Update gen2seg_sd_pipeline.py

gen2seg_sd_pipeline.py

@@ -50,7 +50,7 @@ def zeros_tensor(
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 @dataclass
-class Gen2SegSDSegOutput(BaseOutput):
+class gen2segSDSegOutput(BaseOutput):
     """
     Output class for gen2seg Instance Segmentation prediction pipeline.
 
@@ -67,7 +67,7 @@ class Gen2SegSDSegOutput(BaseOutput):
     latent: Union[None, torch.Tensor]
 
 
-class Gen2SegSDPipeline(DiffusionPipeline):
+class gen2segSDPipeline(DiffusionPipeline):
     """
     # add
     Pipeline for Instance Segmentation prediction using our Stable Diffusion model.  
@@ -251,7 +251,7 @@ class Gen2SegSDPipeline(DiffusionPipeline):
                 within the ensemble. These codes can be saved, modified, and used for subsequent calls with the
                 `latents` argument.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`Gen2SegSDSegOutput`] instead of a plain tuple.
+                Whether or not to return a [`gen2segSDSegOutput`] instead of a plain tuple.
 
         # add
         E2E FT models are deterministic single step models involving no ensembling, i.e. E=1.
@@ -397,7 +397,7 @@ class Gen2SegSDPipeline(DiffusionPipeline):
         if not return_dict:
             return (prediction, pred_latent)
 
-        return Gen2SegSDSegOutput(
+        return gen2segSDSegOutput(
             prediction=prediction,
             latent=pred_latent,
         )