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import numpy as np
import gradio as gr
from run_gaussian_shading import *
examples = [
"A photo of a cat",
"A pizza with pineapple on it",
"A photo of dog",
]
css = """
#col-container {
margin: 0 auto;
max-width: 700px;
}
"""
MAX_SEED = np.iinfo(np.int32).max
#---------------------------------------------------------------------------------------------------
with gr.Blocks(css=css) as demo:
# ---------------------------------- Add Watermark -----------------------------------------
with gr.Tab("Add watermark"):
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Watermark")
with gr.Accordion("Instruction", open=False):
gr.Markdown("""
# Embedding Watermark
## 1. Generate watermarked image
* Enter your prompt in the text box.
* Click **Run** to generate an image with a random binary watermark.
## 2. Save Image
Click **Download** to save the watermarked image in PNG format
## 3. Advanced Settings
- **Seed**: Generates different images with different seed.
- **Guidance Scale**: Higher values give the model more freedom in image creation.
- **Num Inference Steps**: More steps enhance image detail and quality but increase computational cost.
Source code: [Gaussian Shading](https://github.com/bsmhmmlf/Gaussian-Shading)""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
download_button = gr.DownloadButton(visible=True)
with gr.Row():
result_original = gr.Image(label="Image without watermark", show_label=True)
result = gr.Image(label="Watermarked Image", show_label=True)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1.5,
maximum=10,
step=0.1,
value=7.5,
)
num_inference_steps = gr.Slider(
label="Num inference steps",
minimum=10,
maximum=100,
step=1,
value=50,
)
gr.Examples(examples=examples, inputs=[prompt])
# ---------------------------------- Extract Watermark -----------------------------------------
with gr.Tab("Extract watermark"):
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Watermark Extraction")
with gr.Accordion("Instruction", open=False):
gr.Markdown("""
# Extracting Watermark
**Note**: Ensure you create an image first to add the watermark to the database.
## 1. Upload Image
- Upload the image to the Image box.
- Click the **Extract** button to extract the watermark.
## 2. Advanced Settings
These settings are **optional** and can be used to simulate real-world attacks to erase the watermark:
Click the **Attack** button to generate a distorted image.
* **Seed**: Initialize the random number generator, ensuring reproducibility of the attack
* **Random crop ratio**: determines the proportion of the image to be randomly cropped. A lower ratio means more of the image will be cropped.
* **Random drop ratio**: specifies the fraction of pixels to be randomly dropped. A higher ratio increases the number of dropped pixels.
* **Resize ratio**: determines how much the image will be resized. A lower ratio means the image will be reduced more significantly.
* **Gaussian blur R**: the radius of the Gaussian blur applied to the image. A larger radius results in a more blurred image.
* **Gaussian Std**: standard deviation of the Gaussian distribution used for blurring. A higher value results in a stronger blur effect.
* **Sp prob**: the probability of each pixel being replaced with either black or white noise. A higher probability increases the amount of noise added to the image.
## Output Explanation
- **Output watermark**: The binary bit embedding in the image.
- **Accuracy bit**: The number of binary bits extracted that match the binary watermark in the database.
""")
with gr.Row():
input_image = gr.Image(type='pil')
extract_button = gr.Button("Extract", scale=0, variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
attack_button = gr.Button("Attack!", scale=0, variant="primary")
with gr.Row():
random_crop_ratio = gr.Slider(
label="Random crop ratio",
minimum=0.5,
maximum=1,
step=0.1,
value=1,
)
random_drop_ratio = gr.Slider(
label="Random drop ratio",
minimum=0,
maximum=1,
step=0.1,
value=0,
)
with gr.Row():
resize_ratio = gr.Slider(
label="Resize ratio",
minimum=0.2,
maximum=1,
step=0.1,
value=1,
)
gaussian_blur_r = gr.Slider(
label="Gaussian blur r",
minimum=0,
maximum=1,
step=0.1,
value=0,
)
with gr.Row():
gaussian_std = gr.Slider(
label="Gaussian std",
minimum=0,
maximum=0.01,
step=0.0001,
value=0,
)
sp_prob = gr.Slider(
label="Sp prob",
minimum=0,
maximum=0.1,
step=0.001,
value=0,
)
attack_image = gr.Image(label="Attacked Image")
output = gr.Textbox(label="Output")
with gr.Accordion("More Details", open=False):
result_extract = gr.Textbox(label="Bit watermark")
accuracy_bit = gr.Textbox(label="Accuracy bit")
# ----------------------------- Embedding watermark -------------------------
gr.on(
triggers=[run_button.click, prompt.submit],
fn=generate_with_watermark,
inputs=[
seed,
prompt,
guidance_scale,
num_inference_steps
],
outputs=[result_original, result, download_button],
)
# ----------------------------- Extract watermark -------------------------
gr.on(
triggers=[extract_button.click, attack_button.click],
fn=reverse_watermark,
inputs=[
input_image,
seed,
random_crop_ratio,
random_drop_ratio,
resize_ratio,
gaussian_blur_r,
gaussian_std,
sp_prob,
],
outputs=[output, result_extract, accuracy_bit, attack_image],
)
demo.launch(share=True) |