--- 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 --- ### "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. 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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. 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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.