from io import BytesIO import requests import gradio as gr import requests import torch from tqdm import tqdm from PIL import Image, ImageOps from diffusers import StableDiffusionInpaintPipeline from torchvision.transforms import ToPILImage from utils import preprocess, prepare_mask_and_masked_image, recover_image, resize_and_crop gr.close_all() topil = ToPILImage() pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, safety_checker=None, ) pipe_inpaint = pipe_inpaint.to("cuda") ## Good params for editing that we used all over the paper --> decent quality and speed GUIDANCE_SCALE = 7.5 NUM_INFERENCE_STEPS = 100 DEFAULT_SEED = 1234 def pgd(X, targets, model, criterion, eps=0.1, step_size=0.015, iters=40, clamp_min=0, clamp_max=1, mask=None): X_adv = X.clone().detach() + (torch.rand(*X.shape)*2*eps-eps).cuda() pbar = tqdm(range(iters)) for i in pbar: actual_step_size = step_size - (step_size - step_size / 100) / iters * i X_adv.requires_grad_(True) loss = (model(X_adv).latent_dist.mean - targets).norm() pbar.set_description(f"Loss {loss.item():.5f} | step size: {actual_step_size:.4}") grad, = torch.autograd.grad(loss, [X_adv]) X_adv = X_adv - grad.detach().sign() * actual_step_size X_adv = torch.minimum(torch.maximum(X_adv, X - eps), X + eps) X_adv.data = torch.clamp(X_adv, min=clamp_min, max=clamp_max) X_adv.grad = None if mask is not None: X_adv.data *= mask return X_adv def get_target(): target_url = 'https://www.rtings.com/images/test-materials/2015/204_Gray_Uniformity.png' response = requests.get(target_url) target_image = Image.open(BytesIO(response.content)).convert("RGB") target_image = target_image.resize((512, 512)) return target_image def immunize_fn(init_image, mask_image): with torch.autocast('cuda'): mask, X = prepare_mask_and_masked_image(init_image, mask_image) X = X.half().cuda() mask = mask.half().cuda() targets = pipe_inpaint.vae.encode(preprocess(get_target()).half().cuda()).latent_dist.mean adv_X = pgd(X, targets = targets, model=pipe_inpaint.vae.encode, criterion=torch.nn.MSELoss(), clamp_min=-1, clamp_max=1, eps=0.12, step_size=0.01, iters=200, mask=1-mask ) adv_X = (adv_X / 2 + 0.5).clamp(0, 1) adv_image = topil(adv_X[0]).convert("RGB") adv_image = recover_image(adv_image, init_image, mask_image, background=True) return adv_image def run(image, prompt, seed, guidance_scale, num_inference_steps, immunize=False): if seed == '': seed = DEFAULT_SEED else: seed = int(seed) torch.manual_seed(seed) init_image = Image.fromarray(image['image']) init_image = resize_and_crop(init_image, (512,512)) mask_image = ImageOps.invert(Image.fromarray(image['mask']).convert('RGB')) mask_image = resize_and_crop(mask_image, init_image.size) if immunize: immunized_image = immunize_fn(init_image, mask_image) image_edited = pipe_inpaint(prompt=prompt, image=init_image if not immunize else immunized_image, mask_image=mask_image, height = init_image.size[0], width = init_image.size[1], eta=1, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, ).images[0] image_edited = recover_image(image_edited, init_image, mask_image) if immunize: return [(immunized_image, 'Immunized Image'), (image_edited, 'Edited After Immunization')] else: return [(image_edited, 'Edited Image (Without Immunization)')] description='''Official demo of our paper:
**Raising the Cost of Malicious AI-Powered Image Editing**
*[Hadi Salman](https://twitter.com/hadisalmanX), [Alaa Khaddaj](https://twitter.com/Alaa_Khaddaj), [Guillaume Leclerc](https://twitter.com/gpoleclerc), [Andrew Ilyas](https://twitter.com/andrew_ilyas), [Aleksander Madry](https://twitter.com/aleks_madry)*
MIT   [Paper](https://arxiv.org/abs/2302.06588)   [Blog post](https://gradientscience.org/photoguard/)   [![](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/MadryLab/photoguard)
Below you can test our (encoder attack) immunization method for making images resistant to manipulation by Stable Diffusion. This immunization process forces the model to perform unrealistic edits. **See Section 5 in our paper for a discussion of the intended use cases for (as well as limitations of) this tool.**
''' examples_list = [ ['./images/hadi_and_trevor.jpg', 'man attending a wedding', '329357', GUIDANCE_SCALE, NUM_INFERENCE_STEPS], ['./images/trevor_2.jpg', 'two men in prison', '329357', GUIDANCE_SCALE, NUM_INFERENCE_STEPS], ['./images/elon_2.jpg', 'man in a metro station', '214213', GUIDANCE_SCALE, NUM_INFERENCE_STEPS], ] with gr.Blocks() as demo: gr.HTML(value="""

Interactive Demo: Raising the Cost of Malicious AI-Powered Image Editing

""") gr.Markdown(description) with gr.Accordion(label='How to use (step by step):', open=False): gr.Markdown(''' *First, let's edit your image:* + Upload an image (or select from the examples below) + Use the brush to mask the parts of the image you want to keep unedited (e.g., faces of people) + Add a prompt to guide the edit (see examples below) + Play with the seed and click submit until you get a realistic edit that you are happy with (we provided good example seeds for you below) *Now, let's immunize your image and try again:* + Click on the "Immunize" button, then submit. + You will get an immunized version of the image (which should look essentially identical to the original one) as well as its edited version (which should now look rather unrealistic) ''') with gr.Accordion(label='Example (video):', open=False): gr.HTML('''
''' ) with gr.Row(): with gr.Column(): imgmask = gr.ImageMask(label='Drawing tool to mask regions you want to keep, e.g. faces') prompt = gr.Textbox(label='Prompt', placeholder='A photo of a man in a wedding') seed = gr.Textbox(label='Seed (change to get different edits)', placeholder=str(DEFAULT_SEED), visible=True) with gr.Accordion("Advanced options (to improve quality of edits)", open=False): scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=25.0, value=GUIDANCE_SCALE, step=0.1) num_steps = gr.Slider(label="Number of inference steps (higher better, but slower)", minimum=10, maximum=250, value=NUM_INFERENCE_STEPS, step=5) immunize = gr.Checkbox(label='Immunize', value=False) b1 = gr.Button('Submit') with gr.Column(): genimages = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1,2], height="auto") duplicate = gr.HTML("""

For faster inference without waiting in queue, run this demo locally (instruction in our Github repo), or duplicate this space and upgrade to GPU in settings.
Duplicate Space

""") b1.click(run, [imgmask, prompt, seed, scale, num_steps, immunize], [genimages]) examples = gr.Examples(examples=examples_list,inputs = [imgmask, prompt, seed, scale, num_steps, immunize], outputs=[genimages], cache_examples=False, fn=run) demo.launch() # demo.launch(server_name='0.0.0.0', share=False, server_port=7860, inline=False)