Update README.md

README.md

@@ -59,13 +59,6 @@ The key to domain-agnostic debiasing lies in the constructive deconstruction fra
 
 By applying domain-agnostic debiasing, Mobius sets a new standard for fairness and impartiality in image generation while maintaining its exceptional ability to adapt to a wide range of styles and domains.
 
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-Mobius outperforms existing state-of-the-art diffusion models in several key areas:
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-Unbiased generation: Mobius generates images have siginifcantly reduced inherent biases commonly found in other diffusion models, setting a new benchmark for fairness and impartiality across all domains.
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-Exceptional generalization: With its unparalleled ability to adapt to an extensive range of styles and domains, Mobius consistently delivers top-quality results, surpassing the limitations of previous models.
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 # Surpassing the State-of-the-Art
 
 Mobius outperforms existing state-of-the-art diffusion models in several key areas:
@@ -79,13 +72,13 @@ Efficient fine-tuning: The Mobius base model serves as a superior foundation for
 
 # Recommendations
 
-- a CFG between 3.5 and 7
+- CFG between 3.5 and 7
 - Requires a CLIP skip of -3
 - Sampler: DPM++ 3M SDE
 - Scheduler: Karras
 - Resolution: 1024x1024
 
-please also consider using these keep words to improve your prompts: best quality, HD, '~*~aesthetic~*~'.
+Please also consider using these keep words to improve your prompts: best quality, HD, '~*~aesthetic~*~'.
 
 
 # Use it with 🧨 diffusers