Stable Diffusion XL
Stable Diffusion XL (SDXL) was proposed in SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis 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 because the solver becomes numerically unstable. To fix this issue, take a look at this PR which recommends for ODE/SDE solvers:
- set
use_karras_sigmas=True
orlu_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)
- set
- 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.
- 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
, andnegative_target_size
to negatively condition the model on image resolution and cropping parameters.
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 guide.
Check out the Stability AI Hub organization for the official base and refiner model checkpoints!
StableDiffusionXLPipeline
class diffusers.StableDiffusionXLPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None force_zeros_for_empty_prompt: bool = True add_watermarker: Optional = None )
Parameters
- vae (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, specifically the clip-vit-large-patch14 variant. - text_encoder_2 (
CLIPTextModelWithProjection
) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant. - tokenizer (
CLIPTokenizer
) — Tokenizer of class CLIPTokenizer. - tokenizer_2 (
CLIPTokenizer
) — Second Tokenizer of class CLIPTokenizer. - unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or 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 ofstabilityai/stable-diffusion-xl-base-1-0
. - add_watermarker (
bool
, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from 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() for loading textual inversion embeddings
- from_single_file() for loading
.ckpt
files - load_lora_weights() for loading LoRA weights
- save_lora_weights() for saving LoRA weights
- load_ip_adapter() for loading IP Adapters
__call__
< source >( prompt: Union = None prompt_2: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = None sigmas: List = None denoising_end: Optional = None guidance_scale: float = 5.0 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Optional = None crops_coords_top_left: Tuple = (0, 0) target_size: Optional = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None clip_skip: Optional = None callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
or tuple
Parameters
- prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_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 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 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 atimesteps
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 asigmas
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 - guidance_scale (
float
, optional, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. - generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) 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 ge generated by sampling using the supplied randomgenerator
. - 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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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 ifdo_classifier_free_guidance
is set toTrue
. If not provided, embeddings are computed from theip_adapter_image
input argument. - output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. - return_dict (
bool
, optional, defaults toTrue
) — 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 theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. - guidance_rescale (
float
, optional, defaults to 0.0) — Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawedguidance_scale
is defined asφ
in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR. - original_size (
Tuple[int]
, optional, defaults to (1024, 1024)) — Iforiginal_size
is not the same astarget_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. - 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 positioncrops_coords_top_left
downwards. Favorable, well-centered images are usually achieved by settingcrops_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. - 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. - 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. 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. 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 thetarget_size
for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of 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 ofPipelineCallback
orMultiPipelineCallbacks
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 bycallback_on_step_end_tensor_inputs
. - callback_on_step_end_tensor_inputs (
List
, optional) — The list of tensor inputs for thecallback_on_step_end
function. The tensors specified in the list will be passed ascallback_kwargs
argument. You will only be able to include variables listed in the._callback_tensor_inputs
attribute of your pipeline class.
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> 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]
encode_prompt
< source >( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )
Parameters
- prompt (
str
orList[str]
, optional) — prompt to be encoded - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_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
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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.
Encodes the prompt into text encoder hidden states.
get_guidance_scale_embedding
< source >( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) → torch.Tensor
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 totorch.float32
) — Data type of the generated embeddings.
Returns
torch.Tensor
Embedding vectors with shape (len(w), embedding_dim)
.
StableDiffusionXLImg2ImgPipeline
class diffusers.StableDiffusionXLImg2ImgPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None requires_aesthetics_score: bool = False force_zeros_for_empty_prompt: bool = True add_watermarker: Optional = None )
Parameters
- vae (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, specifically the clip-vit-large-patch14 variant. - text_encoder_2 (
CLIPTextModelWithProjection
) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant. - tokenizer (
CLIPTokenizer
) — Tokenizer of class CLIPTokenizer. - tokenizer_2 (
CLIPTokenizer
) — Second Tokenizer of class CLIPTokenizer. - unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. - requires_aesthetics_score (
bool
, optional, defaults to"False"
) — Whether theunet
requires anaesthetic_score
condition to be passed during inference. Also see the config ofstabilityai/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 ofstabilityai/stable-diffusion-xl-base-1-0
. - add_watermarker (
bool
, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from 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() for loading textual inversion embeddings
- from_single_file() for loading
.ckpt
files - load_lora_weights() for loading LoRA weights
- save_lora_weights() for saving LoRA weights
- load_ip_adapter() for loading IP Adapters
__call__
< source >( prompt: Union = None prompt_2: Union = None image: Union = None strength: float = 0.3 num_inference_steps: int = 50 timesteps: List = None sigmas: List = None denoising_start: Optional = None denoising_end: Optional = None guidance_scale: float = 5.0 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Tuple = None crops_coords_top_left: Tuple = (0, 0) target_size: Tuple = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: Optional = None callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
Parameters
- prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_encoder_2
. If not defined,prompt
is used in both text-encoders - image (
torch.Tensor
orPIL.Image.Image
ornp.ndarray
orList[torch.Tensor]
orList[PIL.Image.Image]
orList[np.ndarray]
) — The image(s) to modify with the pipeline. - strength (
float
, optional, defaults to 0.3) — Conceptually, indicates how much to transform the referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. Note that in the case ofdenoising_start
being declared as an integer, the value ofstrength
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 atimesteps
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 asigmas
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 passedimage
is a partly denoised image. Note that when this is specified, strength will be ignored. Thedenoising_start
parameter is particularly beneficial when this pipeline is integrated into a “Mixture of Denoisers” multi-pipeline setup, as detailed in 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 hasdenoising_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. - guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. - generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) 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 ge generated by sampling using the supplied randomgenerator
. - 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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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 ifdo_classifier_free_guidance
is set toTrue
. If not provided, embeddings are computed from theip_adapter_image
input argument. - output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. - return_dict (
bool
, optional, defaults toTrue
) — 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 theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. - guidance_rescale (
float
, optional, defaults to 0.0) — Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawedguidance_scale
is defined asφ
in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR. - original_size (
Tuple[int]
, optional, defaults to (1024, 1024)) — Iforiginal_size
is not the same astarget_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. - 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 positioncrops_coords_top_left
downwards. Favorable, well-centered images are usually achieved by settingcrops_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. - 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. - 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. 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. 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 thetarget_size
for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of 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. - 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. 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 ofPipelineCallback
orMultiPipelineCallbacks
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 bycallback_on_step_end_tensor_inputs
. - callback_on_step_end_tensor_inputs (
List
, optional) — The list of tensor inputs for thecallback_on_step_end
function. The tensors specified in the list will be passed ascallback_kwargs
argument. You will only be able to include variables listed in the._callback_tensor_inputs
attribute of your pipeline class.
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> 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]
encode_prompt
< source >( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )
Parameters
- prompt (
str
orList[str]
, optional) — prompt to be encoded - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_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
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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.
Encodes the prompt into text encoder hidden states.
get_guidance_scale_embedding
< source >( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) → torch.Tensor
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 totorch.float32
) — Data type of the generated embeddings.
Returns
torch.Tensor
Embedding vectors with shape (len(w), embedding_dim)
.
StableDiffusionXLInpaintPipeline
class diffusers.StableDiffusionXLInpaintPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None requires_aesthetics_score: bool = False force_zeros_for_empty_prompt: bool = True add_watermarker: Optional = None )
Parameters
- vae (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, specifically the clip-vit-large-patch14 variant. - text_encoder_2 (
CLIPTextModelWithProjection
) — Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant. - tokenizer (
CLIPTokenizer
) — Tokenizer of class CLIPTokenizer. - tokenizer_2 (
CLIPTokenizer
) — Second Tokenizer of class CLIPTokenizer. - unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unet
to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. - requires_aesthetics_score (
bool
, optional, defaults to"False"
) — Whether theunet
requires a aesthetic_score condition to be passed during inference. Also see the config ofstabilityai/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 ofstabilityai/stable-diffusion-xl-base-1-0
. - add_watermarker (
bool
, optional) — Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits from 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() for loading textual inversion embeddings
- from_single_file() for loading
.ckpt
files - load_lora_weights() for loading LoRA weights
- save_lora_weights() for saving LoRA weights
- load_ip_adapter() for loading IP Adapters
__call__
< source >( prompt: Union = None prompt_2: Union = None image: Union = None mask_image: Union = None masked_image_latents: Tensor = None height: Optional = None width: Optional = None padding_mask_crop: Optional = None strength: float = 0.9999 num_inference_steps: int = 50 timesteps: List = None sigmas: List = None denoising_start: Optional = None denoising_end: Optional = None guidance_scale: float = 7.5 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Tuple = None crops_coords_top_left: Tuple = (0, 0) target_size: Tuple = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: Optional = None callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
Parameters
- prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_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 withmask_image
and repainted according toprompt
. - mask_image (
PIL.Image.Image
) —Image
, or tensor representing an image batch, to maskimage
. White pixels in the mask will be repainted, while black pixels will be preserved. Ifmask_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 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 and checkpoints that are not specifically fine-tuned on low resolutions. - padding_mask_crop (
int
, optional, defaults toNone
) — The size of margin in the crop to be applied to the image and masking. IfNone
, no crop is applied to image and mask_image. Ifpadding_mask_crop
is notNone
, 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 onpadding_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 referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignores the masked portion of the referenceimage
. Note that in the case ofdenoising_start
being declared as an integer, the value ofstrength
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 atimesteps
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 asigmas
argument in theirset_timesteps
method. If not defined, the default behavior whennum_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 passedimage
is a partly denoised image. Note that when this is specified, strength will be ignored. Thedenoising_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. - 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 hasdenoising_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. - guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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 ifdo_classifier_free_guidance
is set toTrue
. If not provided, embeddings are computed from theip_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://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. - generator (
torch.Generator
, optional) — One or a list of torch generator(s) 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 ge generated by sampling using the supplied randomgenerator
. - output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. - cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. - original_size (
Tuple[int]
, optional, defaults to (1024, 1024)) — Iforiginal_size
is not the same astarget_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. - 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 positioncrops_coords_top_left
downwards. Favorable, well-centered images are usually achieved by settingcrops_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. - 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. - 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. 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. 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 thetarget_size
for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of 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. - 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. 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 ofPipelineCallback
orMultiPipelineCallbacks
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 bycallback_on_step_end_tensor_inputs
. - callback_on_step_end_tensor_inputs (
List
, optional) — The list of tensor inputs for thecallback_on_step_end
function. The tensors specified in the list will be passed ascallback_kwargs
argument. You will only be able to include variables listed in the._callback_tensor_inputs
attribute of your pipeline class.
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> 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]
encode_prompt
< source >( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )
Parameters
- prompt (
str
orList[str]
, optional) — prompt to be encoded - prompt_2 (
str
orList[str]
, optional) — The prompt or prompts to be sent to thetokenizer_2
andtext_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
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt_2 (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation to be sent totokenizer_2
andtext_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 fromprompt
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 fromnegative_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 fromprompt
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 fromnegative_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.
Encodes the prompt into text encoder hidden states.
get_guidance_scale_embedding
< source >( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) → torch.Tensor
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 totorch.float32
) — Data type of the generated embeddings.
Returns
torch.Tensor
Embedding vectors with shape (len(w), embedding_dim)
.