#
Stable Diffusion XL

Stable Diffusion XL (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:

1. the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters
2. introduces size and crop-conditioning to preserve training data from being discarded and gain more control over how a generated image should be cropped
3. introduces a two-stage model process; the base model (can also be run as a standalone model) generates an image as an input to the refiner model which adds additional high-quality details

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

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 invisible-watermark>=0.2.0

We recommend installing the invisible-watermark library to help identify images that are generated. If the invisible-watermark library is installed, it is used by default. To disable the watermarker:

    pipeline = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)

##
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 StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
    import torch

    pipeline = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
    ).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:

    from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
    import torch

    pipeline = StableDiffusionXLPipeline.from_single_file(
        "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
        torch_dtype=torch.float16
    ).to("cuda")

    refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
        "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
    ).to("cuda")

##
Text-to-image

For text-to-image, pass a text prompt. By default, SDXL generates a 1024x1024 image for the best results. You can try setting the `height` and `width` parameters to 768x768 or 512x512, but anything below 512x512 is not likely to work.

    from diffusers import AutoPipelineForText2Image
    import torch

    pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipeline_text2image(prompt=prompt).images[0]
    image

##
Image-to-image

For image-to-image, SDXL works especially well with image sizes between 768x768 and 1024x1024. Pass an initial image, and a text prompt to condition the image with:

    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 = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")

    url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
    init_image = load_image(url)
    prompt = "a dog catching a frisbee in the jungle"
    image = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]
    make_image_grid([init_image, image], rows=1, cols=2)

##
Inpainting

For inpainting, you’ll need the original image and a mask of what you want to replace in the original image. Create a prompt to describe what you want to replace the masked area with.

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

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

    img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
    mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"

    init_image = load_image(img_url)
    mask_image = load_image(mask_url)

    prompt = "A deep sea diver floating"
    image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]
    make_image_grid([init_image, mask_image, image], rows=1, cols=3)

##
Refine image quality

SDXL includes a refiner model specialized in denoising low-noise stage images to generate higher-quality images from the base model. There are two ways to use the refiner:

1. use the base and refiner models together to produce a refined image
2. use the base model to produce an image, and subsequently use the refiner model to add more details to the image (this is how SDXL was originally trained)

###
Base + refiner model

When you use the base and refiner model together to generate an image, this is known as an ensemble of expert denoisers. The ensemble of expert denoisers approach requires fewer overall denoising steps versus passing the base model’s output to the refiner model, so it should be significantly faster to run. However, you won’t be able to inspect the base model’s output because it still contains a large amount of noise.

As an ensemble of expert denoisers, the base model serves as the expert during the high-noise diffusion stage and the refiner model serves as the expert during the low-noise diffusion stage. Load the base and refiner model:

    from diffusers import DiffusionPipeline
    import torch

    base = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    refiner = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=base.text_encoder_2,
        vae=base.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    ).to("cuda")

To use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the `denoising_end` parameter and for the refiner model, it is controlled by the `denoising_start` parameter.

The `denoising_end` and `denoising_start` parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you’re also using the `strength` parameter, it’ll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff.

Let’s set `denoising_end=0.8` so the base model performs the first 80% of denoising the high-noise timesteps and set `denoising_start=0.8` so the refiner model performs the last 20% of denoising the low-noise timesteps. The base model output should be in latent space instead of a PIL image.

    prompt = "A majestic lion jumping from a big stone at night"

    image = base(
        prompt=prompt,
        num_inference_steps=40,
        denoising_end=0.8,
        output_type="latent",
    ).images
    image = refiner(
        prompt=prompt,
        num_inference_steps=40,
        denoising_start=0.8,
        image=image,
    ).images[0]
    image

default base model
ensemble of expert denoisers

The refiner model can also be used for inpainting in the StableDiffusionXLInpaintPipeline:

    from diffusers import StableDiffusionXLInpaintPipeline
    from diffusers.utils import load_image, make_image_grid
    import torch

    base = StableDiffusionXLInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=base.text_encoder_2,
        vae=base.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    ).to("cuda")

    img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

    init_image = load_image(img_url)
    mask_image = load_image(mask_url)

    prompt = "A majestic tiger sitting on a bench"
    num_inference_steps = 75
    high_noise_frac = 0.7

    image = base(
        prompt=prompt,
        image=init_image,
        mask_image=mask_image,
        num_inference_steps=num_inference_steps,
        denoising_end=high_noise_frac,
        output_type="latent",
    ).images
    image = refiner(
        prompt=prompt,
        image=image,
        mask_image=mask_image,
        num_inference_steps=num_inference_steps,
        denoising_start=high_noise_frac,
    ).images[0]
    make_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3)

This ensemble of expert denoisers method works well for all available schedulers!

