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| 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(): | |
| print("***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, 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')] | |
| description='''<u>Official</u> demo of our paper: <br> | |
| **Raising the Cost of Malicious AI-Powered Image Editing** <br> | |
| *<a href='https://twitter.com/hadisalmanX' target='_blank'>Hadi Salman</a>, <a href='https://twitter.com/Alaa_Khaddaj' target='_blank'>Alaa Khaddaj</a>, | |
| <a href='https://twitter.com/gpoleclerc' target='_blank'>Guillaume Leclerc</a>, <a href=`https://twitter.com/andrew_ilyas` target='_blank'>Andrew Ilyas</a>, | |
| <a href='https://twitter.com/aleks_madry' target='_blank'>Aleksander Madry</><br> | |
| MIT <a href=`https://arxiv.org/abs/2302.06588' target='_blank'>Paper</a>, <a href='https://gradientscience.org/photoguard/' target='_blank'>Blog post</a> | |
| ''' | |
| with gr.Blocks() as demo: | |
| gr.HTML(value="""<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> | |
| Interactive Demo: Immunize your Photos Against AI-powered Malicious Manipulation </h1><br> | |
| """) | |
| gr.HTML(description) | |
| gr.HTML('''<a href="https://github.com/MadryLab/photoguard"><img src="https://badgen.net/badge/icon/GitHub?icon=github&label" alt="GitHub"></a>''') | |
| gr.HTML('''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.<br> | |
| <b>This is a research project and is not production-ready. See Section 5 in our paper for discussion on its limitations.</b> | |
| ''') | |
| with gr.Accordion(label='Click for demo steps:', open=False): | |
| gr.HTML(''' | |
| - Upload an image (or select from the below examples!) | |
| - Mask (using the drawing tool) the parts of the image you want to maintain unedited (e.g., faces of people) | |
| - Add a prompt to edit the image accordingly (see examples below) | |
| - Play with the seed and click submit until you get a realistic edit that you are happy with (or use default seeds below) | |
| Now let's immunize your image and try again! | |
| - Click on the "immunize" button, then submit. | |
| - You will get the immunized image (which looks identical to the original one) and the edited image, which is now hopefully unrealistic! | |
| ''') | |
| 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) | |
| 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") | |
| b1.click(run, [imgmask, prompt, seed, immunize], [genimages]) | |
| """demo = gr.Interface(fn=run, | |
| inputs=[ | |
| gr.ImageMask(label='Drawing tool to mask regions you want to keep, e.g. faces'), | |
| gr.Textbox(label='Prompt', placeholder='A photo of a man in a wedding'), | |
| gr.Textbox(label='Seed (Change to get different edits!)', placeholder=str(DEFAULT_SEED), visible=True), | |
| gr.Checkbox(label='Immunize', value=False), | |
| ], | |
| cache_examples=False, | |
| outputs=[gr.Gallery( | |
| label="Generated images", | |
| show_label=False, | |
| elem_id="gallery").style(grid=[1,2], height="auto")], | |
| examples=[ | |
| ['./images/hadi_and_trevor.jpg', 'man attending a wedding', '329357'], | |
| ['./images/trevor_2.jpg', 'two men in prison', '329357'], | |
| ['./images/elon_2.jpg', 'man in a metro station', '214213'], | |
| ], | |
| examples_per_page=20, | |
| allow_flagging='never', | |
| title="Interactive Demo: Immunize your Photos Against AI-powered Malicious Manipulation", | |
| description='''<u>Official</u> demo of our paper: <br> | |
| **Raising the Cost of Malicious AI-Powered Image Editing** <br> | |
| *[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)* <br> | |
| MIT [Paper](https://arxiv.org/abs/2302.06588) | |
| [Blog post](https://gradientscience.org/photoguard/) | |
| [](https://github.com/MadryLab/photoguard) | |
| <br /> | |
| 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. | |
| <br /> | |
| **This is a research project and is not production-ready. See Section 5 in our paper for discussion on its limitations.** | |
| <details closed> | |
| <summary>Click for demo steps:</summary> | |
| + Upload an image (or select from the below examples!) | |
| + Mask (using the drawing tool) the parts of the image you want to maintain unedited (e.g., faces of people) | |
| + Add a prompt to edit the image accordingly (see examples below) | |
| + Play with the seed and click submit until you get a realistic edit that you are happy with (or use default seeds below) | |
| Now let's immunize your image and try again! | |
| + Click on the "immunize" button, then submit. | |
| + You will get the immunized image (which looks identical to the original one) and the edited image, which is now hopefully unrealistic! | |
| </details> | |
| ''', | |
| ) | |
| """ | |
| # demo.launch() | |
| demo.launch() #server_name='0.0.0.0', share=False, server_port=7860, inline=False) |