refine (#13)

- refine (a793f274a0f84a0b55f520fae8a0572136a06ee6)


Co-authored-by: Yimeng Zhang <DamonDemon@users.noreply.huggingface.co>

src/about.py

@@ -21,41 +21,52 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 # Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk Benchmark</h1>"""
+TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk: Unlearned Diffusion Model Benchmark</h1>"""
 
 # subtitle
-SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for diffusion models</h2>"""
+SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for unlearned diffusion model evaluations.</h2>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
-This benchmark evaluates the robustness of safety-driven unlearned diffusion models (DMs) 
-(i.e., DMs after unlearning undesirable concepts, styles, or objects) across a variety of tasks. 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).\\
+This benchmark evaluates the <strong>robustness and utility retaining</strong> of safety-driven unlearned diffusion models (DMs) across a variety of tasks. For more details, please visit the [project](https://www.optml-group.com/posts/mu_attack).
+- The <strong>robustness</strong> of unlearned DM is evaluated through our proposed adversarial prompt attack, [UnlearnDiffAtk](https://github.com/OPTML-Group/Diffusion-MU-Attack), which has been accepted to ECCV 2024.
+- The <strong>utility retaining</strong> of unlearned DM is evaluated through FID and CLIP score on the generated images using [10K randomly sampled COCO caption prompts](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/main/prompts/coco_10k.csv). 
+
 Demo of our offensive method: [UnlearnDiffAtk](https://huggingface.co/spaces/Intel/UnlearnDiffAtk)\\
 Demo of our defensive method: [AdvUnlearn](https://huggingface.co/spaces/Intel/AdvUnlearn)
 """
 
+EVALUATION_QUEUE_TEXT = """
+<strong>\[Evaluation Metrics\]</strong>: 
+- Pre-Attack Success Rate (<strong>Pre-ASR</strong>): lower is better;   
+- Post-attack success rate (<strong>Post-ASR</strong>): lower is better; 
+- Fréchet inception distance(<strong>FID</strong>):  evaluate distributional quality of image generations, lower is better; 
+- <strong>CLIP Score</strong>: measure contextual alignment with prompt descriptions, higher is better. 
+
+<strong>\[DM Unlearning Tasks\]</strong>: 
+- NSFW: Nudity
+- Style: Van Gogh
+- Objects: Church, Tench, Parachute, Garbage Truck
+"""
+
 # Which evaluations are you running? how can people reproduce what you have?
 LLM_BENCHMARKS_TEXT = f"""
-For more details of Unlearning Methods used in this benchmarks:\\
-(1) [Erased Stable Diffusion (ESD)](https://github.com/rohitgandikota/erasing);\\
-(2) [Forget-Me-Not (FMN)](https://github.com/SHI-Labs/Forget-Me-Not);\\
-(3) [Ablating Concepts (AC)](https://github.com/nupurkmr9/concept-ablation);\\
-(4) [Unified Concept Editing (UCE)](https://github.com/rohitgandikota/unified-concept-editing);\\
-(5) [concept-SemiPermeable Membrane (SPM)](https://github.com/Con6924/SPM); \\
-(6) [Saliency Unlearning (SalUn)](https://github.com/OPTML-Group/Unlearn-Saliency); \\
-(7) [EraseDiff (ED)](https://github.com/JingWu321/EraseDiff); \\
-(8) [ScissorHands (SH)](https://github.com/JingWu321/Scissorhands).
+For more details of Unlearning Methods used in this benchmarks:
+- [Adversarial Unlearning (AdvUnlearn)](https://github.com/OPTML-Group/AdvUnlearn);
+- [Erased Stable Diffusion (ESD)](https://github.com/rohitgandikota/erasing);
+- [Forget-Me-Not (FMN)](https://github.com/SHI-Labs/Forget-Me-Not);
+- [Ablating Concepts (AC)](https://github.com/nupurkmr9/concept-ablation);
+- [Unified Concept Editing (UCE)](https://github.com/rohitgandikota/unified-concept-editing);
+- [concept-SemiPermeable Membrane (SPM)](https://github.com/Con6924/SPM); 
+- [Saliency Unlearning (SalUn)](https://github.com/OPTML-Group/Unlearn-Saliency); 
+- [EraseDiff (ED)](https://github.com/JingWu321/EraseDiff); 
+- [ScissorHands (SH)](https://github.com/JingWu321/Scissorhands).
 
+<strong>We will evaluate your model on UnlearnDiffAtk Benchmark!</strong> \\
+Open a [github issue](https://github.com/OPTML-Group/Diffusion-MU-Attack/issues) or email us at zhan1853@msu.edu!
 """
 
-EVALUATION_QUEUE_TEXT = """
-Evaluation Metrics: \\
-(1) Pre-attack success rate (pre-ASR), lower is better;   \\
-(2) Post-attack success rate (post-ASR), lower is better; \\
-(3) Fréchet inception distance(FID) of images generated by Unlearned Methods, lower is better; \\
-(3) CLIP (Contrastive Language-Image Pretraining) Score is to measure contextual alignment with prompt descriptions, higher is better.
-"""
+
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
 CITATION_BUTTON_TEXT = r"""