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import torch
def get_ltxv_text_encoder_filename(text_encoder_quantization):
text_encoder_filename = "ckpts/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 text_encoder_filename
class family_handler():
@staticmethod
def query_model_def(base_model_type, model_def):
flux_model = model_def.get("flux-model", "flux-dev")
flux_schnell = flux_model == "flux-schnell"
flux_chroma = flux_model == "flux-chroma"
flux_uso = flux_model == "flux-dev-uso"
flux_umo = flux_model == "flux-dev-umo"
flux_kontext = flux_model == "flux-dev-kontext"
extra_model_def = {
"image_outputs" : True,
"no_negative_prompt" : not flux_chroma,
}
if flux_chroma:
extra_model_def["guidance_max_phases"] = 1
elif not flux_schnell:
extra_model_def["embedded_guidance"] = True
if flux_uso :
extra_model_def["any_image_refs_relative_size"] = True
extra_model_def["no_background_removal"] = True
extra_model_def["image_ref_choices"] = {
"choices":[("First Image is a Reference Image, and then the next ones (up to two) are Style Images", "KI"),
("Up to two Images are Style Images", "KIJ")],
"default": "KI",
"letters_filter": "KIJ",
"label": "Reference Images / Style Images"
}
if flux_kontext:
extra_model_def["inpaint_support"] = True
extra_model_def["image_ref_choices"] = {
"choices": [
("None", ""),
("Conditional Images is first Main Subject / Landscape and may be followed by People / Objects", "KI"),
("Conditional Images are People / Objects", "I"),
],
"letters_filter": "KI",
}
extra_model_def["background_removal_label"]= "Remove Backgrounds only behind People / Objects except main Subject / Landscape"
elif flux_umo:
extra_model_def["image_ref_choices"] = {
"choices": [
("Conditional Images are People / Objects", "I"),
],
"letters_filter": "I",
"visible": False
}
extra_model_def["fit_into_canvas_image_refs"] = 0
return extra_model_def
@staticmethod
def query_supported_types():
return ["flux"]
@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("flux")
return latent_rgb_factors, latent_rgb_factors_bias
@staticmethod
def query_model_family():
return "flux"
@staticmethod
def query_family_infos():
return {"flux":(30, "Flux 1")}
@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/Flux",
"sourceFolderList" : ["siglip-so400m-patch14-384", "",],
"fileList" : [ ["config.json", "preprocessor_config.json", "model.safetensors"], ["flux_vae.safetensors"] ]
},
{
"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) ]
},
{
"repoId" : "DeepBeepMeep/HunyuanVideo",
"sourceFolderList" : [ "clip_vit_large_patch14", ],
"fileList" :[
["config.json", "merges.txt", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "vocab.json"],
]
}
]
@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 .flux_main import model_factory
flux_model = model_factory(
checkpoint_dir="ckpts",
model_filename=model_filename,
model_type = model_type,
model_def = model_def,
base_model_type=base_model_type,
text_encoder_filename= get_ltxv_text_encoder_filename(text_encoder_quantization),
quantizeTransformer = quantizeTransformer,
dtype = dtype,
VAE_dtype = VAE_dtype,
mixed_precision_transformer = mixed_precision_transformer,
save_quantized = save_quantized
)
pipe = { "transformer": flux_model.model, "vae" : flux_model.vae, "text_encoder" : flux_model.clip, "text_encoder_2" : flux_model.t5}
if flux_model.vision_encoder is not None:
pipe["siglip_model"] = flux_model.vision_encoder
if flux_model.feature_embedder is not None:
pipe["feature_embedder"] = flux_model.feature_embedder
return flux_model, pipe
@staticmethod
def fix_settings(base_model_type, settings_version, model_def, ui_defaults):
flux_model = model_def.get("flux-model", "flux-dev")
flux_uso = flux_model == "flux-dev-uso"
if flux_uso and settings_version < 2.29:
video_prompt_type = ui_defaults.get("video_prompt_type", "")
if "I" in video_prompt_type:
video_prompt_type = video_prompt_type.replace("I", "KI")
ui_defaults["video_prompt_type"] = video_prompt_type
if settings_version < 2.34:
ui_defaults["denoising_strength"] = 1.
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
flux_model = model_def.get("flux-model", "flux-dev")
flux_uso = flux_model == "flux-dev-uso"
flux_umo = flux_model == "flux-dev-umo"
flux_kontext = flux_model == "flux-dev-kontext"
ui_defaults.update({
"embedded_guidance": 2.5,
})
if flux_kontext or flux_uso:
ui_defaults.update({
"video_prompt_type": "KI",
"denoising_strength": 1.,
})
elif flux_umo:
ui_defaults.update({
"video_prompt_type": "I",
"remove_background_images_ref": 0,
})
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