mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
import torch
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def get_ltxv_text_encoder_filename(text_encoder_quantization):
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text_encoder_filename = "ckpts/T5_xxl_1.1/T5_xxl_1.1_enc_bf16.safetensors"
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if text_encoder_quantization =="int8":
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text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
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return text_encoder_filename
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class family_handler():
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@staticmethod
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def query_model_def(base_model_type, model_def):
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LTXV_config = model_def.get("LTXV_config", "")
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distilled= "distilled" in LTXV_config
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extra_model_def = {}
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if distilled:
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extra_model_def.update({
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"lock_inference_steps": True,
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"no_negative_prompt" : True,
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})
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extra_model_def["fps"] = 30
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extra_model_def["frames_minimum"] = 17
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extra_model_def["frames_steps"] = 8
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extra_model_def["sliding_window"] = True
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extra_model_def["image_prompt_types_allowed"] = "TSEV"
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extra_model_def["guide_preprocessing"] = {
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"selection": ["", "PV", "DV", "EV", "V"],
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"labels" : { "V": "Use LTXV raw format"}
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}
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extra_model_def["mask_preprocessing"] = {
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"selection": ["", "A", "NA", "XA", "XNA"],
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}
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return extra_model_def
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@staticmethod
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def query_supported_types():
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return ["ltxv_13B"]
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@staticmethod
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def query_family_maps():
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return {}, {}
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@staticmethod
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def get_rgb_factors(base_model_type ):
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from shared.RGB_factors import get_rgb_factors
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latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("ltxv")
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return latent_rgb_factors, latent_rgb_factors_bias
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@staticmethod
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def query_model_family():
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return "ltxv"
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@staticmethod
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def query_family_infos():
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return {"ltxv":(10, "LTX Video")}
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@staticmethod
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def get_vae_block_size(base_model_type):
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return 32
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@staticmethod
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def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
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text_encoder_filename = get_ltxv_text_encoder_filename(text_encoder_quantization)
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return {
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"repoId" : "DeepBeepMeep/LTX_Video",
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"sourceFolderList" : ["T5_xxl_1.1", "" ],
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"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) ]
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}
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@staticmethod
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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):
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from .ltxv import LTXV
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ltxv_model = LTXV(
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model_filepath = model_filename,
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text_encoder_filepath = get_ltxv_text_encoder_filename(text_encoder_quantization),
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model_type = model_type,
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base_model_type = base_model_type,
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model_def = model_def,
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dtype = dtype,
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# quantizeTransformer = quantizeTransformer,
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VAE_dtype = VAE_dtype,
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mixed_precision_transformer = mixed_precision_transformer
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)
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pipeline = ltxv_model.pipeline
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pipe = {"transformer" : pipeline.video_pipeline.transformer, "vae" : pipeline.vae, "text_encoder" : pipeline.video_pipeline.text_encoder, "latent_upsampler" : pipeline.latent_upsampler}
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return ltxv_model, pipe
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@staticmethod
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def update_default_settings(base_model_type, model_def, ui_defaults):
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pass
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