Spaces:
Sleeping
Sleeping
File size: 5,697 Bytes
0b00c74 d2794b1 ca86cf6 8b44d8d ca86cf6 ab4f056 ca86cf6 8b44d8d ca86cf6 c68dc14 ca86cf6 8b44d8d ca86cf6 8b44d8d ca86cf6 d2794b1 ca86cf6 0b00c74 ca86cf6 c1568a8 ca86cf6 4188365 ca86cf6 0b00c74 ca86cf6 0b00c74 ca86cf6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import gradio as gr
import os
import torch
import numpy as np
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
from diffusers import DiffusionPipeline
import torchvision.transforms as transforms
from copy import deepcopy
from collections import OrderedDict
import requests
import json
from PIL import Image, ImageEnhance
import base64
import io
class BZHStableSignatureDemo(object):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16").to("cuda")
# load the patched VQ-VAEs
sd1 = deepcopy(self.pipe.vae.state_dict()) # save initial state dict
self.decoders = decoders = OrderedDict([("no watermark", sd1)])
for name, patched_decoder_ckpt in (
("weak", "models/checkpoint_000.pth.50000"),
("medium", "models/checkpoint_000.pth.150000"),
("strong", "models/checkpoint_000.pth.500000"),
("extreme", "models/checkpoint_000.pth.1500000")):
sd2 = torch.load(patched_decoder_ckpt)['ldm_decoder']
msg = self.pipe.vae.load_state_dict(sd2, strict=False)
print(f"loaded LDM decoder state_dict with message\n{msg}")
print("you should check that the decoder keys are correctly matched")
decoders[name] = sd2
self.decoders = decoders
def generate(self, mode, seed, prompt):
generator = torch.Generator(device=device)
if seed:
torch.manual_seed(seed)
# load the patched VAE decoder
sd = self.decoders[mode]
self.pipe.vae.load_state_dict(sd, strict=False)
output = self.pipe(prompt, num_inference_steps=4, guidance_scale=0.0, output_type="pil")
return output.images[0]
@staticmethod
def pad(img, padding, mode="edge"):
npimg = np.asarray(img)
nppad = ((padding[1], padding[3]), (padding[0], padding[2]), (0,0))
npimg = np.pad(npimg, nppad, mode=mode)
return Image.fromarray(npimg)
def attack_detect(self, img, jpeg_compression, downscale, saturation):
# attack
if downscale != 1:
size = img.size
size = (int(size[0] / downscale), int(size[1] / downscale))
img = img.resize(size, Image.BICUBIC)
converter = ImageEnhance.Color(img)
img = converter.enhance(saturation)
# send to detection API and apply JPEG compression attack
mf = io.BytesIO()
img.save(mf, format='JPEG', quality=jpeg_compression) # includes JPEG attack
b64 = base64.b64encode(mf.getvalue())
data = {
'image': b64.decode('utf8')
}
headers = {}
api_key = os.environ.get('BZH_API_KEY', None)
if api_key:
headers['BZH_API_KEY'] = api_key
response = requests.post('https://bzh.imatag.com/bzh/api/v1.0/detect',
json=data, headers=headers)
response.raise_for_status()
data = response.json()
pvalue = data['p-value']
mf.seek(0)
img0 = Image.open(mf) # reload to show JPEG attack
#result = "resolution = %dx%d p-value = %e" % (img.size[0], img.size[1], pvalue))
result = "No watermark detected."
chances = int(1 / pvalue + 1)
if pvalue < 1e-3:
result = "Weak watermark detected" # (< 1/%d chances of being wrong)" % chances
if pvalue < 1e-9:
result = "Strong watermark detected" # (< 1/%d chances of being wrong)" % chances
return (img0, result)
def interface():
prompt = "sailing ship in storm by Rembrandt"
backend = BZHStableSignatureDemo()
decoders = list(backend.decoders.keys())
with gr.Blocks() as demo:
gr.Markdown("""# Watermarked SDXL-Turbo demo
This demo presents watermarking of images generated via StableDiffusion XL Turbo.
Using the method presented in [StableSignature](https://ai.meta.com/blog/stable-signature-watermarking-generative-ai/),
the VAE decoder of StableDiffusion is fine-tuned to produce images including a specific invisible watermark. We combined
this method with our in-house decoder which operates in zero-bit mode for improved robustness.""")
with gr.Row():
inp = gr.Textbox(label="Prompt", value=prompt)
seed = gr.Number(label="Seed", precision=0)
mode = gr.Dropdown(choices=decoders, label="Watermark strength", value="medium")
with gr.Row():
btn1 = gr.Button("Generate")
with gr.Row():
watermarked_image = gr.ImageEditor(type="pil", width=512, height=512)
with gr.Column():
downscale = gr.Slider(1, 3, value=1, step=0.1, label="Downscale ratio")
saturation = gr.Slider(0, 2, value=1, step=0.1, label="Color saturation")
jpeg_compression = gr.Slider(value=100, step=5, label="JPEG quality")
btn2 = gr.Button("Attack & Detect")
with gr.Row():
attacked_image = gr.Image(type="pil", width=256)
detection_label = gr.Label(label="Detection info")
btn1.click(fn=backend.generate, inputs=[mode, seed, inp], outputs=[watermarked_image], api_name="generate")
btn2.click(fn=backend.attack_detect, inputs=[watermarked_image, jpeg_compression, downscale, saturation], outputs=[attacked_image, detection_label], api_name="detect")
return demo
if __name__ == '__main__':
demo = interface()
demo.launch()
|