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Configuration error
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import torch
from shared.utils import files_locator as fl
def get_ltxv_text_encoder_filename(text_encoder_quantization):
text_encoder_filename = "T5_xxl_1.1/T5_xxl_1.1_enc_bf16.safetensors"
if text_encoder_quantization =="int8":
text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
return fl.locate_file(text_encoder_filename, True)
class family_handler():
@staticmethod
def query_model_def(base_model_type, model_def):
LTXV_config = model_def.get("LTXV_config", "")
distilled= "distilled" in LTXV_config
extra_model_def = {}
if distilled:
extra_model_def.update({
"lock_inference_steps": True,
"no_negative_prompt" : True,
})
extra_model_def["fps"] = 30
extra_model_def["frames_minimum"] = 17
extra_model_def["frames_steps"] = 8
extra_model_def["sliding_window"] = True
extra_model_def["image_prompt_types_allowed"] = "TSEV"
extra_model_def["guide_preprocessing"] = {
"selection": ["", "PV", "DV", "EV", "V"],
"labels" : { "V": "Use LTXV raw format"}
}
extra_model_def["mask_preprocessing"] = {
"selection": ["", "A", "NA", "XA", "XNA"],
}
extra_model_def["extra_control_frames"] = 1
extra_model_def["dont_cat_preguide"]= True
return extra_model_def
@staticmethod
def query_supported_types():
return ["ltxv_13B"]
@staticmethod
def query_family_maps():
return {}, {}
@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("ltxv")
return latent_rgb_factors, latent_rgb_factors_bias
@staticmethod
def query_model_family():
return "ltxv"
@staticmethod
def query_family_infos():
return {"ltxv":(10, "LTX Video")}
@staticmethod
def get_vae_block_size(base_model_type):
return 32
@staticmethod
def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
text_encoder_filename = get_ltxv_text_encoder_filename(text_encoder_quantization)
return {
"repoId" : "DeepBeepMeep/LTX_Video",
"sourceFolderList" : ["T5_xxl_1.1", "" ],
"fileList" : [ ["added_tokens.json", "special_tokens_map.json", "spiece.model", "tokenizer_config.json"] + computeList(text_encoder_filename), ["ltxv_0.9.7_VAE.safetensors", "ltxv_0.9.7_spatial_upscaler.safetensors", "ltxv_scheduler.json"] + computeList(model_filename) ]
}
@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):
from .ltxv import LTXV
ltxv_model = LTXV(
model_filepath = model_filename,
text_encoder_filepath = get_ltxv_text_encoder_filename(text_encoder_quantization),
model_type = model_type,
base_model_type = base_model_type,
model_def = model_def,
dtype = dtype,
# quantizeTransformer = quantizeTransformer,
VAE_dtype = VAE_dtype,
mixed_precision_transformer = mixed_precision_transformer
)
pipeline = ltxv_model.pipeline
pipe = {"transformer" : pipeline.video_pipeline.transformer, "vae" : pipeline.vae, "text_encoder" : pipeline.video_pipeline.text_encoder, "latent_upsampler" : pipeline.latent_upsampler}
return ltxv_model, pipe
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
pass
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