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--- |
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language: |
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- en |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- art |
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- artistic |
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- diffusers |
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- protogen |
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inference: true |
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widget: |
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- text: >- |
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modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body |
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photo of the most beautiful artwork in the world, english medieval witch, |
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black silk vale, pale skin, black silk robe, black cat, necromancy magic, |
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medieval era, photorealistic painting by Ed Blinkey, Atey Ghailan, Studio |
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Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, |
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trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, |
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photorealistic painting art by midjourney and greg rutkowski |
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example_title: Model photo |
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license: creativeml-openrail-m |
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--- |
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<center><img src="https://huggingface.co/darkstorm2150/OpenGen/resolve/main/OpenGen_v1.jpeg" style="height:400px; border-radius: 7%; border: 10px solid #663380; padding-top:0px;" span title="Protogen Nova Raw Output with a bladerunner 2049 embedding ;)"></center> |
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<center><h1>OpenGen v1</h1></center> |
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<center><p><em>Research Model by <a href="https://instagram.com/officialvictorespinoza">darkstorm2150</a></em></p></center> |
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</div> |
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## Table of contents |
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* [General info](#general-info) |
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* [Granular Adaptive Learning](#granular-adaptive-learning) |
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* [Setup](#setup) |
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* [Space](#space) |
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* [CompVis](#compvis) |
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* [Diffusers](#diffusers) |
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* [Checkpoint Merging Data Reference](#checkpoint-merging-data-reference) |
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* [License](#license) |
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## General info |
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OpenGen is a continuation from Protogen model, this model was handcrafted by a selection from what I personally consider the best models currently available, the licensing continues its respective formal bindings from its previous merges. |
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* Adding images to gallery for better viewing |
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* Checkpoint Merge Data |
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* seek.art MEGA commercial restrictions removed. |
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## Granular Adaptive Learning |
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Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends. |
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Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data. |
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Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing. |
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## Setup |
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To run this model, download the model.ckpt and install it in your "stable-diffusion-webui\models\Stable-diffusion" directory |
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## Space |
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## CompVis |
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## Diffusers |
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## Checkpoint Merging Data Reference |
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<left><em><a href="https://github.com/darkstorm2150/OpenGen">Link</a></em></left> |
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## License |
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By downloading you agree to the terms of these licenses |
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<a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">CreativeML Open RAIL-M</a> |
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<a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md">Dreamlike License</a> |
