README.md

---
pipeline_tag: text-to-image
widget:
- text: >-
     movie scene screencap, cinematic footage. thanos smelling a little yellow rose. extreme wide angle,
  output:
    url: 1man.png 
- text: >-
     A tiny robot taking a break under a tree in the garden 
  output:
    url: robot.png 
- text: >-
     mystery
  output:
    url: mystery.png
- text: >-
     a cat wearing sunglasses in the summer
  output:
    url: cat.png
- text: >-
    robot holding a sign that says ’a storm is coming’ 
  output:
    url: storm.png
- text: >-
    the vibrance of the human soul
  output:
    url: soul.png
- text: >-
    Lady of War, chique dark clothes, vinyl, imposing pose, anime style, 90s natural photography of a man, glasses, cinematic,
  output:
    url: anime.png
- text: >-
    natural photography of a man, glasses, cinematic,  anime girl
  output:
    url: glasses.png
- text: >-
    anime girl
  output:
    url: animegirl.png

license: cc-by-nc-nd-4.0
---
<Gallery />

title: "Constructive Deconstruction: Domain-Agnostic Debiasing of Diffusion Models"

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.

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: |
  Constructive Deconstruction and the Mobius model represent a monumental leap forward in AI image generation. By addressing and eliminating biases through innovative techniques, we have created the best open source AI image generation model ever made. Mobius sets a new standard for quality and diversity, enabling unprecedented levels of creativity and precision. Its versatility across styles and domains makes it the ultimate tool for artists, designers, and creators, offering a level of excellence unmatched by any other open source model except MidJourney.

  By releasing the weights of the Mobius model, we are empowering the community with a tool that drives innovation and sets the benchmark for future developments in AI image synthesis. The quality, diversity, and reliability of Mobius make it the gold standard in the realm of open source AI models, rivaling even the most advanced proprietary solutions.










## Usage and Recommendations


- Requires a CLIP skip of -3

This model supports and encourages experimentation with various tags, offering users the freedom to explore their creative visions in depth.

## License

This model is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.