OpenFLUX.1
Maintainer: ostris
78
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
OpenFLUX.1 is a work-in-progress model being developed by ostris. It is not ready for general use yet, but the goal is to create a non-distilled version of the impressive FLUX.1-schnell model, which was created by Black Forest Labs. The FLUX.1-schnell model is a 12 billion parameter rectified flow transformer capable of generating high-quality images from text descriptions. However, since FLUX.1-schnell is a distilled model, it cannot be fine-tuned with techniques like LoRAs, IP adapters, or control nets. The OpenFLUX.1 model aims to address this limitation by providing a non-distilled base that can be used to train these types of adapters, which can then be used with the FLUX.1-schnell model.
Model inputs and outputs
OpenFLUX.1 is a text-to-image generation model. It takes text prompts as input and generates corresponding images as output.
Inputs
- Text prompts: The model accepts natural language descriptions of the desired image as input.
Outputs
- Generated images: The model outputs images that attempt to visually represent the input text prompt.
Capabilities
The OpenFLUX.1 model is still in development, so its current capabilities are limited. Since it is breaking the distillation of the FLUX.1-schnell model, it may not produce images of the same high quality. Additionally, the model currently lacks guidance embeddings, which can negatively impact image generation. However, the goal is for OpenFLUX.1 to serve as a base model for training adapters that can then be used with the FLUX.1-schnell model to enable fine-tuning and other advanced techniques.
What can I use it for?
At this stage, OpenFLUX.1 is primarily useful for researchers and developers interested in exploring the potential of training adapters on a non-distilled version of the FLUX.1-schnell model. While the generated images may not be of the highest quality, the model could be a valuable tool for experimenting with different fine-tuning approaches and techniques. Once the model is more mature, it may have broader applications in text-to-image generation, but for now, its primary use case is as a research and development platform.
Things to try
Since OpenFLUX.1 is a work-in-progress, the best thing to try is experimenting with different fine-tuning techniques and monitoring the impact on image quality and performance. Researchers and developers interested in advancing the field of text-to-image generation may find this model a useful starting point for their own work.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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