Flux-Schnell-Lora

The flux-schnell-lora model is a collection of fine-tuned models , specifically designed for text-to-image generation. LoRA, which stands for Low-Rank Adaptation, is a technique used to fine-tune pre-trained models efficiently. The flux-schnell-lora model is based on the FLUX.1-dev model by Black Forest Labs.

Key Features

• Superior Visual Output: Produces high-quality images with excellent adherence to input prompts, standing on par with many high-end closed-source alternatives.
• Rapid Generation Process: Utilizes latent adversarial diffusion distillation techniques to generate quality images in as few as one to four steps.
• Flexible Licensing: Released under the Apache-2.0 license, making flux-schnell-lora suitable for personal, scientific, and commercial applications.

Limitations

• Creative Focused: flux-schnell-lora is optimized for creative image generation rather than providing reliable factual information.
• Potential Biases: As a statistical model, it may inherit biases present in its training data.
• Prompt Sensitivity: The performance and fidelity of outputs can vary depending on the specificity and style of the provided prompt.

Out-of-Scope Use

The model and its derivatives may not be used in any way that violates applicable national, federal, state, local, or international law or regulation, including but not limited to:

• Exploiting, harming, or attempting to exploit or harm minors, including solicitation, creation, acquisition, or dissemination of child exploitative content.
• Generating or disseminating verifiably false information with the intent to harm others.
• Creating or distributing personal identifiable information that could be used to harm an individual.
• Harassing, abusing, threatening, stalking, or bullying individuals or groups.
• Producing non-consensual nudity or illegal pornographic content.
• Making fully automated decisions that adversely affect an individual’s legal rights or create binding obligations.
• Facilitating large-scale disinformation campaigns.