###
Base to refiner model

SDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image from the base model, in an image-to-image setting.

Load the base and refiner models:

    from diffusers import DiffusionPipeline
    import torch

    base = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    refiner = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-refiner-1.0",
        text_encoder_2=base.text_encoder_2,
        vae=base.vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        variant="fp16",
    ).to("cuda")

You can use SDXL refiner with a different base model. For example, you can use the Hunyuan-DiT or PixArt-Sigma pipelines to generate images with better prompt adherence. Once you have generated an image, you can pass it to the SDXL refiner model to enhance final generation quality.

Generate an image from the base model, and set the model output to latent space:

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

    image = base(prompt=prompt, output_type="latent").images[0]

Pass the generated image to the refiner model:

    image = refiner(prompt=prompt, image=image[None, :]).images[0]

base model
base model + refiner model

For inpainting, load the base and the refiner model in the StableDiffusionXLInpaintPipeline, remove the `denoising_end` and `denoising_start` parameters, and choose a smaller number of inference steps for the refiner.

##
Micro-conditioning

SDXL training involves several additional conditioning techniques, which are referred to as micro-conditioning. These include original image size, target image size, and cropping parameters. The micro-conditionings can be used at inference time to create high-quality, centered images.

You can use both micro-conditioning and negative micro-conditioning parameters thanks to classifier-free guidance. They are available in the StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline, and StableDiffusionXLControlNetPipeline.

###
Size conditioning

There are two types of size conditioning:

- `original_size` conditioning comes from upscaled images in the training batch (because it would be wasteful to discard the smaller images which make up almost 40% of the total training data). This way, SDXL learns that upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to indicate the original image resolution. Using the default value of `(1024, 1024)` produces higher-quality images that resemble the 1024x1024 images in the dataset. If you choose to use a lower resolution, such as `(256, 256)`, the model still generates 1024x1024 images, but they’ll look like the low resolution images (simpler patterns, blurring) in the dataset.

- `target_size` conditioning comes from finetuning SDXL to support different image aspect ratios. During inference, if you use the default value of `(1024, 1024)`, you’ll get an image that resembles the composition of square images in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with other options!

🤗 Diffusers also lets you specify negative conditions about an image’s size to steer generation away from certain image resolutions:

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipe = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(
        prompt=prompt,
        negative_original_size=(512, 512),
        negative_target_size=(1024, 1024),
    ).images[0]

Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512).

###
Crop conditioning

Images generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the same size. By conditioning on crop coordinates, SDXL learns that no cropping - coordinates `(0, 0)` - usually correlates with centered subjects and complete faces (this is the default value in 🤗 Diffusers). You can experiment with different coordinates if you want to generate off-centered compositions!

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipeline = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0]
    image

You can also specify negative cropping coordinates to steer generation away from certain cropping parameters:

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipe = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(
        prompt=prompt,
        negative_original_size=(512, 512),
        negative_crops_coords_top_left=(0, 0),
        negative_target_size=(1024, 1024),
    ).images[0]
    image

##
Use a different prompt for each text-encoder

SDXL uses two text-encoders, so it is possible to pass a different prompt to each text-encoder, which can improve quality. Pass your original prompt to `prompt` and the second prompt to `prompt_2` (use `negative_prompt` and `negative_prompt_2` if you’re using negative prompts):

    from diffusers import StableDiffusionXLPipeline
    import torch

    pipeline = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
    ).to("cuda")

    # prompt is passed to OAI CLIP-ViT/L-14
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    # prompt_2 is passed to OpenCLIP-ViT/bigG-14
    prompt_2 = "Van Gogh painting"
    image = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]
    image

The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the SDXL textual inversion section.

##
Optimizations

SDXL is a large model, and you may need to optimize memory to get it to run on your hardware. Here are some tips to save memory and speed up inference.

1. Offload the model to the CPU with enable_model_cpu_offload() for out-of-memory errors:

    - base.to("cuda")
    - refiner.to("cuda")
    + base.enable_model_cpu_offload()
    + refiner.enable_model_cpu_offload()

2. Use `torch.compile` for ~20% speed-up (you need `torch>=2.0`):

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

3. Enable xFormers to run SDXL if `torch<2.0`:

    + base.enable_xformers_memory_efficient_attention()
    + refiner.enable_xformers_memory_efficient_attention()

##
Other resources

If you’re interested in experimenting with a minimal version of the UNet2DConditionModel used in SDXL, take a look at the minSDXL implementation which is written in PyTorch and directly compatible with 🤗 Diffusers.

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