sdxl_dpo_turbo
Maintainer: thibaud
83
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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
sdxl_dpo_turbo is a merge of two large language models - SDXL Turbo and SDXL DPO - developed by Stability AI and Anthropic researchers. SDXL Turbo is a fast generative text-to-image model trained using a novel technique called Adversarial Diffusion Distillation, which allows for high-quality image synthesis in just 1-4 steps. SDXL DPO is a text-to-image diffusion model that has been aligned to human preferences using Direct Preference Optimization. By combining these two models, sdxl_dpo_turbo aims to provide both high-speed image generation and strong alignment with human preferences.
Similar models include the dpo-sdxl-text2image-v1 and dpo-sd1.5-text2image-v1 models, which also use DPO to align diffusion models to human preferences, but are based on different base models.
Model inputs and outputs
Inputs
- Text prompts to generate images
Outputs
- Images generated from the input text prompts
Capabilities
sdxl_dpo_turbo is capable of generating photorealistic images from text prompts in a single network evaluation, thanks to the speed of the SDXL Turbo model. The human preference alignment from SDXL DPO aims to ensure the generated images are well-aligned with the intent of the input text. Example use cases include creating illustrations, concept art, and other visuals based on textual descriptions.
What can I use it for?
sdxl_dpo_turbo can be used for a variety of non-commercial and commercial applications, including research on generative models, real-time applications of text-to-image generation, and the creation of artwork and design assets. For commercial use, you will need to refer to Stability AI's membership options.
Things to try
One interesting thing to try with sdxl_dpo_turbo is exploring the model's ability to generate images that closely match the intent and details of the input text prompt, thanks to the DPO fine-tuning. You could experiment with prompts that require specific visual elements or styles and see how well the model is able to capture those requirements.
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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