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

library/sdxl_model_util.py

@@ -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",

networks/extract_lora_from_models.py

@@ -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:

networks/sdxl_merge_lora.py

@@ -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)
 

sdxl_gen_img.py

@@ -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

sdxl_minimal_inference.py

@@ -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

sdxl_train.py

@@ -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):

sdxl_train_network.py

@@ -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)

sdxl_train_textual_inversion.py

@@ -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)