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SDXL-512 is a checkpoint fine-tuned from SDXL 1.0 that is designed to more simply generate higher-fidelity images at and around the 512x512 resolution. The model has been fine-tuned using a learning rate of 1e-6 over 7000 steps with a batch size of 64 on a curated dataset of multiple aspect ratios. alternating low and high resolution batches (per aspect ratio) so as not to impair the base model's existing performance at higher resolution.

Note: It bears repeating that SDXL-512 was not trained to be "better" than SDXL, but rather to simplify prompting for higher-fidelity outputs at and around the 512x512 resolution.

Model Description

  • Developed by: Natural Synthetics Inc.
  • Model type: Diffusion-based text-to-image generative model
  • License: CreativeML Open RAIL++-M License
  • Model Description: This is a model that can be used to generate and modify higher-fidelity images at and around the 512x512 resolution.
  • Resources for more information: Check out our GitHub Repository.
  • Finetuned from model: Stable Diffusion XL 1.0

🧨 Diffusers

Make sure to upgrade diffusers to >= 0.18.2:

pip install diffusers --upgrade

In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:

pip install invisible_watermark transformers accelerate safetensors

Running the pipeline (if you don't swap the scheduler it will run with the default EulerDiscreteScheduler in this example we are swapping it to EulerAncestralDiscreteScheduler:

from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

pipe = StableDiffusionXLPipeline.from_pretrained(

pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

prompt = "a woman laughing"
negative_prompt = ""

image = pipe(
    target_size=(1024, 1024),
    original_size=(4096, 4096),


Limitations and Bias


  • The model does not achieve perfect photorealism
  • The model cannot render legible text
  • The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
  • Faces and people in general may not be generated properly.


While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

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