Spaces:
Running
on
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Running
on
Zero
Commit
•
e266395
1
Parent(s):
d1c0879
Native safety checker (#918)
Browse files- Native safety checker (07822f88cb601e5baa8e97c166607b8af5a7e518)
Co-authored-by: Apolinário from multimodal AI art <multimodalart@users.noreply.huggingface.co>
app.py
CHANGED
@@ -23,7 +23,7 @@ import user_history
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from illusion_style import css
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import os
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from transformers import CLIPImageProcessor
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from safety_checker import StableDiffusionSafetyChecker
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BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
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@@ -49,16 +49,16 @@ main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
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).to("cuda")
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# Function to check NSFW images
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def check_nsfw_images(images: list[Image.Image]) -> tuple[list[Image.Image], list[bool]]:
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if SAFETY_CHECKER_ENABLED:
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safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
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has_nsfw_concepts = safety_checker(
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images=[images],
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clip_input=safety_checker_input.pixel_values.to("cuda")
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)
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return images, has_nsfw_concepts
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else:
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return images, [False] * len(images)
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#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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#main_pipe.unet.to(memory_format=torch.channels_last)
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@@ -284,4 +284,4 @@ with gr.Blocks(css=css) as app_with_history:
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app_with_history.queue(max_size=20,api_open=False )
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if __name__ == "__main__":
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app_with_history.launch(max_threads=400)
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from illusion_style import css
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import os
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from transformers import CLIPImageProcessor
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
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).to("cuda")
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# Function to check NSFW images
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#def check_nsfw_images(images: list[Image.Image]) -> tuple[list[Image.Image], list[bool]]:
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# if SAFETY_CHECKER_ENABLED:
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# safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
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# has_nsfw_concepts = safety_checker(
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# images=[images],
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# clip_input=safety_checker_input.pixel_values.to("cuda")
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# )
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# return images, has_nsfw_concepts
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# else:
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# return images, [False] * len(images)
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#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
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#main_pipe.unet.to(memory_format=torch.channels_last)
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app_with_history.queue(max_size=20,api_open=False )
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if __name__ == "__main__":
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app_with_history.launch(max_threads=400)
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