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# SDXL-Turbo Model Card |
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<!-- Provide a quick summary of what the model is/does. --> |
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![row01](output_tile.jpg) |
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SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. |
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A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo |
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## Model Details |
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### Model Description |
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SDXL-Turbo is a distilled version of [SDXL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), trained for real-time synthesis. |
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SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the [technical report](TODO)), which allows sampling large-scale foundational |
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image diffusion models in 1--4 steps at high image quality. |
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This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an |
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adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. |
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- **Developed by:** Stability AI |
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- **Funded by:** Stability AI |
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- **Model type:** Generative text-to-image model |
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- **Finetuned from model:** [SDXL 1.0 Base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) |
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### Model Sources |
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For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), |
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which implements the most popular diffusion frameworks (both training and inference). |
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- **Repository:** https://github.com/Stability-AI/generative-models |
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- **Paper:** TODO |
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- **Demo:** http://clipdrop.co/stable-diffusion-turbo |
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## Evaluation |
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![comparison1](image_quality_one_step.png) |
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![comparison2](prompt_alignment_one_step.png) |
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The chart above evaluates user preference for SDXL-Turbo over TODO. |
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SDXL-Turbo is preferred by human voters in terms of image quality and prompt following. |
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For details on the user study, we refer to the [research paper](TODO). |
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## Uses |
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### Direct Use |
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The model is intended for research purposes only. Possible research areas and tasks include |
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- Research on generative models. |
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- Research on real-time applications of generative models. |
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- Research on the impact of real-time generative models. |
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- Safe deployment of models which have the potential to generate harmful content. |
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- Probing and understanding the limitations and biases of generative models. |
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- Generation of artworks and use in design and other artistic processes. |
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- Applications in educational or creative tools. |
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Excluded uses are described below. |
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### Out-of-Scope Use |
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The model was not trained to be factual or true representations of people or events, |
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and therefore using the model to generate such content is out-of-scope for the abilities of this model. |
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The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). |
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## Limitations and Bias |
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### Limitations |
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- The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism. |
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- The model cannot render legible text. |
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- Faces and people in general may not be generated properly. |
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- The autoencoding part of the model is lossy. |
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### Recommendations |
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The model is intended for research purposes only. |
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## How to Get Started with the Model |
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Check out https://github.com/Stability-AI/generative-models |
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