Upload 2 files

about.py

@@ -1,90 +1,90 @@
-from dataclasses import dataclass
-from enum import Enum
-
-@dataclass
-class Task:
- benchmark: str
- metric: str
- col_name: str
-
-
-# Select your tasks here
-# ---------------------------------------------------
-class Tasks(Enum):
- # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
- task0 = Task("anli_r1", "acc", "ANLI")
- task1 = Task("logiqa", "acc_norm", "LogiQA")
-
-NUM_FEWSHOT = 0 # Change with your few shot
-# ---------------------------------------------------
-
-
-
-
-# Your leaderboard name
-TITLE = """<h1 align="center" id="space-title"> Demo of UnlearnDiffAtk</h1>"""
-
-# subtitle
-SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for diffusion models</h1>"""
-
-# What does your leaderboard evaluate?
-INTRODUCTION_TEXT = """
-UnlearnDiffAtk is an effective and efficient adversarial prompt generation approach for unlearned diffusion models(DMs). For more details,
-please refer to the [benchmark of UnlearnDiffAtk](https://huggingface.co/spaces/xinchen9/UnlearnDiffAtk-Benchmark), 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).\\
-The prompts were validated by us for undesirable concepts: ([Church](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/church.csv),
-[Garbage Truck](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/garbage_truck.csv),
-[Parachute](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/parachute.csv),
-style ([Van Gogh](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/vangogh.csv)),
-and objects ([Nudity](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/nudity.csv)).
-
-"""
-
-# Which evaluations are you running? how can people reproduce what you have?
-LLM_BENCHMARKS_TEXT = f"""
-## How it works
-
-## Reproducibility
-To reproduce our results, here is the commands you can run:
-
-"""
-
-EVALUATION_QUEUE_TEXT = """
-## Some good practices before submitting a model
-
-### 1) Make sure you can load your model and tokenizer using AutoClasses:
-```python
-from transformers import AutoConfig, AutoModel, AutoTokenizer
-config = AutoConfig.from_pretrained("your model name", revision=revision)
-model = AutoModel.from_pretrained("your model name", revision=revision)
-tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
-```
-If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
-
-Note: make sure your model is public!
-Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
-
-### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
-It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
-
-### 3) Make sure your model has an open license!
-This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
-
-### 4) Fill up your model card
-When we add extra information about models to the leaderboard, it will be automatically taken from the model card
-
-## In case of model failure
-If your model is displayed in the `FAILED` category, its execution stopped.
-Make sure you have followed the above steps first.
-If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
-"""
-
-CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
-CITATION_BUTTON_TEXT = r"""
-@article{zhang2023generate,
- title={To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now},
- author={Zhang, Yimeng and Jia, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia},
- journal={arXiv preprint arXiv:2310.11868},
- year={2023}
-}
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+ benchmark: str
+ metric: str
+ col_name: str
+
+
+# Select your tasks here
+# ---------------------------------------------------
+class Tasks(Enum):
+ # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
+ task0 = Task("anli_r1", "acc", "ANLI")
+ task1 = Task("logiqa", "acc_norm", "LogiQA")
+
+NUM_FEWSHOT = 0 # Change with your few shot
+# ---------------------------------------------------
+
+
+
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title"> Demo of UnlearnDiffAtk</h1>"""
+
+# subtitle
+SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for diffusion models</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+UnlearnDiffAtk is an effective and efficient adversarial prompt generation approach for unlearned diffusion models(DMs). For more details,
+please refer to the [benchmark of UnlearnDiffAtk](https://huggingface.co/spaces/xinchen9/UnlearnDiffAtk-Benchmark), 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).\\
+The prompts were validated by us for undesirable concepts: ([Church](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/church.csv),
+[Garbage Truck](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/garbage_truck.csv),
+[Parachute](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/parachute.csv),
+style ([Van Gogh](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/vangogh.csv)),
+and objects ([Nudity](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/e848ddd19df1f86d08e08cc9146f8a2bb126da12/prompts/nudity.csv)).
+
+"""
+
+# Which evaluations are you running? how can people reproduce what you have?
