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RUF013 update

pull/4706/head
awsr 2026-03-23 22:57:35 -07:00
parent de86927c1b
commit 3e228afa78
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7 changed files with 63 additions and 52 deletions
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16
modules/caption/deepbooru.py
View File

@ -53,13 +53,13 @@ class DeepDanbooru:
def tag_multi(
self,
pil_image,
general_threshold: float = None,
include_rating: bool = None,
exclude_tags: str = None,
max_tags: int = None,
sort_alpha: bool = None,
use_spaces: bool = None,
escape_brackets: bool = None,
general_threshold: float | None = None,
include_rating: bool | None = None,
exclude_tags: str | None = None,
max_tags: int | None = None,
sort_alpha: bool | None = None,
use_spaces: bool | None = None,
escape_brackets: bool | None = None,
):
"""Run inference and return formatted tag string.
@ -134,7 +134,7 @@ def get_models() -> list:
return ["DeepBooru"]
def load_model(model_name: str = None) -> bool: # pylint: disable=unused-argument
def load_model(model_name: str = "") -> bool: # pylint: disable=unused-argument
"""Load the DeepBooru model."""
try:
model.load()

8
modules/caption/joycaption.py
View File

@ -53,12 +53,12 @@ class JoyOptions:
return f'repo="{self.repo}" temp={self.temp} top_k={self.top_k} top_p={self.top_p} sample={self.sample} tokens={self.max_new_tokens}'
processor: AutoProcessor = None
llava_model: LlavaForConditionalGeneration = None
processor: AutoProcessor | None = None
llava_model: LlavaForConditionalGeneration | None = None
opts = JoyOptions()
def load(repo: str = None):
def load(repo: str | None = None):
"""Load JoyCaption model."""
global llava_model, processor # pylint: disable=global-statement
repo = repo or opts.repo
@ -93,7 +93,7 @@ def unload():
log.debug('JoyCaption unload: no model loaded')
def predict(question: str, image, vqa_model: str = None) -> str:
def predict(question: str, image, vqa_model: str | None = None) -> str:
opts.max_new_tokens = shared.opts.caption_vlm_max_length
load(vqa_model)

10
modules/caption/moondream3.py
View File

@ -87,7 +87,7 @@ def _image_hash(image: Image.Image) -> str:
return h.hexdigest()
def encode_image(image: Image.Image, cache_key: str = None):
def encode_image(image: Image.Image, cache_key: str | None = None):
"""
Encode image for reuse across multiple queries.
@ -119,7 +119,7 @@ def encode_image(image: Image.Image, cache_key: str = None):
def query(image: Image.Image, question: str, repo: str, stream: bool = False,
temperature: float = None, top_p: float = None, max_tokens: int = None,
temperature: float | None = None, top_p: float | None = None, max_tokens: int | None = None,
use_cache: bool = False, reasoning: bool = True):
"""
Visual question answering with optional streaming.
@ -180,7 +180,7 @@ def query(image: Image.Image, question: str, repo: str, stream: bool = False,
def caption(image: Image.Image, repo: str, length: str = 'normal', stream: bool = False,
temperature: float = None, top_p: float = None, max_tokens: int = None):
temperature: float | None = None, top_p: float | None = None, max_tokens: int | None = None):
"""
Generate image captions at different lengths.
@ -290,8 +290,8 @@ def detect(image: Image.Image, object_name: str, repo: str, max_objects: int = 1
return detections
def predict(question: str, image: Image.Image, repo: str, model_name: str = None, thinking_mode: bool = False,
mode: str = None, stream: bool = False, use_cache: bool = False, **kwargs):
def predict(question: str, image: Image.Image, repo: str, model_name: str | None = None, thinking_mode: bool = False,
mode: str | None = None, stream: bool = False, use_cache: bool = False, **kwargs):
"""
Main entry point for Moondream 3 VQA - auto-detects mode from question.

2
modules/caption/tagger.py
View File

@ -65,7 +65,7 @@ def unload_model():
waifudiffusion.unload_model()
def tag(image, model_name: str = None, **kwargs) -> str:
def tag(image, model_name: str | None = None, **kwargs) -> str:
"""Unified tagging - dispatch to correct backend.
Args:

