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pipeline_tag: text-to-image |
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inference: false |
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license: other |
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license_name: sai-nc-community |
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license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT |
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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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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](https://stability.ai/research/adversarial-diffusion-distillation)), which allows sampling large-scale foundational |
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image diffusion models in 1 to 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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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:** https://stability.ai/research/adversarial-diffusion-distillation |
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- **Demo:** http://clipdrop.co/stable-diffusion-turbo |
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The charts above evaluate user preference for SDXL-Turbo over other single- and multi-step models. |
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SDXL-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-XL evaluated at four (or fewer) steps. |
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In addition, we see that using four steps for SDXL-Turbo further improves performance. |
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For details on the user study, we refer to the [research paper](https://stability.ai/research/adversarial-diffusion-distillation). |
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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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``` |
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pip install diffusers transformers accelerate |
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``` |
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- **Text-to-image**: |
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SDXL-Turbo does not make use of `guidance_scale` or `negative_prompt`, we disable it with `guidance_scale=0.0`. |
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Preferably, the model generates images of size 512x512 but higher image sizes work as well. |
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A **single step** is enough to generate high quality images. |
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```py |
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from diffusers import AutoPipelineForText2Image |
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import torch |
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pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") |
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pipe.to("cuda") |
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prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe." |
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image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0] |
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``` |
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- **Image-to-image**: |
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When using SDXL-Turbo for image-to-image generation, make sure that `num_inference_steps` * `strength` is larger or equal |
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to 1. The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, *e.g.* 0.5 * 2.0 = 1 step in our example |
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below. |
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```py |
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from diffusers import AutoPipelineForImage2Image |
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from diffusers.utils import load_image |
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pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") |
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init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512)) |
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prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k" |
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image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0] |
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``` |
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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 |
