_shift"] = True
|
| extra_model_def["model_modes"] = {
|
| "choices": [
|
| ("Synchronous", 0),
|
| ("Asynchronous (better quality but around 50% extra steps added)", 5),
|
| ],
|
| "default": 0,
|
| "label" : "Generation Type"
|
| }
|
|
|
| extra_model_def["image_prompt_types_allowed"] = "TSV"
|
|
|
|
|
| return extra_model_def
|
|
|
| @staticmethod
|
| def query_supported_types():
|
| return ["sky_df_1.3B", "sky_df_14B"]
|
|
|
|
|
| @staticmethod
|
| def query_family_maps():
|
| models_eqv_map = {
|
| "sky_df_1.3B" : "sky_df_14B",
|
| }
|
|
|
| models_comp_map = {
|
| "sky_df_14B": ["sky_df_1.3B"],
|
| }
|
| return models_eqv_map, models_comp_map
|
|
|
|
|
|
|
| @staticmethod
|
| def query_model_family():
|
| return "wan"
|
|
|
| @staticmethod
|
| def query_family_infos():
|
| return {}
|
|
|
| @staticmethod
|
| def register_lora_cli_args(parser, lora_root):
|
| from .wan_handler import family_handler as wan_family_handler
|
|
|
| return wan_family_handler.register_lora_cli_args(parser, lora_root)
|
|
|
| @staticmethod
|
| def get_lora_dir(base_model_type, args, lora_root):
|
| from .wan_handler import family_handler as wan_family_handler
|
|
|
| return wan_family_handler.get_lora_dir(base_model_type, args, lora_root)
|
|
|
| @staticmethod
|
| def get_rgb_factors(base_model_type ):
|
| from shared.RGB_factors import get_rgb_factors
|
| latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("wan", base_model_type)
|
| return latent_rgb_factors, latent_rgb_factors_bias
|
|
|
| @staticmethod
|
| def query_model_files(computeList, base_model_type, model_def=None):
|
| from .wan_handler import family_handler
|
| return family_handler.query_model_files(computeList, base_model_type, model_def)
|
|
|
| @staticmethod
|
| def load_model(model_filename, model_type, base_model_type, model_def, quantizeTransformer = False, text_encoder_quantization = None, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized= False, submodel_no_list = None, text_encoder_filename = None, **kwargs):
|
| from .configs import WAN_CONFIGS
|
| cfg = WAN_CONFIGS['t2v-14B']
|
| from . import DTT2V
|
| wan_model = DTT2V(
|
| config=cfg,
|
| checkpoint_dir="ckpts",
|
| model_filename=model_filename,
|
| model_type = model_type,
|
| model_def = model_def,
|
| base_model_type=base_model_type,
|
| text_encoder_filename= text_encoder_filename,
|
| quantizeTransformer = quantizeTransformer,
|
| dtype = dtype,
|
| VAE_dtype = VAE_dtype,
|
| mixed_precision_transformer = mixed_precision_transformer,
|
| save_quantized = save_quantized
|
| )
|
|
|
| pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
|
| return wan_model, pipe
|
|
|
| @staticmethod
|
| def update_default_settings(base_model_type, model_def, ui_defaults):
|
| ui_defaults.update({
|
| "guidance_scale": 6.0,
|
| "flow_shift": 8,
|
| "sliding_window_discard_last_frames" : 0,
|
| "resolution": "1280x720" if "720" in base_model_type else "960x544",
|
| "sliding_window_size" : 121 if "720" in base_model_type else 97,
|
| "RIFLEx_setting": 2,
|
| "guidance_scale": 6,
|
| "flow_shift": 8,
|
| })
|
|
|