49
modules/caption/vqa.py
View File

@ -50,7 +50,7 @@ def get_prompts_for_model(model_name: str) -> list:
return vlm_prompts_common
def get_internal_prompt(friendly_name: str, user_prompt: str = None) -> str:
def get_internal_prompt(friendly_name: str, user_prompt: str | None = None) -> str:
"""Convert friendly prompt name to internal token/command."""
internal = vlm_prompt_mapping.get(friendly_name, friendly_name)
@ -350,7 +350,7 @@ class VQA:
self.processor = None
devices.torch_gc(force=True, reason='vqa model switch')
def load(self, model_name: str = None):
def load(self, model_name: str | None = None):
"""Load VLM model into memory for the specified model name."""
model_name = model_name or shared.opts.caption_vlm_model
if not model_name:
@ -444,7 +444,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _fastvlm(self, question: str, image: Image.Image, repo: str, model_name: str = None):
def _fastvlm(self, question: str, image: Image.Image, repo: str, model_name: str | None = None):
debug(f'VQA caption: handler=fastvlm model_name="{model_name}" repo="{repo}" question="{question}" image_size={image.size if image else None}')
self._load_fastvlm(repo)
move_aux_to_gpu('vqa')
@ -521,7 +521,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _qwen(self, question: str, image: Image.Image, repo: str, system_prompt: str = None, model_name: str = None, prefill: str = None, thinking_mode: bool = False):
def _qwen(self, question: str, image: Image.Image, repo: str, system_prompt: str | None = None, model_name: str | None = None, prefill: str | None = None, thinking_mode: bool = False):
self._load_qwen(repo)
move_aux_to_gpu('vqa')
# Get model class name for logging
@ -644,7 +644,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _gemma(self, question: str, image: Image.Image, repo: str, system_prompt: str = None, model_name: str = None, prefill: str = None, thinking_mode: bool = False):
def _gemma(self, question: str, image: Image.Image, repo: str, system_prompt: str | None = None, model_name: str | None = None, prefill: str | None = None, thinking_mode: bool = False):
self._load_gemma(repo)
move_aux_to_gpu('vqa')
# Get model class name for logging
@ -757,7 +757,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _mistral(self, question: str, image: Image.Image, repo: str, system_prompt: str = None, model_name: str = None, prefill: str = None, thinking_mode: bool = False):
def _mistral(self, question: str, image: Image.Image, repo: str, system_prompt: str | None = None, model_name: str | None = None, prefill: str | None = None, thinking_mode: bool = False):
self._load_mistral(repo)
move_aux_to_gpu('vqa')
cls_name = self.model.__class__.__name__
@ -828,7 +828,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _paligemma(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _paligemma(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_paligemma(repo)
move_aux_to_gpu('vqa')
question = question.replace('<', '').replace('>', '').replace('_', ' ')
@ -870,7 +870,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _ovis(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _ovis(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
try:
pass # pylint: disable=unused-import
except Exception:
@ -925,7 +925,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _smol(self, question: str, image: Image.Image, repo: str, system_prompt: str = None, model_name: str = None, prefill: str = None, thinking_mode: bool = False):
def _smol(self, question: str, image: Image.Image, repo: str, system_prompt: str | None = None, model_name: str | None = None, prefill: str | None = None, thinking_mode: bool = False):
self._load_smol(repo)
move_aux_to_gpu('vqa')
# Get model class name for logging
@ -1019,7 +1019,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _git(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _git(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_git(repo)
move_aux_to_gpu('vqa')
pixel_values = self.processor(images=image, return_tensors="pt").pixel_values
@ -1053,7 +1053,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _blip(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _blip(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_blip(repo)
move_aux_to_gpu('vqa')
inputs = self.processor(image, question, return_tensors="pt")
@ -1081,7 +1081,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _vilt(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _vilt(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_vilt(repo)
move_aux_to_gpu('vqa')
inputs = self.processor(image, question, return_tensors="pt")
@ -1111,7 +1111,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _pix(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _pix(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_pix(repo)
move_aux_to_gpu('vqa')
if len(question) > 0:
@ -1144,7 +1144,7 @@ class VQA:
register_aux('vqa', self.model)
devices.torch_gc()
def _moondream(self, question: str, image: Image.Image, repo: str, model_name: str = None, thinking_mode: bool = False):
def _moondream(self, question: str, image: Image.Image, repo: str, model_name: str | None = None, thinking_mode: bool = False):
debug(f'VQA caption: handler=moondream model_name="{model_name}" repo="{repo}" question="{question}" thinking_mode={thinking_mode}')
self._load_moondream(repo)
move_aux_to_gpu('vqa')
@ -1207,7 +1207,7 @@ class VQA:
# When keep_thinking is False, just use the answer (reasoning is discarded)
return response
def _load_florence(self, repo: str, revision: str = None):
def _load_florence(self, repo: str, revision: str | None = None):
"""Load Florence-2 model and processor."""
_get_imports = transformers.dynamic_module_utils.get_imports
@ -1247,7 +1247,7 @@ class VQA:
self.loaded = cache_key
devices.torch_gc()
def _florence(self, question: str, image: Image.Image, repo: str, revision: str = None, model_name: str = None): # pylint: disable=unused-argument
def _florence(self, question: str, image: Image.Image, repo: str, revision: str | None = None, model_name: str | None = None): # pylint: disable=unused-argument
self._load_florence(repo, revision)
move_aux_to_gpu('vqa')
if question.startswith('<'):
@ -1306,7 +1306,7 @@ class VQA:
self.loaded = repo
devices.torch_gc()
def _sa2(self, question: str, image: Image.Image, repo: str, model_name: str = None): # pylint: disable=unused-argument
def _sa2(self, question: str, image: Image.Image, repo: str, model_name: str | None = None): # pylint: disable=unused-argument
self._load_sa2(repo)
move_aux_to_gpu('vqa')
if question.startswith('<'):
@ -1325,7 +1325,18 @@ class VQA:
response = return_dict["prediction"] # the text format answer
return response
def caption(self, question: str = '', system_prompt: str = None, prompt: str = None, image: Image.Image = None, model_name: str = None, prefill: str = None, thinking_mode: bool = None, quiet: bool = False, generation_kwargs: dict = None) -> str:
def caption(
self,
question: str = "",
system_prompt: str | None = None,
prompt: str | None = None,
image: list[Image.Image] | Image.Image | dict | None = None,
model_name: str | None = None,
prefill: str | None = None,
thinking_mode: bool | None = None,
quiet: bool = False,
generation_kwargs: dict | None = None,
) -> str:
"""
Main entry point for VQA captioning. Returns string answer.
Detection data stored in self.last_detection_data for annotated image creation.
@ -1596,7 +1607,7 @@ def unload_model():
return get_instance().unload()
def load_model(model_name: str = None):
def load_model(model_name: str | None = None):
return get_instance().load(model_name)

