bug fixed

app.py

@@ -103,28 +103,9 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
     return p.replace("{prompt}", positive), n + negative
 
 
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--is_lora', action='store_true', help='enable lora ckpt loading')
-    parser.add_argument('--repo_id', default="PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", type=str)
-    parser.add_argument('--lora_repo_id', default=None, type=str)
-    parser.add_argument('--model_path', default=None, type=str)
-    parser.add_argument(
-        '--pipeline_load_from', default="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers", type=str,
-        help="Download for loading text_encoder, tokenizer and vae "
-             "from https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS")
-    parser.add_argument('--T5_token_max_length', default=120, type=int, help='max length of tokens for T5')
-    return parser.parse_args()
-
-
-args = get_args()
-
 if torch.cuda.is_available():
     weight_dtype = torch.float16
-    T5_token_max_length = args.T5_token_max_length
-    model_path = args.model_path
-    if 'Sigma' in args.model_path:
-        T5_token_max_length = 300
+    T5_token_max_length = 300
 
     # tmp patches for diffusers PixArtSigmaPipeline Implementation
     print(
@@ -132,29 +113,17 @@ if torch.cuda.is_available():
         "using scripts.diffusers_patches.pixart_sigma_init_patched_inputs")
     setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs)
 
-    if not args.is_lora:
-        transformer = Transformer2DModel.from_pretrained(
-            model_path,
-            subfolder='transformer',
-            torch_dtype=weight_dtype,
-        )
-        pipe = PixArtSigmaPipeline.from_pretrained(
-            args.pipeline_load_from,
-            transformer=transformer,
-            torch_dtype=weight_dtype,
-            use_safetensors=True,
-        )
-    else:
-        assert args.lora_repo_id is not None
-        transformer = Transformer2DModel.from_pretrained(args.repo_id, subfolder="transformer", torch_dtype=torch.float16)
-        transformer = PeftModel.from_pretrained(transformer, args.lora_repo_id)
-        pipe = PixArtSigmaPipeline.from_pretrained(
-            args.repo_id,
-            transformer=transformer,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-        )
-        del transformer
+    transformer = Transformer2DModel.from_pretrained(
+        "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
+        subfolder='transformer',
+        torch_dtype=weight_dtype,
+    )
+    pipe = PixArtSigmaPipeline.from_pretrained(
+        "PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
+        transformer=transformer,
+        torch_dtype=weight_dtype,
+        use_safetensors=True,
+    )
 
     if os.getenv('CONSISTENCY_DECODER', False):
         print("Using DALL-E 3 Consistency Decoder")