62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
# Use a pipeline as a high-level helper
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from PIL import Image
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from transformers import pipeline
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import torchvision.transforms as T
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import torch
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import numpy
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def tensor2pil(image):
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return Image.fromarray(numpy.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(numpy.uint8))
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def pil2tensor(image):
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return torch.from_numpy(numpy.array(image).astype(numpy.float32) / 255.0).unsqueeze(0)
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class NSFWScore:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "run"
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CATEGORY = "NSFWScore"
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def run(self, image):
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transform = T.ToPILImage()
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cuda = torch.cuda.is_available()
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classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device="cuda" if cuda else "cpu")
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# Process batch of images
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scores = []
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for single_image in image:
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result = classifier(transform(single_image.permute(2, 0, 1)))
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nsfw_score = 0.0
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for r in result:
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if r["label"] == "nsfw":
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nsfw_score = r["score"]
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scores.append(nsfw_score)
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return (scores,)
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"NSFWScore": NSFWScore
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"NSFWScore": "NSFW Score"
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}
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