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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
from functools import lru_cache
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import numpy as np
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import torch
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from diffusers.pipelines.stable_diffusion import safety_checker as safety_checker_mod
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from diffusers.pipelines.stable_diffusion.safety_checker import (
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StableDiffusionSafetyChecker,
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)
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from transformers import AutoFeatureExtractor
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@lru_cache()
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def safety_models():
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safety_model_id = "CompVis/stable-diffusion-safety-checker"
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monkeypatch_safety_cosine_distance()
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safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
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safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
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return safety_feature_extractor, safety_checker
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@lru_cache()
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def monkeypatch_safety_cosine_distance():
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orig_cosine_distance = safety_checker_mod.cosine_distance
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def cosine_distance_float32(image_embeds, text_embeds):
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"""
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In some environments we need to distance to be in float32
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but it was coming as BFloat16
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"""
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return orig_cosine_distance(image_embeds, text_embeds).to(torch.float32)
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safety_checker_mod.cosine_distance = cosine_distance_float32
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def is_nsfw(img):
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safety_feature_extractor, safety_checker = safety_models()
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safety_checker_input = safety_feature_extractor([img], return_tensors="pt")
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clip_input = safety_checker_input.pixel_values
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_, has_nsfw_concept = safety_checker(
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images=[np.empty((2, 2))], clip_input=clip_input
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)
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return has_nsfw_concept[0]
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