Update pipeline.py

pipeline.py

@@ -50,51 +50,6 @@ else:
 
 class SwDPipeline(StableDiffusion3Pipeline):
 
-    """
-    def __init__(
-            self,
-            transformer: SD3Transformer2DModel,
-            scheduler: FlowMatchEulerDiscreteScheduler,
-            vae: AutoencoderKL,
-            text_encoder: CLIPTextModelWithProjection,
-            tokenizer: CLIPTokenizer,
-            text_encoder_2: CLIPTextModelWithProjection,
-            tokenizer_2: CLIPTokenizer,
-            text_encoder_3: T5EncoderModel,
-            tokenizer_3: T5TokenizerFast,
-            image_encoder: SiglipVisionModel = None,
-            feature_extractor: SiglipImageProcessor = None,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            text_encoder_3=text_encoder_3,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            tokenizer_3=tokenizer_3,
-            transformer=transformer,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.tokenizer_max_length = (
-            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
-        )
-        self.default_sample_size = (
-            self.transformer.config.sample_size
-            if hasattr(self, "transformer") and self.transformer is not None
-            else 128
-        )
-        self.patch_size = (
-            self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
-        )
-    """
-
     @torch.no_grad()
     def __call__(
             self,