Add ruff CI gate and fix deterministic lint issues

Introduces pyproject.toml + .github/workflows/ruff.yml that gate
E701, E711, E712, F401, F541 on every PR and push to main.

Fixes the existing findings for those rules:
- Remove unused imports (sklearn.silhouette_score, numpy in several
  files, typing.Optional, get_one_face, gpu_cvt_color, sys,
  insightface.face_align)
- Annotate the intentional tkinter_fix side-effect import with
  `# noqa: F401`
- Split multi-statement `if x: y` one-liners onto separate lines
- Replace `state == True` / `state == False` with truthiness checks
- Drop `f` prefix from f-strings with no placeholders

F841 (unused-variable), E402 (module-level-import-not-at-top), and
F821 (undefined-name) are left out of the gate for now — they surface
real findings (including a latent NameError in face_swapper.py) that
require human review to fix safely.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

modules/processors/frame/core.py

@@ -60,7 +60,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
     current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
 
     for frame_processor, state in modules.globals.fp_ui.items():
-        if state == True and frame_processor not in current_processor_names:
+        if state and frame_processor not in current_processor_names:
             try:
                 frame_processor_module = load_frame_processor_module(frame_processor)
                 FRAME_PROCESSORS_MODULES.append(frame_processor_module)
@@ -71,7 +71,7 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
             except Exception as e:
                  print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
 
-        elif state == False and frame_processor in current_processor_names:
+        elif not state and frame_processor in current_processor_names:
             try:
                 module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
                 if module_to_remove:

modules/processors/frame/face_enhancer.py

@@ -11,7 +11,7 @@ import onnxruntime
 import modules.globals
 import modules.processors.frame.core
 from modules.core import update_status
-from modules.face_analyser import get_one_face, get_many_faces
+from modules.face_analyser import get_many_faces
 from modules.typing import Frame, Face
 from modules.utilities import (
     is_image,

modules/processors/frame/face_enhancer_gpen256.py

@@ -5,7 +5,6 @@ import os
 import threading
 
 import cv2
-import numpy as np
 
 import modules.globals
 import modules.processors.frame.core

modules/processors/frame/face_enhancer_gpen512.py

@@ -5,7 +5,6 @@ import os
 import threading
 
 import cv2
-import numpy as np
 
 import modules.globals
 import modules.processors.frame.core

modules/processors/frame/face_masking.py

@@ -2,7 +2,7 @@ import cv2
 import numpy as np
 from modules.typing import Face, Frame
 import modules.globals
-from modules.gpu_processing import gpu_gaussian_blur, gpu_resize, gpu_cvt_color
+from modules.gpu_processing import gpu_gaussian_blur, gpu_resize
 
 def apply_color_transfer(source, target):
     """

modules/processors/frame/face_swapper.py

@@ -16,7 +16,7 @@ from modules.utilities import (
     is_video,
 )
 from modules.cluster_analysis import find_closest_centroid
-from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize, gpu_cvt_color
+from modules.gpu_processing import gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted, gpu_resize
 import os
 from collections import deque
 import time
@@ -680,7 +680,8 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
                 continue
 
             face_region = processed_frame[y1:y2, x1:x2]
-            if face_region.size == 0: continue
+            if face_region.size == 0:
+                continue
 
             # Apply sharpening (GPU-accelerated when CUDA OpenCV is available)
             try:
@@ -815,9 +816,11 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
             else: # Single face or specific mapping
                  for map_data in source_target_map:
                     source_info = map_data.get("source", {})
-                    if not source_info: continue # Skip if no source info
+                    if not source_info:
+                        continue # Skip if no source info
                     source_face = source_info.get("face")
-                    if not source_face: continue # Skip if no source defined for this map entry
+                    if not source_face:
+                        continue # Skip if no source defined for this map entry
 
                     if is_image(modules.globals.target_path):
                         target_info = map_data.get("target", {})
@@ -854,7 +857,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                      if len(detected_faces) <= len(target_embeddings):
                           # More targets defined than detected - match each detected face
                           for detected_face in detected_faces:
-                              if detected_face.normed_embedding is None: continue
+                              if detected_face.normed_embedding is None:
+                                  continue
                               closest_idx, _ = find_closest_centroid(target_embeddings, detected_face.normed_embedding)
                               if 0 <= closest_idx < len(source_faces):
                                   source_target_pairs.append((source_faces[closest_idx], detected_face))
@@ -862,7 +866,8 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                           # More faces detected than targets defined - match each target embedding to closest detected face
                           detected_embeddings = [f.normed_embedding for f in detected_faces if f.normed_embedding is not None]
                           detected_faces_with_embedding = [f for f in detected_faces if f.normed_embedding is not None]
-                          if not detected_embeddings: return processed_frame # No embeddings to match
+                          if not detected_embeddings:
+                              return processed_frame # No embeddings to match
 
                           for i, target_embedding in enumerate(target_embeddings):
                               if 0 <= i < len(source_faces): # Ensure source face exists for this embedding
@@ -936,7 +941,7 @@ def process_frames(
 
     # --- Stop processing entirely if in Simple Mode and source face is invalid ---
     if not use_v2 and source_face is None:
-        update_status(f"Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
+        update_status("Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
         if progress:
             # Ensure the progress bar completes if it was started
             remaining_updates = total_frames - progress.n if hasattr(progress, 'n') else total_frames
@@ -955,11 +960,13 @@ def process_frames(
             temp_frame = cv2.imread(temp_frame_path)
             if temp_frame is None:
                 print(f"{NAME}: Error: Could not read frame: {temp_frame_path}, skipping.")
-                if progress: progress.update(1)
+                if progress:
+                    progress.update(1)
                 continue # Skip this frame if read fails
         except Exception as read_e:
             print(f"{NAME}: Error reading frame {temp_frame_path}: {read_e}, skipping.")
-            if progress: progress.update(1)
+            if progress:
+                progress.update(1)
             continue
 
         # Select processing function and execute
@@ -1496,7 +1503,8 @@ def apply_color_transfer(source, target):
             if len(source.shape) == 2: # Grayscale
                 source = cv2.cvtColor(source, cv2.COLOR_GRAY2BGR)
             source = np.clip(source, 0, 255).astype(np.uint8)
-            if len(source.shape)!= 3 or source.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(source.shape) != 3 or source.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
             return source
     if len(target.shape) != 3 or target.shape[2] != 3 or target.dtype != np.uint8:
@@ -1505,7 +1513,8 @@ def apply_color_transfer(source, target):
             if len(target.shape) == 2: # Grayscale
                 target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
             target = np.clip(target, 0, 255).astype(np.uint8)
-            if len(target.shape)!= 3 or target.shape[2]!= 3: raise ValueError("Conversion failed")
+            if len(target.shape) != 3 or target.shape[2] != 3:
+                raise ValueError("Conversion failed")
         except Exception:
              return source # Return original source if target invalid