#
Stable Diffusion XL Turbo

SDXL Turbo is an adversarial time-distilled Stable Diffusion XL (SDXL) model capable of running inference in as little as 1 step.

This guide will show you how to use SDXL-Turbo for text-to-image and image-to-image.

Before you begin, make sure you have the following libraries installed:

    # uncomment to install the necessary libraries in Colab
    #!pip install -q diffusers transformers accelerate

##
Load model checkpoints

Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the from_pretrained() method:

    from diffusers import AutoPipelineForText2Image
    import torch

    pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
    pipeline = pipeline.to("cuda")

You can also use the from_single_file() method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally. For this loading method, you need to set `timestep_spacing="trailing"` (feel free to experiment with the other scheduler config values to get better results):

    from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
    import torch

    pipeline = StableDiffusionXLPipeline.from_single_file(
        "https://huggingface.co/stabilityai/sdxl-turbo/blob/main/sd_xl_turbo_1.0_fp16.safetensors",
        torch_dtype=torch.float16, variant="fp16")
    pipeline = pipeline.to("cuda")
    pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")

##
Text-to-image

For text-to-image, pass a text prompt. By default, SDXL Turbo generates a 512x512 image, and that resolution gives the best results. You can try setting the `height` and `width` parameters to 768x768 or 1024x1024, but you should expect quality degradations when doing so.

Make sure to set `guidance_scale` to 0.0 to disable, as the model was trained without it. A single inference step is enough to generate high quality images. Increasing the number of steps to 2, 3 or 4 should improve image quality.

    from diffusers import AutoPipelineForText2Image
    import torch

    pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
    pipeline_text2image = pipeline_text2image.to("cuda")

    prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."

    image = pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=1).images[0]
    image

##
Image-to-image

For image-to-image generation, make sure that `num_inference_steps * strength` is larger or equal 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 below.

    from diffusers import AutoPipelineForImage2Image
    from diffusers.utils import load_image, make_image_grid

    # use from_pipe to avoid consuming additional memory when loading a checkpoint
    pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")

    init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
    init_image = init_image.resize((512, 512))

    prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

    image = pipeline_image2image(prompt, image=init_image, strength=0.5, guidance_scale=0.0, num_inference_steps=2).images[0]
    make_image_grid([init_image, image], rows=1, cols=2)

##
Speed-up SDXL Turbo even more

- Compile the UNet if you are using PyTorch version 2.0 or higher. The first inference run will be very slow, but subsequent ones will be much faster.

    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

- When using the default VAE, keep it in `float32` to avoid costly `dtype` conversions before and after each generation. You only need to do this one before your first generation:

    pipe.upcast_vae()

As an alternative, you can also use a 16-bit VAE created by community member `@madebyollin` that does not need to be upcasted to `float32`.