6
modules/caption/vqa_detection.py
View File

@ -38,7 +38,7 @@ def parse_points(result) -> list:
return points
def parse_detections(result, label: str, max_objects: int = None) -> list:
def parse_detections(result, label: str, max_objects: int | None = None) -> list:
"""Parse and validate detection bboxes from model result.
Args:
@ -74,7 +74,7 @@ def parse_detections(result, label: str, max_objects: int = None) -> list:
return detections
def parse_florence_detections(response, image_size: tuple = None) -> list:
def parse_florence_detections(response, image_size: tuple | None = None) -> list:
"""Parse Florence-style detection response into standard detection format.
Florence returns detection data in two possible formats:
@ -286,7 +286,7 @@ def calculate_eye_position(face_bbox: dict) -> tuple:
return (eye_x, eye_y)
def draw_bounding_boxes(image: Image.Image, detections: list, points: list = None) -> Image.Image:
def draw_bounding_boxes(image: Image.Image, detections: list, points: list | None = None) -> Image.Image:
"""
Draw bounding boxes and/or points on an image.

24
modules/caption/waifudiffusion.py
View File

@ -50,7 +50,7 @@ class WaifuDiffusionTagger:
self.model_path = None
self.image_size = 448 # Standard for WD models
def load(self, model_name: str = None):
def load(self, model_name: str | None = None):
"""Load the ONNX model and tags from HuggingFace."""
import huggingface_hub
@ -195,15 +195,15 @@ class WaifuDiffusionTagger:
def predict(
self,
image: Image.Image,
general_threshold: float = None,
character_threshold: float = None,
include_rating: bool = None,
exclude_tags: str = None,
max_tags: int = None,
sort_alpha: bool = None,
use_spaces: bool = None,
escape_brackets: bool = None,
image: Image.Image | list[Image.Image] | dict | None,
general_threshold: float | None = None,
character_threshold: float | None = None,
include_rating: bool | None = None,
exclude_tags: str | None = None,
max_tags: int | None = None,
sort_alpha: bool | None = None,
use_spaces: bool | None = None,
escape_brackets: bool | None = None,
) -> str:
"""Run inference and return formatted tag string.
@ -352,7 +352,7 @@ def refresh_models() -> list:
return get_models()
def load_model(model_name: str = None) -> bool:
def load_model(model_name: str | None = None) -> bool:
"""Load the specified WaifuDiffusion model."""
return tagger.load(model_name)
@ -362,7 +362,7 @@ def unload_model():
tagger.unload()
def tag(image: Image.Image, model_name: str = None, **kwargs) -> str:
def tag(image: Image.Image, model_name: str | None = None, **kwargs) -> str:
"""Tag an image using WaifuDiffusion tagger.
Args: