Fix linting issues

src/mflux/models/transformer/joint_attention.py

@@ -28,8 +28,6 @@ class JointAttention(nn.Module):
         encoder_hidden_states: mx.array,
         image_rotary_emb: mx.array,
     ) -> (mx.array, mx.array):
-        residual = hidden_states
-
         query = self.to_q(hidden_states)
         key = self.to_k(hidden_states)
         value = self.to_v(hidden_states)

src/mflux/post_processing/generated_image.py

@@ -83,15 +83,6 @@ class GeneratedImage:
             # Define the UserComment tag ID
             USER_COMMENT_TAG_ID = 0x9286
 
-            # Create an EXIF dictionary
-            exif_dict = {
-                "0th": {},
-                "Exif": {USER_COMMENT_TAG_ID: user_comment_bytes},
-                "GPS": {},
-                "1st": {},
-                "thumbnail": None,
-            }
-
             # Create a piexif-compatible dictionary structure
             exif_piexif_dict = {"Exif": {USER_COMMENT_TAG_ID: user_comment_bytes}}
 

src/mflux/weights/lora_converter.py

@@ -25,7 +25,6 @@ class LoRAConverter:
     def _load_pytorch_weights(lora_path: str) -> dict:
         state_dict = {}
         with safe_open(lora_path, framework="pt") as f:
-            metadata = f.metadata()
             for k in f.keys():
                 state_dict[k] = f.get_tensor(k)
         return state_dict

src/mflux/weights/lora_util.py

@@ -78,7 +78,7 @@ class LoraUtil:
                     if parentKey not in visited:
                         visited[parentKey] = {}
                     visited[parentKey][splitKey] = weight
-                    if not "weight" in target:
+                    if "weight" not in target:
                         raise ValueError(f"LoRA weights for layer {parentKey} cannot be loaded into the model.")
                     if "lora_A" in visited[parentKey] and "lora_B" in visited[parentKey]:
                         lora_a = visited[parentKey]["lora_A"]