sdxl_turbo.md

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# Stable Diffusion XL Turbo

[[open-in-colab]]

SDXL Turbo is an adversarial time-distilled [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (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:

```py
# 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 [`~StableDiffusionXLPipeline.from_pretrained`] method:

```py
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 [`~StableDiffusionXLPipeline.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):

```py
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.

```py
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
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-text2img.png" alt="generated image of a racoon in a robe"/>
</div>

## 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.

```py
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)
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-img2img.png" alt="Image-to-image generation sample using SDXL Turbo"/>
</div>

## 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.

```py
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:

```py
pipe.upcast_vae()
```

As an alternative, you can also use a [16-bit VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) created by community member [`@madebyollin`](https://huggingface.co/madebyollin) that does not need to be upcasted to `float32`.