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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
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import torch
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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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from PIL import Image
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import modules.shared as shared
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safety_model_id = "CompVis/stable-diffusion-safety-checker"
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safety_feature_extractor = None
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safety_checker = None
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def numpy_to_pil(images):
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"""
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Convert a numpy image or a batch of images to a PIL image.
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"""
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if images.ndim == 3:
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images = images[None, ...]
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images = (images * 255).round().astype("uint8")
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pil_images = [Image.fromarray(image) for image in images]
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return pil_images
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# check and replace nsfw content
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def check_safety(x_image):
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global safety_feature_extractor, safety_checker
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if safety_feature_extractor is None:
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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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safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
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x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
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return x_checked_image, has_nsfw_concept
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def censor_batch(x):
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x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
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x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
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x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
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return x
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