refactor: Refactored code by Merging isinstance calls (#7710)

* Merged isinstance calls to make the code simpler.

* Corrected formatting errors using ruff.

---------

Co-authored-by: Sayak Paul <[email protected]>
Co-authored-by: YiYi Xu <[email protected]>

Author: 3 people

examples/community/pipeline_stable_diffusion_upscale_ldm3d.py

@@ -460,7 +460,7 @@ def check_inputs(
             )
 
         # verify batch size of prompt and image are same if image is a list or tensor or numpy array
-        if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
+        if isinstance(image, (list, np.ndarray, torch.Tensor)):
             if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
             elif prompt is not None and isinstance(prompt, list):

src/diffusers/models/unets/unet_2d_condition.py

@@ -685,7 +685,7 @@ def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: i
             positive_len = 768
             if isinstance(cross_attention_dim, int):
                 positive_len = cross_attention_dim
-            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
+            elif isinstance(cross_attention_dim, (list, tuple)):
                 positive_len = cross_attention_dim[0]
 
             feature_type = "text-only" if attention_type == "gated" else "text-image"

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -817,7 +817,7 @@ def __init__(
             positive_len = 768
             if isinstance(cross_attention_dim, int):
                 positive_len = cross_attention_dim
-            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
+            elif isinstance(cross_attention_dim, (list, tuple)):
                 positive_len = cross_attention_dim[0]
 
             feature_type = "text-only" if attention_type == "gated" else "text-image"

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -197,7 +197,7 @@ def check_inputs(
             )
 
         # verify batch size of prompt and image are same if image is a list or tensor or numpy array
-        if isinstance(image, list) or isinstance(image, np.ndarray):
+        if isinstance(image, (list, np.ndarray)):
             if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
             elif prompt is not None and isinstance(prompt, list):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -221,7 +221,7 @@ def check_inputs(self, prompt, image, callback_steps):
             )
 
         # verify batch size of prompt and image are same if image is a list or tensor
-        if isinstance(image, list) or isinstance(image, torch.Tensor):
+        if isinstance(image, (list, torch.Tensor)):
             if isinstance(prompt, str):
                 batch_size = 1
             else:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -468,7 +468,7 @@ def check_inputs(
             )
 
         # verify batch size of prompt and image are same if image is a list or tensor or numpy array
-        if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
+        if isinstance(image, (list, np.ndarray, torch.Tensor)):
             if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
             elif prompt is not None and isinstance(prompt, list):

src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py

@@ -185,7 +185,7 @@ def preprocess(image):
 def preprocess_mask(mask, batch_size: int = 1):
     if not isinstance(mask, torch.Tensor):
         # preprocess mask
-        if isinstance(mask, PIL.Image.Image) or isinstance(mask, np.ndarray):
+        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
             mask = [mask]
 
         if isinstance(mask, list):

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -347,11 +347,7 @@ def step(
                 otherwise a tuple is returned where the first element is the sample tensor.
         """
 
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
+        if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"

src/diffusers/schedulers/scheduling_edm_euler.py

@@ -310,11 +310,7 @@ def step(
                 returned, otherwise a tuple is returned where the first element is the sample tensor.
         """
 
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
+        if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -375,11 +375,7 @@ def step(
 
         """
 
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
+        if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"