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import gradio as gr
import gradio as gr
import os
import requests
import json
from huggingface_hub import login
# myip = os.environ["0.0.0.0"]
# myport = os.environ["80"]
myip = "0.0.0.0"
myport=80
is_spaces = True if "SPACE_ID" in os.environ else False
is_shared_ui = False
from css_html_js import custom_css
from about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
def excute_udiff(diffusion_model_id, concept, attacker):
print(f"my IP is {myip}, my port is {myport}")
print(f"my input is diffusion_model_id: {diffusion_model_id}, concept: {concept}, attacker: {attacker}")
result = requests.post('http://{}:{}/udiff'.format(myip, myport), json={"diffusion_model_id": diffusion_model_id, "concept": concept, "attacker": attacker})
result = result.text[1:-1]
return result
css = '''
.instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
.arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
#component-4, #component-3, #component-10{min-height: 0}
.duplicate-button img{margin: 0}
#img_1, #img_2, #img_3, #img_4{height:15rem}
#mdStyle{font-size: 0.7rem}
#titleCenter {text-align:center}
'''
with gr.Blocks(css=custom_css) as demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
# gr.Markdown("# Demo of UnlearnDiffAtk.")
# gr.Markdown("### UnlearnDiffAtk is an effective and efficient adversarial prompt generation approach for unlearned diffusion models(DMs).")
# # gr.Markdown("####For more details, please visit the [project](https://www.optml-group.com/posts/mu_attack),
# # check the [code](https://github.com/OPTML-Group/Diffusion-MU-Attack), and read the [paper](https://arxiv.org/abs/2310.11868).")
# gr.Markdown("### Please notice that the process may take a long time, but the results will be saved. You can try it later if it waits for too long.")
with gr.Row() as udiff:
with gr.Column():
# gr.Markdown("Please upload your model id.")
diffusion_model_id = gr.Textbox(label='diffusion_model_id')
concept = gr.Textbox(label='concept')
attacker = gr.Textbox(label='attacker')
start_button = gr.Button("Attack!")
with gr.Column():
result = gr.Textbox(label="unsafe prompt")
with gr.Column():
gr.Examples(examples=[
["CompVis/stable-diffusion-v1-4", "nudity", "text_grad"]
], inputs=[diffusion_model_id, concept, attacker])
start_button.click(fn=excute_udiff, inputs=[diffusion_model_id, concept, attacker], outputs=result, api_name="udiff")
# demo.queue(default_enabled=False, api_open=False, max_size=5).launch(debug=True, show_api=False)
demo.queue().launch(server_name='0.0.0.0')
# with gr.Blocks() as demo:
# with gr.Row():
# prompt = gr.Textbox(label='Input Prompt')
# with gr.Row():
# shown_columns_1 = gr.CheckboxGroup(
# choices=["Church","Parachute","Tench", "Garbage Truck"],
# label="Undersirable Objects",
# elem_id="column-object",
# interactive=True,
# )
# with gr.Row():
# shown_columns_2 = gr.CheckboxGroup(
# choices=["Van Gogh"],
# label="Undersirable Styles",
# elem_id="column-style",
# interactive=True,
# )
# with gr.Row():
# shown_columns_3 = gr.CheckboxGroup(
# choices=["Violence","Illegal Activity","Nudity"],
# label="Undersirable Concepts (Outputs that may be offensive in nature)",
# elem_id="column-select",
# interactive=True,
# )
# with gr.Row():
# with gr.Column(scale=1, min_width=300):
# img1 = gr.Image("images/cheetah.jpg",label="Unlearning")
# with gr.Column(scale=1, min_width=300):
# img2 = gr.Image("images/cheetah.jpg",label="Attacking")
# with gr.Row():
# # gr.Markdown("Please upload your model id.")
# diffusion_model_id = gr.Textbox(label='diffusion_model_id')
# shown_columns_4 = gr.Slider(
# 1, 100, value=40,
# step=1, label="Attacking Steps", info="Choose between 1 and 100",
# interactive=True,)
# # concept = gr.Textbox(label='concept')
# attacker = gr.Textbox(label='attacker')
# start_button = gr.Button("Attack!")
# demo.launch() |