refine

src/about.py

@@ -28,22 +28,22 @@ SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adver
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
-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 robustness 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 utility retaining 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). \\
+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 robustness 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 utility retaining 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>: \\
+<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
@@ -51,18 +51,19 @@ EVALUATION_QUEUE_TEXT = """
 
 # 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:\\
-- [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); \\
+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).
 
-We will evaluate your model on UnlearnDiffAtk Benchmark! Open a [github issue](https://github.com/OPTML-Group/Diffusion-MU-Attack/issues) or email us at zhan1853@msu.edu!
+<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!
 """