Update README.md

README.md

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-### "Constructive Deconstruction: Domain-Agnostic Debiasing of Diffusion Models"
+### Mobius: Redefining State-of-the-Art in Debiased Diffusion Models
+Mobius, a revolutionary diffusion model, pushes the boundaries of domain-agnostic debiasing and representation realignment. By employing the cutting-edge constructive deconstruction framework, Mobius achieves unrivaled generalization across a vast array of styles and domains, eliminating the need for expensive pretraining from scratch.
 
-## introduction: |
-  Constructive Deconstruction is a groundbreaking approach to debiasing diffusion models used in generative tasks like image synthesis. This method significantly enhances the quality and fidelity of generated images across various domains by removing biases inherited from the training data. Our technique involves overtraining the model to a controlled noisy state, applying nightshading, and using bucketing techniques to realign the model's internal representations.
+#### Surpassing the State-of-the-Art
+Mobius outperforms existing state-of-the-art diffusion models in several key areas:
 
-## methodology:
-  - overtraining_to_controlled_noisy_state: |
-      By purposely overtraining the model until it predictably fails, we create a controlled noisy state. This state helps in identifying and addressing the inherent biases in the model's training data.
-  - nightshading: |
-      Nightshading is repurposed to induce a controlled failure, making it easier to retrain the model. This involves injecting carefully selected data points to stress the model and cause predictable failures.
-  - bucketing: |
-      Using mathematical techniques like slerp (Spherical Linear Interpolation) and bislerp (Bilinear Interpolation), we merge the induced noise back into the model. This step highlights the model's learned knowledge while suppressing biases.
-  - retraining_and_fine_tuning: |
-      The noisy state is retrained on a large, diverse dataset to create a new base model called "Mobius." Initial issues such as grainy details and inconsistent colors are resolved during fine-tuning, resulting in high-quality, unbiased outputs.
-
-## results_and_highlights:
-  increased_diversity_of_outputs: |
-    Training the model on high-quality data naturally increases the diversity of the generated outputs without intentionally loosening associations. This leads to improved generalization and variety in generated images.
-  enhanced_quality: |
-    The fine-tuning process eliminates initial issues, leading to clear, consistent, and high-quality image outputs.
-  versatility_across_styles: |
-    The Mobius model exhibits exceptional performance across various art styles and domains, ensuring the model can handle a wide range of artistic expressions with precision and creativity.
-
-## conclusion: 
-
-to be determined. 
+Unbiased generation: Mobius generates images that are virtually free from the inherent biases commonly found in other diffusion models, setting a new benchmark for fairness and impartiality across all domains.
+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.
+Efficient fine-tuning: The Mobius base model serves as a superior foundation for creating specialized models tailored to specific tasks or domains, requiring significantly less fine-tuning and computational resources compared to other state-of-the-art models.
 
+### Groundbreaking Performance
+Mobius has been rigorously tested across an unparalleled variety of prompts, consistently demonstrating its exceptional performance in numerous scenarios:
 
+Generating photorealistic images of people, animals, and objects with an unprecedented level of detail and accuracy
+Creating breathtaking artwork in a wide spectrum of styles, from classic paintings to cutting-edge digital art, surpassing the quality and diversity of existing models
+Producing highly detailed and precise visualizations of complex scientific concepts and data, enabling new possibilities for research and education
 
+### Collaborative Evolution
 
+We invite the community to contribute to the ongoing development of Mobius, helping to shape the future of debiased diffusion models. Please refer to our contribution guidelines for more information on how to get involved.
 
 
 ## Usage and Recommendations
 
-
+- a CFG of either 3.5 or 7.0
 - Requires a CLIP skip of -3
 - highly suggested to preappenmed watermark to all negatives and keep negatives simple such as "watermark" or "worst, watermark"