language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
inference: true
widget:
- text: >-
modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot
body photo of the most beautiful artwork in the world, english medieval
witch, black silk vale, pale skin, black silk robe, black cat, necromancy
magic, medieval era, photorealistic painting by Ed Blinkey, Atey Ghailan,
Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on
ArtStation, trending on CGSociety, Intricate, High Detail, Sharp focus,
dramatic, photorealistic painting art by midjourney and greg rutkowski
example_title: Model photo
license: creativeml-openrail-m
Protogen v2.2
Research Model by darkstorm2150
Table of contents
- General info
- Granular Adaptive Learning
- Setup
- Space
- CompVis
- Diffusers
- Checkpoint Merging Data Reference
- License
General info
The Protogen v2.2 model is a cutting-edge machine learning algorithm that leverages the power of granular adaptive learning through the utilization of the revolutionary Stable Diffusion v1-5 algorithm. By fine-tuning the model with a vast and diverse array of data sourced from some of the most contemporary and comprehensive datasets available on Civitai.com and Huggingface.co, the Protogen v2.2 model has the ability to adapt to specific patterns and features in the data, unlocking a new level of performance and accuracy in the field of machine learning.
Granular Adaptive Learning
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.
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.
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.
PENDING DATA FOR MERGE, RPGv2 not accounted..
Checkpoint Merging Data Reference
| Models | Protogen v2.2 (Anime) | Protogen x3.4 (Photo) | Protogen x5.3 (Photo) | Protogen x5.8 (Sci-fi/Anime) | Protogen x5.9 (Dragon) | Protogen x7.4 (Eclipse) | Protogen x8.0 (Nova) | Protogen x8.6 (Infinity) |
|---|---|---|---|---|---|---|---|---|
| seek_art_mega v1 | 52.50% | 42.76% | 42.63% | 25.21% | 14.83% | |||
| modelshoot v1 | 30.00% | 24.44% | 24.37% | 2.56% | 2.05% | 3.48% | 22.91% | 13.48% |
| elldreth v1 | 12.64% | 10.30% | 10.23% | 6.06% | 3.57% | |||
| photoreal v2 | 10.00% | 48.64% | 38.91% | 66.33% | 20.49% | 12.06% | ||
| analogdiffusion v1 | 4.75% | 4.50% | 1.75% | 1.03% | ||||
| openjourney v2 | 4.51% | 4.28% | 4.75% | 2.26% | 1.33% | |||
| hassan1.4 | 2.63% | 2.14% | 2.13% | 1.26% | 0.74% | |||
| f222 | 2.23% | 1.82% | 1.81% | 1.07% | 0.63% | |||
| hasdx | 20.00% | 16.00% | 4.07% | 5.01% | 2.95% | |||
| moistmix | 16.00% | 12.80% | 3.86% | 4.08% | 2.40% | |||
| roboDiffusion v1 | 4.29% | 12.80% | 10.24% | 3.67% | 4.41% | 2.60% | ||
| RPG v3 | 5.00% | 20.00% | 4.29% | 4.29% | 2.52% | |||
| anything&everything | 4.51% | 0.56% | 0.33% | |||||
| dreamlikediff v1 | 5.0% | 0.63% | 0.37% | |||||
| sci-fidiff v1 | 3.10% | |||||||
| synthwavepunk v2 | 3.26% | |||||||
| mashupv2 | 11.51% | |||||||
| dreamshaper 252 | 4.04% | |||||||
| comicdiff v2 | 4.25% | |||||||
| artEros | 15.00% |
Setup
To run this model, download the model.ckpt and install it in your "stable-diffusion-webui\models\Stable-diffusion" directory
License
By downloading you agree to the terms of these licenses