mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
a23dedd2ee
- Replace legacy model manager service with the v2 manager. - Update invocations to use new load interface. - Fixed many but not all type checking errors in the invocations. Most were unrelated to model manager - Updated routes. All the new routes live under the route tag `model_manager_v2`. To avoid confusion with the old routes, they have the URL prefix `/api/v2/models`. The old routes have been de-registered. - Added a pytest for the loader. - Updated documentation in contributing/MODEL_MANAGER.md
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
"""
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This module defines a singleton object, "safety_checker" that
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wraps the safety_checker model. It respects the global "nsfw_checker"
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configuration variable, that allows the checker to be supressed.
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"""
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import numpy as np
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from PIL import Image
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import invokeai.backend.util.logging as logger
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from invokeai.app.services.config import InvokeAIAppConfig
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from invokeai.backend.util.devices import choose_torch_device
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from invokeai.backend.util.silence_warnings import SilenceWarnings
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config = InvokeAIAppConfig.get_config()
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CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
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class SafetyChecker:
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"""
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Wrapper around SafetyChecker model.
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"""
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safety_checker = None
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feature_extractor = None
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tried_load: bool = False
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@classmethod
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def _load_safety_checker(cls):
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if cls.tried_load:
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return
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if config.nsfw_checker:
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try:
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from transformers import AutoFeatureExtractor
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cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
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cls.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
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logger.info("NSFW checker initialized")
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except Exception as e:
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logger.warning(f"Could not load NSFW checker: {str(e)}")
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else:
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logger.info("NSFW checker loading disabled")
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cls.tried_load = True
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@classmethod
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def safety_checker_available(cls) -> bool:
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cls._load_safety_checker()
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return cls.safety_checker is not None
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@classmethod
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def has_nsfw_concept(cls, image: Image.Image) -> bool:
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if not cls.safety_checker_available():
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return False
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device = choose_torch_device()
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features = cls.feature_extractor([image], return_tensors="pt")
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features.to(device)
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cls.safety_checker.to(device)
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x_image = np.array(image).astype(np.float32) / 255.0
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x_image = x_image[None].transpose(0, 3, 1, 2)
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with SilenceWarnings():
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checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
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return has_nsfw_concept[0]
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