hadisalman commited on
Commit
b04b973
1 Parent(s): adc362f

Small fixes

Browse files
Files changed (1) hide show
  1. app.py +9 -5
app.py CHANGED
@@ -23,6 +23,7 @@ pipe_inpaint = pipe_inpaint.to("cuda")
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  ## Good params for editing that we used all over the paper --> decent quality and speed
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  GUIDANCE_SCALE = 7.5
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  NUM_INFERENCE_STEPS = 100
 
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  def pgd(X, targets, model, criterion, eps=0.1, step_size=0.015, iters=40, clamp_min=0, clamp_max=1, mask=None):
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  X_adv = X.clone().detach() + (torch.rand(*X.shape)*2*eps-eps).cuda()
@@ -80,7 +81,10 @@ def immunize_fn(init_image, mask_image):
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  return adv_image
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  def run(image, prompt, seed, immunize=False):
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- seed = int(seed)
 
 
 
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  torch.manual_seed(seed)
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  init_image = Image.fromarray(image['image'])
@@ -111,9 +115,9 @@ def run(image, prompt, seed, immunize=False):
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  demo = gr.Interface(fn=run,
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  inputs=[
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- gr.ImageMask(label='Input Image'),
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  gr.Textbox(label='Prompt', placeholder='A photo of a man in a wedding'),
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- gr.Textbox(label='Seed', placeholder='1234'),
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  gr.Checkbox(label='Immunize', value=False),
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  ],
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  cache_examples=False,
@@ -133,7 +137,7 @@ demo = gr.Interface(fn=run,
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  description='''<u>Official</u> demo of our paper: <br>
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  **Raising the Cost of Malicious AI-Powered Image Editing** <br>
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  *Hadi Salman\*, Alaa Khaddaj\*, Guillaume Leclerc\*, Andrew Ilyas, Aleksander Madry* <br>
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- [Paper](https://arxiv.org/abs/2302.06588)
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  &nbsp;&nbsp;[Blog post](https://gradientscience.org/photoguard/)
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  &nbsp;&nbsp;[![](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/MadryLab/photoguard)
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  <br />
@@ -142,7 +146,7 @@ demo = gr.Interface(fn=run,
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  <br />
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  **Demo steps:**
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  + Upload an image (or select from the below examples!)
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- + Mask the parts of the image you want to maintain unedited (e.g., faces of people)
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  + Add a prompt to edit the image accordingly (see examples below)
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  + Play with the seed and click submit until you get a realistic edit that you are happy with (we have good seeds for you below)
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  ## Good params for editing that we used all over the paper --> decent quality and speed
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  GUIDANCE_SCALE = 7.5
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  NUM_INFERENCE_STEPS = 100
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+ DEFAULT_SEED = 1234
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  def pgd(X, targets, model, criterion, eps=0.1, step_size=0.015, iters=40, clamp_min=0, clamp_max=1, mask=None):
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  X_adv = X.clone().detach() + (torch.rand(*X.shape)*2*eps-eps).cuda()
 
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  return adv_image
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  def run(image, prompt, seed, immunize=False):
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+ if seed == '':
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+ seed = DEFAULT_SEED
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+ else:
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+ seed = int(seed)
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  torch.manual_seed(seed)
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  init_image = Image.fromarray(image['image'])
 
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  demo = gr.Interface(fn=run,
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  inputs=[
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+ gr.ImageMask(label='Input Image (Use drawing tool to mask the regions you want to keep, e.g. faces)'),
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  gr.Textbox(label='Prompt', placeholder='A photo of a man in a wedding'),
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+ gr.Textbox(label='Seed (Change to get different edits!)', placeholder=str(DEFAULT_SEED), visible=True),
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  gr.Checkbox(label='Immunize', value=False),
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  ],
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  cache_examples=False,
 
137
  description='''<u>Official</u> demo of our paper: <br>
138
  **Raising the Cost of Malicious AI-Powered Image Editing** <br>
139
  *Hadi Salman\*, Alaa Khaddaj\*, Guillaume Leclerc\*, Andrew Ilyas, Aleksander Madry* <br>
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+ MIT &nbsp;&nbsp;[Paper](https://arxiv.org/abs/2302.06588)
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  &nbsp;&nbsp;[Blog post](https://gradientscience.org/photoguard/)
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  &nbsp;&nbsp;[![](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/MadryLab/photoguard)
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  <br />
 
146
  <br />
147
  **Demo steps:**
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  + Upload an image (or select from the below examples!)
149
+ + Mask (with the drawing tool) the parts of the image you want to maintain unedited (e.g., faces of people)
150
  + Add a prompt to edit the image accordingly (see examples below)
151
  + Play with the seed and click submit until you get a realistic edit that you are happy with (we have good seeds for you below)
152