chore: ruff check - fix pycodestyle

invokeai/app/invocations/math.py

@@ -266,7 +266,7 @@ class FloatMathInvocation(BaseInvocation):
             raise ValueError("Cannot divide by zero")
         elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
             raise ValueError("Cannot raise zero to a negative power")
-        elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex:
+        elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
             raise ValueError("Root operation resulted in a complex number")
         return v
 

invokeai/app/util/metadata.py

@@ -21,11 +21,11 @@ def get_metadata_graph_from_raw_session(session_raw: str) -> Optional[dict]:
 
     # sanity check make sure the graph is at least reasonably shaped
     if (
-        type(graph) is not dict
+        not isinstance(graph, dict)
         or "nodes" not in graph
-        or type(graph["nodes"]) is not dict
+        or not isinstance(graph["nodes"], dict)
         or "edges" not in graph
-        or type(graph["edges"]) is not list
+        or not isinstance(graph["edges"], list)
     ):
         # something has gone terribly awry, return an empty dict
         return None

invokeai/backend/image_util/txt2mask.py

@@ -88,7 +88,7 @@ class Txt2Mask(object):
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
         """
-        if type(image) is str:
+        if isinstance(image, str):
             image = Image.open(image).convert("RGB")
 
         image = ImageOps.exif_transpose(image)

invokeai/backend/ip_adapter/resampler.py

@@ -56,7 +56,7 @@ class PerceiverAttention(nn.Module):
         x = self.norm1(x)
         latents = self.norm2(latents)
 
-        b, l, _ = latents.shape
+        b, L, _ = latents.shape
 
         q = self.to_q(latents)
         kv_input = torch.cat((x, latents), dim=-2)
@@ -72,7 +72,7 @@ class PerceiverAttention(nn.Module):
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
 
-        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+        out = out.permute(0, 2, 1, 3).reshape(b, L, -1)
 
         return self.to_out(out)
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -642,7 +642,7 @@ class InvokeAIDiffuserComponent:
 
         deltas = None
         uncond_latents = None
-        weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
+        weighted_cond_list = c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
 
         # below is fugly omg
         conditionings = [uc] + [c for c, weight in weighted_cond_list]

invokeai/backend/util/util.py

@@ -228,11 +228,8 @@ def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10
     angles = 2 * math.pi * rand_val
     gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
 
-    tile_grads = (
-        lambda slice1, slice2: gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
-        .repeat_interleave(d[0], 0)
-        .repeat_interleave(d[1], 1)
-    )
+    def tile_grads(slice1, slice2):
+        return gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
 
     def dot(grad, shift):
         return (

tests/nodes/test_session_queue.py

@@ -169,7 +169,7 @@ def test_prepare_values_to_insert(batch_data_collection, batch_graph):
 
     NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
     # should have 3 node field values
-    assert type(values[0].field_values) is str
+    assert isinstance(values[0].field_values, str)
     assert len(NodeFieldValueValidator.validate_json(values[0].field_values)) == 3
 
     # should have batch id and priority