+LLM_BENCHMARKS_TEXT = f"""
+## How it works
+
+## Reproducibility
+To reproduce our results, here is the commands you can run:
+
+"""
+
+EVALUATION_QUEUE_TEXT = """
+## Some good practices before submitting a model
+
+### 1) Make sure you can load your model and tokenizer using AutoClasses:
+```python
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+config = AutoConfig.from_pretrained("your model name", revision=revision)
+model = AutoModel.from_pretrained("your model name", revision=revision)
+tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
+```
+If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
+
+Note: make sure your model is public!
+Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
+
+### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
+It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
+
+### 3) Make sure your model has an open license!
+This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
+
+### 4) Fill up your model card
+When we add extra information about models to the leaderboard, it will be automatically taken from the model card
+
+## In case of model failure
+If your model is displayed in the `FAILED` category, its execution stopped.
+Make sure you have followed the above steps first.
+If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""
+@article{zhang2023generate,
+ title={To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now},
+ author={Zhang, Yimeng and Jia, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia},
+ journal={arXiv preprint arXiv:2310.11868},
+ year={2023}
+}
 """

app.py

@@ -1,127 +1,143 @@
-
-import gradio as gr
-import os
-import requests
-import json
-import base64
-from io import BytesIO
-from huggingface_hub import login
-from PIL import Image
-
-
-# myip = os.environ["0.0.0.0"]
-# myport = os.environ["80"]
-myip = "34.219.98.113"
-myport=8000
-
-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 process_image_from_binary(img_stream):
- if img_stream is None:
- print("no image binary")
- return
- image_data = base64.b64decode(img_stream)
- image_bytes = BytesIO(image_data)
- img = Image.open(image_bytes)
- 
- return img
-
-def excute_udiff(diffusion_model_id, concept, steps, attack_id):
- print(f"my IP is {myip}, my port is {myport}")
- print(f"my input is diffusion_model_id: {diffusion_model_id}, concept: {concept}, steps: {steps}")
- response = requests.post('http://{}:{}/udiff'.format(myip, myport), 
- json={"diffusion_model_id": diffusion_model_id, "concept": concept, "steps": steps, "attack_id": attack_id},
- timeout=(10, 1200))
- print(f"result: {response}")
- # result = result.text[1:-1]
- prompt1 = ""
- prompt2 = ""
- img1 = None
- img2 = None
- if response.status_code == 200:
- response_json = response.json()
- print(response_json)
- prompt1 = response_json['input_prompt']
- prompt2 = response_json['output_prompt']
- img1 = process_image_from_binary(response_json['no_attack_img'])
- img2 = process_image_from_binary(response_json['attack_img'])
- else:
- print(f"Request failed with status code {response.status_code}")
- 
- return prompt1, prompt2, img1, img2
-
-
-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.Row():
- drop = gr.Dropdown(["Object-Church", "Object-Parachute", "Object-Garbage_Truck","Style-VanGogh",
- "Nudity"], 
- label="Unlearning undesirable concepts")
- with gr.Column():
- # gr.Markdown("Please upload your model id.")
- drop_model = gr.Dropdown(["ESD", "FMN", "SPM"], 
- label="Unlearned DMs")
- # diffusion_model_T = gr.Textbox(label='diffusion_model_id')
- # concept = gr.Textbox(label='concept')
- # attacker = gr.Textbox(label='attacker')
-
- # start_button = gr.Button("Attack!")
