Update README.md

README.md

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-## Introducing Proteus-RunDiffusion: A New Era in Artistic AI
+## Introducing Proteus-RunDiffusion
 
 
-Proteus-RunDiffusion heralds a new era in AI art creation, evolving from the pioneering Pony Diffusion v6 XL CLIP. We've overhauled the CLIP architecture, infusing it with a unique retraining approach that not only preserves character recognition and boosts NLP capabilities but also maximizes the creative scope of AI-generated art. This advancement not only builds upon Pony's groundwork by significantly enhancing its style versatility and accuracy but also integrates Proteus prompt comprehension, further extending Pony's stylistic capabilities. The introduction of Proteus-RunDiffusion offers artists an unmatched tool, enabling the production of diverse, high-quality artworks with remarkable precision, while ensuring SDXL model compatibility and intellectual property respect. This reimagined CLIP model propels AI artistry forward, empowering creators to explore new artistic horizons and realize their most innovative ideas.
+In the development of Proteus-RunDiffusion, our team embarked on an exploratory project aimed at advancing the capabilities of AI in art creation. Our journey, inspired by the broad achievements of models like Pony Diffusion v6 XL CLIP, led us to experiment with the CLIP architecture in novel ways. Through a serendipitous process of trial, error, and discovery, we developed a unique approach to retraining CLIP that we hadn't initially set out to achieve. This approach inadvertently unlocked new potentials in character recognition, natural language processing, and, most notably, the versatility of artistic expression.
+
 
 https://rundiffusion.com/proteus-rundiffusion#view-generation-samples
 
-key acheivement: 
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-The thorough redesign and complete retraining of CLIP has resulted in an innovative phenomenon termed "style unlocking". This enhancement enables SDXL-based models to transcend stylistic constraints. For instance, applying it to a model previously confined to anime generation now permits the creation of high-fidelity photorealistic images, among other capabilities. applying this to a anime only model allows it to generate high fiedelity photorealism and so on.  
+The cornerstone of our discovery, which we refer to as "style unlocking," emerged unexpectedly. This breakthrough allows models that were previously limited to specific genres or styles, such as anime, to generate art across a broader spectrum, including high-fidelity photorealism. This was a result of our reimagined CLIP model's ability to interpret and understand prompts in ways that surpass the original boundaries of style and genre.
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+
+We have observed that this retraining has also led to significant improvements in handling CFG scaling, effectively broadening the range from 3 to 50 without the previous limitations or failures. This enhancement opens up new avenues for creative expression and technical reliability in AI-generated art.
 
-The comprehensive rework of the latent representation has successfully addressed CFG scaling issues, allowing the model to effectively handle CFG ranges from 3 to 50. This eliminates the occurrence of total image failures, which were common at CFG levels above 10, without the need for additional steps.
-## Optimal Creative Settings
 
-CLIP Settings: 
-Recommended settings include a CLIP of -2 and the strategic use of light negatives to unleash the full artistic potential of Proteus-RunDiffusion .
+In terms of usage, we recommend a CLIP setting of -2 along with a strategic use of light negatives for optimizing the artistic output of Proteus-RunDiffusion. The CFG setting can vary depending on the project, with 8.5 being ideal for standard requests and 3.5 for more artistic explorations. The model supports and encourages experimentation with various tags, offering users the freedom to explore their creative visions in depth.
 
-CFG:
-For standard requests, a CFG setting of 8.5 is optimal, while artistic endeavors perform best at a CFG setting of 3.5.
+Using Proteus-RunDiffusion: Expect a Different Experience
 
-Tagging Versatility: 
-The model supports the Pony tagging format and encourages experimentation. Users can explore tags like score_9, score_8_up, etc., or freely describe their creative vision.
+When you start using Proteus-RunDiffusion, be ready for it to behave differently from other AI art models you've used. It's been designed in a unique way, which means it will respond to your prompts and commands in its own style. This difference is part of what makes it special, but it also means there's a learning curve. You'll need some time to get familiar with how it works and what it can do. So, as you begin, keep an open mind and be prepared to adjust your approach.
 
-Backward Compatibility:
-Proteus-RunDiffusion’s adaptation of the Pony CLIP architecture guarantees backward compatibility with all SDXL models, offering a seamless and flexible creative experience.
 
+Importantly, we want to clarify that our development of Proteus-RunDiffusion was inspired by existing works but does not directly incorporate or rework specific components from models like Pony Diffusion's CLIP. Our advancements are the result of our proprietary research and development efforts, aimed at enhancing the creative possibilities and compatibility across different AI art generation platforms.
 
-## Legal Disclaimer and Methodology
 
-Innovative Retraining Approach
-Proteus-RunDiffusionrepresents a cutting-edge development in AI-driven artistic creation. It is crucial to note that this model leverages a completely reworked and retrained version of Pony's CLIP model, achieved through a unique and undisclosed method developed specifically for this project. This retraining process is designed to enhance compatibility with all SDXL models, ensuring a broad and flexible application.
 
-Distinct from Pony's Unet
-For legal and ethical considerations, it is important to clarify that ProteusV0.4-RunDiffusion does not incorporate any Unet components originally developed by Pony. Our model’s innovative capabilities and improvements are the result of proprietary advancements and do not infringe upon Pony’s original Unet architecture.
 
 There will be a upcoming Human Preference Study and Research Publication
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