Spaces:
Running
Running
File size: 7,630 Bytes
2b67076 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
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_supported_types():
return ["flux", "flux_chroma", "flux_dev_kontext", "flux_dev_umo", "flux_dev_uso", "flux_schnell" ]
@staticmethod
def query_family_maps():
models_eqv_map = {
"flux_dev_kontext" : "flux",
"flux_dev_umo" : "flux",
"flux_dev_uso" : "flux",
"flux_schnell" : "flux",
"flux_chroma" : "flux",
}
models_comp_map = {
"flux": ["flux_chroma", "flux_dev_kontext", "flux_dev_umo", "flux_dev_uso", "flux_schnell" ]
}
return models_eqv_map, models_comp_map
@staticmethod
def query_model_def(base_model_type, model_def):
flux_model = "flux-dev" if base_model_type == "flux" else base_model_type.replace("_", "-")
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,
"flux-model": flux_model,
}
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 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,
})
|