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 />

### "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

Mobius Creative Commons Attribution License (MCC-AL)

By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Mobius Creative Commons Attribution License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.

Section 1 – Definitions.

a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image.

b. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License.

c. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.

d. Licensor means the individual(s) or entity(ies) granting rights under this Public License.

e. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For the purposes of this Public License, the use of the Licensed Material within the BitTensor network is considered NonCommercial.

f. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them.

Section 2 – Scope.

a. License grant.

Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
B. notwithstanding the NonCommercial clause above, to use the Licensed Material commercially within the BitTensor network, provided that appropriate credit is given, a link to the license is provided, and indication is made if changes were made.
C. use the Licensed Material for fine-tunes and Loras, provided that the base name is used for any fine-tunes (e.g., MobiusAnimeXL or MobiusPony).
b. Exceptions and Limitations. For the avoidance of doubt, where exceptions and limitations to Copyright and Similar Rights apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.

c. Term. The term of this Public License is specified in Section 6(a).

d. Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material.

e. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).

Section 3 – License Conditions.

Your exercise of the Licensed Rights is expressly made subject to the following conditions.

a. Attribution.

If You Share the Licensed Material, You must:
A. retain the following if it is supplied by the Licensor with the Licensed Material:
i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
ii. a copyright notice;
iii. a notice that refers to this Public License;
iv. a notice that refers to the disclaimer of warranties;
v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
For the avoidance of doubt, You do not have permission under this Public License to Share Adapted Material.

Section 4 – Sui Generis Database Rights.

Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:

a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; and

b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material.

Section 5 – Disclaimer of Warranties and Limitation of Liability.

a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable.

b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages.

c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.

Section 6 – Term and Termination.

a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.

b. **Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:

automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.**
c. For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.

d. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.

e. Sections 1, 5, 6, 7, and 8 survive termination of this Public License.

Section 7 – Other Terms and Conditions.

a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.

b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.

Section 8 – Interpretation.

a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.

b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.

c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.

d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.

Additional Permissions for Commercial Use on BitTensor:

BitTensor Use Permission: Notwithstanding the NonCommercial clause above, the material may be used commercially within the BitTensor network. This includes, but is not limited to, using the material in models, datasets, or other applications specifically designed for or running on BitTensor.
Attribution on BitTensor: When using the material within the BitTensor network, you must provide appropriate credit in a manner that is reasonable and customary within the network. This includes linking to the license and indicating if changes were made.
Base Name for Fine-Tunes: Any fine-tunes or Loras based on the Licensed Material must include the base name (e.g., MobiusAnimeXL, MobiusPony).
Distribution Restrictions: Distribution of these weights from repositories or platforms other than the official one is not allowed.
Generated Images: You own all the images you generate and can use them as you like. However, selling the model's output as a service, such as through an API endpoint, is not allowed.
Contact for Other Commercial Uses: For any commercial use outside of the BitTensor network, please contact xander@corcel.io.