support Diffusers format

sdxl_gen_img.py

@@ -1288,38 +1288,9 @@ def main(args):
         if len(files) == 1:
             args.ckpt = files[0]
 
-    use_stable_diffusion_format = os.path.isfile(args.ckpt)
+    (_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
-    assert use_stable_diffusion_format, "Diffusers pretrained models are not supported yet"
+        args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, dtype
-    print("load StableDiffusion checkpoint")
-    text_encoder1, text_encoder2, vae, unet, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
-        sdxl_model_util.MODEL_VERSION_SDXL_BASE_V0_9, args.ckpt, "cpu"
     )
-    # else:
-    #     print("load Diffusers pretrained models")
-    # TODO use Diffusers 0.18.1 and support SDXL pipeline
-    # raise NotImplementedError("Diffusers pretrained models are not supported yet")
-    # loading_pipe = StableDiffusionXLPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
-    # text_encoder = loading_pipe.text_encoder
-    # vae = loading_pipe.vae
-    # unet = loading_pipe.unet
-    # tokenizer = loading_pipe.tokenizer
-    # del loading_pipe
-
-    # # Diffusers U-Net to original U-Net
-    # original_unet = SdxlUNet2DConditionModel(
-    #     unet.config.sample_size,
-    #     unet.config.attention_head_dim,
-    #     unet.config.cross_attention_dim,
-    #     unet.config.use_linear_projection,
-    #     unet.config.upcast_attention,
-    # )
-    # original_unet.load_state_dict(unet.state_dict())
-    # unet = original_unet
-
-    # VAEを読み込む
-    if args.vae is not None:
-        vae = model_util.load_vae(args.vae, dtype)
-        print("additional VAE loaded")
 
     # xformers、Hypernetwork対応
     if not args.diffusers_xformers:
@@ -1329,8 +1300,7 @@ def main(args):
 
     # tokenizerを読み込む
     print("loading tokenizer")
-    if use_stable_diffusion_format:
+    tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)
-        tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)
 
     # schedulerを用意する
     sched_init_args = {}