make separate U-Net for inference

sdxl_gen_img.py

@@ -57,7 +57,7 @@ import library.train_util as train_util
 import library.sdxl_model_util as sdxl_model_util
 import library.sdxl_train_util as sdxl_train_util
 from networks.lora import LoRANetwork
-from library.sdxl_original_unet import SdxlUNet2DConditionModel
+from library.sdxl_original_unet import InferSdxlUNet2DConditionModel
 from library.original_unet import FlashAttentionFunction
 from networks.control_net_lllite import ControlNetLLLite
 
@@ -290,7 +290,7 @@ class PipelineLike:
         vae: AutoencoderKL,
         text_encoders: List[CLIPTextModel],
         tokenizers: List[CLIPTokenizer],
-        unet: SdxlUNet2DConditionModel,
+        unet: InferSdxlUNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         clip_skip: int,
     ):
@@ -328,7 +328,7 @@ class PipelineLike:
         self.vae = vae
         self.text_encoders = text_encoders
         self.tokenizers = tokenizers
-        self.unet: SdxlUNet2DConditionModel = unet
+        self.unet: InferSdxlUNet2DConditionModel = unet
         self.scheduler = scheduler
         self.safety_checker = None
 
@@ -1371,6 +1371,7 @@ def main(args):
     (_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
         args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, dtype
     )
+    unet: InferSdxlUNet2DConditionModel = InferSdxlUNet2DConditionModel(unet)
 
     # xformers、Hypernetwork対応
     if not args.diffusers_xformers:
@@ -1526,10 +1527,14 @@ def main(args):
         print("set vae_dtype to float32")
         vae_dtype = torch.float32
     vae.to(vae_dtype).to(device)
+    vae.eval()
 
     text_encoder1.to(dtype).to(device)
     text_encoder2.to(dtype).to(device)
     unet.to(dtype).to(device)
+    text_encoder1.eval()
+    text_encoder2.eval()
+    unet.eval()
 
     # networkを組み込む
     if args.network_module: