# Stable Diffusion XL

  
  

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

The abstract from the paper is:

*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

- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for ODE/SDE solvers:
	- set `use_karras_sigmas=True` or `lu_lambdas=True` to improve image quality
	- set `euler_at_final=True` if you're using a solver with uniform step sizes (DPM++2M or DPM++2M SDE)
- Most SDXL checkpoints work best with an image size of 1024x1024. Image sizes of 768x768 and 512x512 are also supported, but the results aren't as good. Anything below 512x512 is not recommended and likely won't be for default checkpoints like [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
- SDXL can pass a different prompt for each of the text encoders it was trained on. We can even pass different parts of the same prompt to the text encoders.
- SDXL output images can be improved by making use of a refiner model in an image-to-image setting.
- SDXL offers `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to negatively condition the model on image resolution and cropping parameters.

> [!TIP]
> To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl) guide.
>
> Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!

## StableDiffusionXLPipeline[[diffusers.StableDiffusionXLPipeline]]

#### diffusers.StableDiffusionXLPipeline[[diffusers.StableDiffusionXLPipeline]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L170)

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from [DiffusionPipeline](/docs/diffusers/v0.38.0/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:
- [load_textual_inversion()](/docs/diffusers/v0.38.0/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
- [from_single_file()](/docs/diffusers/v0.38.0/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
- [load_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights) for loading LoRA weights
- [save_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights) for saving LoRA weights
- [load_ip_adapter()](/docs/diffusers/v0.38.0/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters

__call__diffusers.StableDiffusionXLPipeline.__call__https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L821[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "prompt_2", "val": ": str | list[str] | None = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": list = None"}, {"name": "sigmas", "val": ": list = None"}, {"name": "denoising_end", "val": ": float | None = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "negative_prompt_2", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "ip_adapter_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "ip_adapter_image_embeds", "val": ": list[torch.Tensor] | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": tuple[int, int] | None = None"}, {"name": "crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "target_size", "val": ": tuple[int, int] | None = None"}, {"name": "negative_original_size", "val": ": tuple[int, int] | None = None"}, {"name": "negative_crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "negative_target_size", "val": ": tuple[int, int] | None = None"}, {"name": "clip_skip", "val": ": int | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "**kwargs", "val": ""}]- **prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **timesteps** (`list[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`list[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise as determined by the discrete timesteps selected by the
  scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
  "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
- **guidance_scale** (`float`, *optional*, defaults to 5.0) --
  Guidance scale as defined in [Classifier-Free Diffusion
  Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
  of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
  `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **negative_prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to [schedulers.DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), will be ignored for others.
- **generator** (`torch.Generator` or `list[torch.Generator]`, *optional*) --
  One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
  to make generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor will be generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
  provided, text embeddings will be generated from `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
  argument.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`list[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead
  of a plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
  [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`list`, *optional*) --
  The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
  will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
  `._callback_tensor_inputs` attribute of your pipeline class.0`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple``~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionXLPipeline

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

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt).images[0]
```

**Parameters:**

vae ([AutoencoderKL](/docs/diffusers/v0.38.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) : Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.

text_encoder (`CLIPTextModel`) : Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.

text_encoder_2 (` CLIPTextModelWithProjection`) : Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant.

tokenizer (`CLIPTokenizer`) : Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

tokenizer_2 (`CLIPTokenizer`) : Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

unet ([UNet2DConditionModel](/docs/diffusers/v0.38.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) : Conditional U-Net architecture to denoise the encoded image latents.

scheduler ([SchedulerMixin](/docs/diffusers/v0.38.0/en/api/schedulers/overview#diffusers.SchedulerMixin)) : A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/pndm#diffusers.PNDMScheduler).

force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`) : Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.

add_watermarker (`bool`, *optional*) : Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

**Returns:**

``~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple``

`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.
#### encode_prompt[[diffusers.StableDiffusionXLPipeline.encode_prompt]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L283)

Encodes the prompt into text encoder hidden states.

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : prompt to be encoded

prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders

device : (`torch.device`): torch device

num_images_per_prompt (`int`) : number of images that should be generated per prompt

do_classifier_free_guidance (`bool`) : whether to use classifier free guidance or not

negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).

negative_prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.

pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.

negative_pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.

lora_scale (`float`, *optional*) : A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

clip_skip (`int`, *optional*) : Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
#### get_guidance_scale_embedding[[diffusers.StableDiffusionXLPipeline.get_guidance_scale_embedding]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L756)

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

**Parameters:**

w (`torch.Tensor`) : Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.

embedding_dim (`int`, *optional*, defaults to 512) : Dimension of the embeddings to generate.

dtype (`torch.dtype`, *optional*, defaults to `torch.float32`) : Data type of the generated embeddings.

**Returns:**

``torch.Tensor``

Embedding vectors with shape `(len(w), embedding_dim)`.

## StableDiffusionXLImg2ImgPipeline[[diffusers.StableDiffusionXLImg2ImgPipeline]]

#### diffusers.StableDiffusionXLImg2ImgPipeline[[diffusers.StableDiffusionXLImg2ImgPipeline]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L188)

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from [DiffusionPipeline](/docs/diffusers/v0.38.0/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:
- [load_textual_inversion()](/docs/diffusers/v0.38.0/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
- [from_single_file()](/docs/diffusers/v0.38.0/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
- [load_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights) for loading LoRA weights
- [save_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights) for saving LoRA weights
- [load_ip_adapter()](/docs/diffusers/v0.38.0/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters

__call__diffusers.StableDiffusionXLImg2ImgPipeline.__call__https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L973[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "prompt_2", "val": ": str | list[str] | None = None"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "strength", "val": ": float = 0.3"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": list = None"}, {"name": "sigmas", "val": ": list = None"}, {"name": "denoising_start", "val": ": float | None = None"}, {"name": "denoising_end", "val": ": float | None = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "negative_prompt_2", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "ip_adapter_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "ip_adapter_image_embeds", "val": ": list[torch.Tensor] | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": tuple = None"}, {"name": "crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "target_size", "val": ": tuple = None"}, {"name": "negative_original_size", "val": ": tuple[int, int] | None = None"}, {"name": "negative_crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "negative_target_size", "val": ": tuple[int, int] | None = None"}, {"name": "aesthetic_score", "val": ": float = 6.0"}, {"name": "negative_aesthetic_score", "val": ": float = 2.5"}, {"name": "clip_skip", "val": ": int | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "**kwargs", "val": ""}]- **prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **image** (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `list[torch.Tensor]` or `list[PIL.Image.Image]` or `list[np.ndarray]`) --
  The image(s) to modify with the pipeline.
- **strength** (`float`, *optional*, defaults to 0.3) --
  Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
  will be used as a starting point, adding more noise to it the larger the `strength`. The number of
  denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
  be maximum and the denoising process will run for the full number of iterations specified in
  `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
  `denoising_start` being declared as an integer, the value of `strength` will be ignored.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **timesteps** (`list[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`list[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_start** (`float`, *optional*) --
  When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
  bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
  it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
  strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
  is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
  denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
  final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
  forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  Guidance scale as defined in [Classifier-Free Diffusion
  Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
  of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
  `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **negative_prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to [schedulers.DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), will be ignored for others.
- **generator** (`torch.Generator` or `list[torch.Generator]`, *optional*) --
  One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
  to make generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor will be generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
  provided, text embeddings will be generated from `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
  argument.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`list[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
  [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **aesthetic_score** (`float`, *optional*, defaults to 6.0) --
  Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_aesthetic_score** (`float`, *optional*, defaults to 2.5) --
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
  simulate an aesthetic score of the generated image by influencing the negative text condition.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`list`, *optional*) --
  The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
  will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
  `._callback_tensor_inputs` attribute of your pipeline class.0`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple``~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionXLImg2ImgPipeline
>>> from diffusers.utils import load_image

>>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-refiner-1.0", 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]
```

**Parameters:**

vae ([AutoencoderKL](/docs/diffusers/v0.38.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) : Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.

text_encoder (`CLIPTextModel`) : Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.

text_encoder_2 (` CLIPTextModelWithProjection`) : Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant.

tokenizer (`CLIPTokenizer`) : Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

tokenizer_2 (`CLIPTokenizer`) : Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

unet ([UNet2DConditionModel](/docs/diffusers/v0.38.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) : Conditional U-Net architecture to denoise the encoded image latents.

scheduler ([SchedulerMixin](/docs/diffusers/v0.38.0/en/api/schedulers/overview#diffusers.SchedulerMixin)) : A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/pndm#diffusers.PNDMScheduler).

requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`) : Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`.

force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`) : Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.

add_watermarker (`bool`, *optional*) : Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

**Returns:**

``~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple``

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images.
#### encode_prompt[[diffusers.StableDiffusionXLImg2ImgPipeline.encode_prompt]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L301)

Encodes the prompt into text encoder hidden states.

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : prompt to be encoded

prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders

device : (`torch.device`): torch device

num_images_per_prompt (`int`) : number of images that should be generated per prompt

do_classifier_free_guidance (`bool`) : whether to use classifier free guidance or not

negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).

negative_prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.

pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.

negative_pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.

lora_scale (`float`, *optional*) : A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

clip_skip (`int`, *optional*) : Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
#### get_guidance_scale_embedding[[diffusers.StableDiffusionXLImg2ImgPipeline.get_guidance_scale_embedding]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L904)

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

**Parameters:**

w (`torch.Tensor`) : Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.

embedding_dim (`int`, *optional*, defaults to 512) : Dimension of the embeddings to generate.

dtype (`torch.dtype`, *optional*, defaults to `torch.float32`) : Data type of the generated embeddings.

**Returns:**

``torch.Tensor``

Embedding vectors with shape `(len(w), embedding_dim)`.

## StableDiffusionXLInpaintPipeline[[diffusers.StableDiffusionXLInpaintPipeline]]

#### diffusers.StableDiffusionXLInpaintPipeline[[diffusers.StableDiffusionXLInpaintPipeline]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py#L215)

Pipeline for text-to-image generation using Stable Diffusion XL.

This model inherits from [DiffusionPipeline](/docs/diffusers/v0.38.0/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:
- [load_textual_inversion()](/docs/diffusers/v0.38.0/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
- [from_single_file()](/docs/diffusers/v0.38.0/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
- [load_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights) for loading LoRA weights
- [save_lora_weights()](/docs/diffusers/v0.38.0/en/api/loaders/lora#diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights) for saving LoRA weights
- [load_ip_adapter()](/docs/diffusers/v0.38.0/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters

__call__diffusers.StableDiffusionXLInpaintPipeline.__call__https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py#L1078[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "prompt_2", "val": ": str | list[str] | None = None"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "mask_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "masked_image_latents", "val": ": Tensor = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "padding_mask_crop", "val": ": int | None = None"}, {"name": "strength", "val": ": float = 0.9999"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": list = None"}, {"name": "sigmas", "val": ": list = None"}, {"name": "denoising_start", "val": ": float | None = None"}, {"name": "denoising_end", "val": ": float | None = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "negative_prompt_2", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "ip_adapter_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "ip_adapter_image_embeds", "val": ": list[torch.Tensor] | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": tuple = None"}, {"name": "crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "target_size", "val": ": tuple = None"}, {"name": "negative_original_size", "val": ": tuple[int, int] | None = None"}, {"name": "negative_crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "negative_target_size", "val": ": tuple[int, int] | None = None"}, {"name": "aesthetic_score", "val": ": float = 6.0"}, {"name": "negative_aesthetic_score", "val": ": float = 2.5"}, {"name": "clip_skip", "val": ": int | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "**kwargs", "val": ""}]- **prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **image** (`PIL.Image.Image`) --
  `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
  be masked out with `mask_image` and repainted according to `prompt`.
- **mask_image** (`PIL.Image.Image`) --
  `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
  repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
  to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
  instead of 3, so the expected shape would be `(B, H, W, 1)`.
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **padding_mask_crop** (`int`, *optional*, defaults to `None`) --
  The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
  image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
  with the same aspect ration of the image and contains all masked area, and then expand that area based
  on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
  resizing to the original image size for inpainting. This is useful when the masked area is small while
  the image is large and contain information irrelevant for inpainting, such as background.
- **strength** (`float`, *optional*, defaults to 0.9999) --
  Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
  between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
  `strength`. The number of denoising steps depends on the amount of noise initially added. When
  `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
  iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
  portion of the reference `image`. Note that in the case of `denoising_start` being declared as an
  integer, the value of `strength` will be ignored.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **timesteps** (`list[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`list[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_start** (`float`, *optional*) --
  When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
  bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
  it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
  strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
  is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
  denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
  final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
  forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  Guidance scale as defined in [Classifier-Free Diffusion
  Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
  of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
  `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
  the text `prompt`, usually at the expense of lower image quality.
- **negative_prompt** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `list[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
  provided, text embeddings will be generated from `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
  argument.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`list[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to [schedulers.DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), will be ignored for others.
- **generator** (`torch.Generator`, *optional*) --
  One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
  to make generation deterministic.
- **latents** (`torch.Tensor`, *optional*) --
  Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
  tensor will be generated by sampling using the supplied random `generator`.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a [StableDiffusionPipelineOutput](/docs/diffusers/v0.38.0/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **aesthetic_score** (`float`, *optional*, defaults to 6.0) --
  Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_aesthetic_score** (`float`, *optional*, defaults to 2.5) --
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
  simulate an aesthetic score of the generated image by influencing the negative text condition.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
  DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
  list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- **callback_on_step_end_tensor_inputs** (`list`, *optional*) --
  The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
  will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
  `._callback_tensor_inputs` attribute of your pipeline class.0`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple``~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. `tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionXLInpaintPipeline
>>> from diffusers.utils import load_image

>>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-base-1.0",
...     torch_dtype=torch.float16,
...     variant="fp16",
...     use_safetensors=True,
... )
>>> pipe.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).convert("RGB")
>>> mask_image = load_image(mask_url).convert("RGB")

>>> prompt = "A majestic tiger sitting on a bench"
>>> image = pipe(
...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
... ).images[0]
```

**Parameters:**

vae ([AutoencoderKL](/docs/diffusers/v0.38.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) : Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.

text_encoder (`CLIPTextModel`) : Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.

text_encoder_2 (` CLIPTextModelWithProjection`) : Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant.

tokenizer (`CLIPTokenizer`) : Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

tokenizer_2 (`CLIPTokenizer`) : Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).

unet ([UNet2DConditionModel](/docs/diffusers/v0.38.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) : Conditional U-Net architecture to denoise the encoded image latents.

scheduler ([SchedulerMixin](/docs/diffusers/v0.38.0/en/api/schedulers/overview#diffusers.SchedulerMixin)) : A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [DDIMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/v0.38.0/en/api/schedulers/pndm#diffusers.PNDMScheduler).

requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`) : Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`.

force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`) : Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`.

add_watermarker (`bool`, *optional*) : Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

**Returns:**

``~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple``

`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. `tuple. When returning a tuple, the first element is a list with the generated images.
#### encode_prompt[[diffusers.StableDiffusionXLInpaintPipeline.encode_prompt]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py#L405)

Encodes the prompt into text encoder hidden states.

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : prompt to be encoded

prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders

device : (`torch.device`): torch device

num_images_per_prompt (`int`) : number of images that should be generated per prompt

do_classifier_free_guidance (`bool`) : whether to use classifier free guidance or not

negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).

negative_prompt_2 (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders

prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.

negative_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.

pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.

negative_pooled_prompt_embeds (`torch.Tensor`, *optional*) : Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.

lora_scale (`float`, *optional*) : A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

clip_skip (`int`, *optional*) : Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
#### get_guidance_scale_embedding[[diffusers.StableDiffusionXLInpaintPipeline.get_guidance_scale_embedding]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py#L1009)

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

**Parameters:**

w (`torch.Tensor`) : Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.

embedding_dim (`int`, *optional*, defaults to 512) : Dimension of the embeddings to generate.

dtype (`torch.dtype`, *optional*, defaults to `torch.float32`) : Data type of the generated embeddings.

**Returns:**

``torch.Tensor``

Embedding vectors with shape `(len(w), embedding_dim)`.