fix: SDXL Lora Loader not showing weight input (#4430)

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No


## Description


## Related Tickets & Documents

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- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

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## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?

invokeai/app/invocations/model.py

@@ -249,7 +249,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
-    weight: float = Field(default=0.75, description=FieldDescriptions.lora_weight)
+    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = Field(
         default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNET"
     )

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -215,7 +215,10 @@ class InvokeAIDiffuserComponent:
                                 dim=0,
                             ),
                         }
-                    (encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
+                    (
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                    ) = self._concat_conditionings_for_batch(
                         conditioning_data.unconditioned_embeddings.embeds,
                         conditioning_data.text_embeddings.embeds,
                     )
@@ -277,7 +280,10 @@ class InvokeAIDiffuserComponent:
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
 
         if wants_cross_attention_control:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_cross_attention_controlled_conditioning(
                 sample,
                 timestep,
                 conditioning_data,
@@ -285,7 +291,10 @@ class InvokeAIDiffuserComponent:
                 **kwargs,
             )
         elif self.sequential_guidance:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning_sequentially(
                 sample,
                 timestep,
                 conditioning_data,
@@ -293,7 +302,10 @@ class InvokeAIDiffuserComponent:
             )
 
         else:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning(
                 sample,
                 timestep,
                 conditioning_data,

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -562,18 +562,14 @@ def rgb2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
     else:
-        rlt = (
-            np.matmul(
+        rlt = np.matmul(
             img,
             [
                 [65.481, -37.797, 112.0],
                 [128.553, -74.203, -93.786],
                 [24.966, 112.0, -18.214],
             ],
-            )
-            / 255.0
-            + [16, 128, 128]
-        )
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -592,18 +588,14 @@ def ycbcr2rgb(img):
     if in_img_type != np.uint8:
         img *= 255.0
     # convert
-    rlt = (
-        np.matmul(
+    rlt = np.matmul(
         img,
         [
             [0.00456621, 0.00456621, 0.00456621],
             [0, -0.00153632, 0.00791071],
             [0.00625893, -0.00318811, 0],
         ],
-        )
-        * 255.0
-        + [-222.921, 135.576, -276.836]
-    )
+    ) * 255.0 + [-222.921, 135.576, -276.836]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -626,18 +618,14 @@ def bgr2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
     else:
-        rlt = (
-            np.matmul(
+        rlt = np.matmul(
             img,
             [
                 [24.966, 112.0, -18.214],
                 [128.553, -74.203, -93.786],
                 [65.481, -37.797, 112.0],
             ],
-            )
-            / 255.0
-            + [16, 128, 128]
-        )
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:

invokeai/backend/training/textual_inversion_training.py

@@ -475,7 +475,10 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (h, w,) = (
+            (
+                h,
+                w,
+            ) = (
                 img.shape[0],
                 img.shape[1],
             )

invokeai/backend/util/mps_fixes.py

@@ -1,7 +1,7 @@
 import math
-import torch
-import diffusers
 
+import diffusers
+import torch
 
 if torch.backends.mps.is_available():
     torch.empty = torch.zeros