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# Stable diffusion XL

Stable Diffusion XL was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/abs/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach

The abstract of the paper is the following:

*We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.*

## Tips

- Stable Diffusion XL works especially well with images between 768 and 1024.
- Stable Diffusion XL output image can be improved by making use of a refiner as shown below

### Available checkpoints:

- *Text-to-Image (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) with [`StableDiffusionXLPipeline`]
- *Image-to-Image / Refiner (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9) with [`StableDiffusionXLImg2ImgPipeline`]

## Usage Example

Before using SDXL make sure to have `transformers`, `accelerate`, `safetensors` and `invisible_watermark` installed. 
You can install the libraries as follows:

```
pip install transformers
pip install accelerate
pip install safetensors
pip install invisible-watermark>=2.0
```

### *Text-to-Image*

You can use SDXL as follows for *text-to-image*:

```py
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt=prompt).images[0]
```

### Refining the image output

The image can be refined by making use of [stabilityai/stable-diffusion-xl-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
In this case, you only have to output the `latents` from the base model.

```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
refiner.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
image = refiner(prompt=prompt, image=image[None, :]).images[0]
```

### Loading single file checkpoitns / original file format

By making use of [`~diffusers.loaders.FromSingleFileMixin.from_single_file`] you can also load the 
original file format into `diffusers`:

```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
refiner.to("cuda")
```

### Memory optimization via model offloading 

If you are seeing out-of-memory errors, we recommend making use of [`StableDiffusionXLPipeline.enable_model_cpu_offload`].

```diff
- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()
```

and 

```diff
- refiner.to("cuda")
+ refiner.enable_model_cpu_offload()
```

### Speed-up inference with `torch.compile`

You can speed up inference by making use of `torch.compile`. This should give you **ca.** 20% speed-up.

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

### Running with `torch` < 2.0

**Note** that if you want to run Stable Diffusion XL with `torch` < 2.0, please make sure to enable xformers 
attention:

```
pip install xformers
```

```diff
+pipe.enable_xformers_memory_efficient_attention()
+refiner.enable_xformers_memory_efficient_attention()
```

## StableDiffusionXLPipeline

[[autodoc]] StableDiffusionXLPipeline
	- all
	- __call__

## StableDiffusionXLImg2ImgPipeline

[[autodoc]] StableDiffusionXLImg2ImgPipeline
	- all
	- __call__
