<!--Copyright 2023 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# 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")

use_refiner = True
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]
```

### Image-to-image 

```py 
import torch
from diffusers import StableDiffusionXLImg2ImgPipeline
from diffusers.utils import load_image

pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"

init_image = load_image(url).convert("RGB")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt, image=init_image).images[0]
```

| Original Image | Refined Image |
|---|---|
| ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png) | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png) |

### Loading single file checkpoints / 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__
