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update sdxl ver in lora metadata from v0-9 to v1-0

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Kohya S
2023-08-06 22:06:48 +09:00
parent c142dadb46
commit 92f41f1051
8 changed files with 13 additions and 13 deletions
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4
library/sdxl_model_util.py
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@@ -10,10 +10,10 @@ from library import sdxl_original_unet
VAE_SCALE_FACTOR = 0.13025
MODEL_VERSION_SDXL_BASE_V0_9 = "sdxl_base_v0-9"
MODEL_VERSION_SDXL_BASE_V1_0 = "sdxl_base_v1-0"
# Diffusersの設定を読み込むための参照モデル
DIFFUSERS_REF_MODEL_ID_SDXL = "stabilityai/stable-diffusion-xl-base-0.9" # アクセス権が必要
DIFFUSERS_REF_MODEL_ID_SDXL = "stabilityai/stable-diffusion-xl-base-1.0"
DIFFUSERS_SDXL_UNET_CONFIG = {
"act_fn": "silu",

6
networks/extract_lora_from_models.py
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@@ -61,15 +61,15 @@ def svd(args):
else:
print(f"loading original SDXL model : {args.model_org}")
text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, args.model_org, "cpu"
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_org, "cpu"
)
text_encoders_o = [text_encoder_o1, text_encoder_o2]
print(f"loading original SDXL model : {args.model_tuned}")
text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, args.model_tuned, "cpu"
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_tuned, "cpu"
)
text_encoders_t = [text_encoder_t1, text_encoder_t2]
model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9
model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0
# create LoRA network to extract weights: Use dim (rank) as alpha
if args.conv_dim is None:

2
networks/sdxl_merge_lora.py
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@@ -234,7 +234,7 @@ def merge(args):
unet,
logit_scale,
ckpt_info,
) = sdxl_model_util.load_models_from_sdxl_checkpoint(sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, args.sd_model, "cpu")
) = sdxl_model_util.load_models_from_sdxl_checkpoint(sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.sd_model, "cpu")
merge_to_sd_model(text_model1, text_model2, unet, args.models, args.ratios, merge_dtype)

2
sdxl_gen_img.py
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@@ -1294,7 +1294,7 @@ def main(args):
args.ckpt = files[0]
(_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, dtype
args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, dtype
)
# xformers、Hypernetwork対応

2
sdxl_minimal_inference.py
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@@ -112,7 +112,7 @@ if __name__ == "__main__":
# 本体RAMが少ない場合はGPUにロードするといいかも
# If the main RAM is small, it may be better to load it on the GPU
text_model1, text_model2, vae, unet, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, args.ckpt_path, "cpu"
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.ckpt_path, "cpu"
)
# Text Encoder 1はSDXL本体でもHuggingFaceのものを使っている

2
sdxl_train.py
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@@ -151,7 +151,7 @@ def train(args):
else:
save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
assert save_stable_diffusion_format, "save_model_as must be ckpt or safetensors / save_model_asはckptかsafetensorsである必要があります"
# assert save_stable_diffusion_format, "save_model_as must be ckpt or safetensors / save_model_asはckptかsafetensorsである必要があります"
# Diffusers版のxformers使用フラグを設定する関数
def set_diffusers_xformers_flag(model, valid):

4
sdxl_train_network.py
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@@ -32,13 +32,13 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
unet,
logit_scale,
ckpt_info,
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, weight_dtype)
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
self.load_stable_diffusion_format = load_stable_diffusion_format
self.logit_scale = logit_scale
self.ckpt_info = ckpt_info
return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, [text_encoder1, text_encoder2], vae, unet
return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, [text_encoder1, text_encoder2], vae, unet
def load_tokenizer(self, args):
tokenizer = sdxl_train_util.load_tokenizers(args)

4
sdxl_train_textual_inversion.py
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@@ -28,13 +28,13 @@ class SdxlTextualInversionTrainer(train_textual_inversion.TextualInversionTraine
unet,
logit_scale,
ckpt_info,
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, weight_dtype)
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
self.load_stable_diffusion_format = load_stable_diffusion_format
self.logit_scale = logit_scale
self.ckpt_info = ckpt_info
return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, [text_encoder1, text_encoder2], vae, unet
return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, [text_encoder1, text_encoder2], vae, unet
def load_tokenizer(self, args):
tokenizer = sdxl_train_util.load_tokenizers(args)