- with gr.Column():
- atk_idx = gr.Textbox(label="attack index")
-
- with gr.Column():
- shown_columns_step = gr.Slider(
- 0, 100, value=40, 
- step=1, label="Attack Steps", info="Choose between 0 and 100",
- interactive=True,)
- with gr.Row() as attack:
- with gr.Column(min_width=512):
- text_input = gr.Textbox(label="Input Prompt")
- 
- orig_img = gr.Image(label="Image Generated by Input Prompt",width=512,show_share_button=False,show_download_button=False)
- with gr.Column():
- start_button = gr.Button("UnlearnDiffAtk!",size='lg')
- with gr.Column(min_width=512):
- text_ouput = gr.Textbox(label="Prompt Genetated by UnlearnDiffAtk")
- result_img = gr.Image(label="Image Gnerated by Prompt of UnlearnDiffAtk",width=512,show_share_button=False,show_download_button=False)
- 
-
- start_button.click(fn=excute_udiff, inputs=[drop_model, drop, shown_columns_step, atk_idx], outputs=[text_input, text_ouput, orig_img, result_img], api_name="udiff")
-
-
+
+import gradio as gr
+import os
+import requests
+import json
+import base64
+from io import BytesIO
+from huggingface_hub import login
+from PIL import Image
+
+
+# myip = os.environ["0.0.0.0"]
+# myport = os.environ["80"]
+myip = "34.219.98.113"
+myport=8000
+
+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 process_image_from_binary(img_stream):
+ if img_stream is None:
+ print("no image binary")
+ return
+ image_data = base64.b64decode(img_stream)
+ image_bytes = BytesIO(image_data)
+ img = Image.open(image_bytes)
+ 
+ return img
+
+def execute_prepare(diffusion_model_id, concept, steps, attack_id):
+ print(f"my IP is {myip}, my port is {myport}")
+ print(f"my input is diffusion_model_id: {diffusion_model_id}, concept: {concept}, steps: {steps}")
+ response = requests.post('http://{}:{}/prepare'.format(myip, myport), 
+ json={"diffusion_model_id": diffusion_model_id, "concept": concept, "steps": steps, "attack_id": attack_id},
+ timeout=(10, 1200))
+ print(f"result: {response}")
+ # result = result.text[1:-1]
+ prompt = ""
+ img = None
+ if response.status_code == 200:
+ response_json = response.json()
+ print(response_json)
+ prompt = response_json['input_prompt']
+ img = process_image_from_binary(response_json['no_attack_img'])
+ else:
+ print(f"Request failed with status code {response.status_code}")
+ 
+ return prompt, img
+
+def execute_udiff(diffusion_model_id, concept, steps, attack_id):
+ print(f"my IP is {myip}, my port is {myport}")
+ print(f"my input is diffusion_model_id: {diffusion_model_id}, concept: {concept}, steps: {steps}")
+ response = requests.post('http://{}:{}/udiff'.format(myip, myport), 
+ json={"diffusion_model_id": diffusion_model_id, "concept": concept, "steps": steps, "attack_id": attack_id},
+ timeout=(10, 1200))
+ print(f"result: {response}")
+ # result = result.text[1:-1]
+ prompt = ""
+ img = None
+ if response.status_code == 200:
+ response_json = response.json()
+ print(response_json)
+ prompt = response_json['output_prompt']
+ img = process_image_from_binary(response_json['attack_img'])
+ else:
+ print(f"Request failed with status code {response.status_code}")
+ 
+ return prompt, img
+
+
+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.Row():
+ drop = gr.Dropdown(["Object-Church", "Object-Parachute", "Object-Garbage_Truck","Style-VanGogh",
+ "Nudity"], 
+ label="Unlearning undesirable concepts")
+ with gr.Column():
+ # gr.Markdown("Please upload your model id.")
+ drop_model = gr.Dropdown(["ESD", "FMN", "SPM"], 
+ label="Unlearned DMs")
+ # diffusion_model_T = gr.Textbox(label='diffusion_model_id')
+ # concept = gr.Textbox(label='concept')
+ # attacker = gr.Textbox(label='attacker')
+
+ # start_button = gr.Button("Attack!")
+ with gr.Column():
+ atk_idx = gr.Textbox(label="attack index")
+
+ with gr.Column():
+ shown_columns_step = gr.Slider(
+ 0, 100, value=40, 
+ step=1, label="Attack Steps", info="Choose between 0 and 100",
+ interactive=True,)
+ with gr.Row() as attack:
+ with gr.Column(min_width=512):
+ start_button = gr.Button("Attack prepare!",size='lg')
+ text_input = gr.Textbox(label="Input Prompt")
+ 
+ orig_img = gr.Image(label="Image Generated by Input Prompt",width=512,show_share_button=False,show_download_button=False)
+ with gr.Column():
+ attack_button = gr.Button("UnlearnDiffAtk!",size='lg')
+ text_ouput = gr.Textbox(label="Prompt Genetated by UnlearnDiffAtk")
+ result_img = gr.Image(label="Image Gnerated by Prompt of UnlearnDiffAtk",width=512,show_share_button=False,show_download_button=False)
+ 
+ start_button.click(fn=execute_prepare, inputs=[drop_model, drop, shown_columns_step, atk_idx], outputs=[text_input, orig_img], api_name="prepare")
+ attack_button.click(fn=execute_udiff, inputs=[drop_model, drop, shown_columns_step, atk_idx], outputs=[text_ouput, result_img], api_name="udiff")
+
+
 demo.queue().launch(server_name='0.0.0.0')