wavespeed-ai/flux-schnell

flux-schnell is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.

Key Features

• Exceptional Image Quality: Delivers state-of-the-art visual outputs with precise adherence to input prompts, rivaling the performance of proprietary alternatives.
• Ultra-Fast Inference: Designed to produce high-quality images in as few as 1 to 4 generation steps, meeting the demands of time-sensitive creative tasks.
• Broad Usage Rights: Distributed under the Apache-2.0 license, making flux-schnell a robust choice for personal, scientific, and commercial projects.

Limitations

• Content Accuracy: flux-schnell is optimized for artistic generation and is not intended to provide reliable factual information.
• Potential Biases: As a statistical model, it may inadvertently reinforce biases present in the training data.
• Prompt Sensitivity: The output quality and adherence can vary depending on the style and complexity of the input 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.

Accelerated Inference

Our accelerated inference approach leverages advanced optimization technology from WavespeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.