#
Stable diffusion pipelines

Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. It’s trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.

Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. You can learn more details about it in the specific pipeline for latent diffusion that is part of 🤗 Diffusers.

For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, please refer to the official launch announcement post and this section of our own blog post.

Tips:

- To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook:

Overview:

Pipeline | Tasks | Colab | Demo
--- | --- | --- | ---
pipeline_stable_diffusion.py | Text-to-Image Generation |  | 🤗 Stable Diffusion
pipeline_stable_diffusion_img2img.py | Image-to-Image Text-Guided Generation |  | 🤗 Diffuse the Rest
pipeline_stable_diffusion_inpaint.py | Experimental – Text-Guided Image Inpainting |  | Coming soon

##
Tips

###
How to load and use different schedulers.

The stable diffusion pipeline uses PNDMScheduler scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler etc. To use a different scheduler, you can either change it via the ConfigMixin.from_config() method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the EulerDiscreteScheduler, you can do the following:

    >>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

    >>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
    >>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

    >>> # or
    >>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
    >>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)

###
How to convert all use cases with multiple or single pipeline

If you want to use all possible use cases in a single `DiffusionPipeline` you can either:

- Make use of the Stable Diffusion Mega Pipeline or
- Make use of the `components` functionality to instantiate all components in the most memory-efficient way:

    >>> from diffusers import (
    ...     StableDiffusionPipeline,
    ...     StableDiffusionImg2ImgPipeline,
    ...     StableDiffusionInpaintPipeline,
    ... )

    >>> text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
    >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
    >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)

    >>> # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline

##
StableDiffusionPipelineOutput

###
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- nsfw_content_detected (`List[bool]`) — List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” (nsfw) content, or `None` if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

##
StableDiffusionPipeline

###
class diffusers.StableDiffusionPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )

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 uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — 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.
- safety_checker (`StableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
- feature_extractor (`CLIPFeatureExtractor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

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

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.)

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image generation.
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- 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.FloatTensor`, 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 random `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding.

When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

##
StableDiffusionImg2ImgPipeline

###
class diffusers.StableDiffusionImg2ImgPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )

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 uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — 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.
- safety_checker (`StableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
- feature_extractor (`CLIPFeatureExtractor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

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

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.)

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] image: typing.Union[torch.FloatTensor, PIL.Image.Image] = None strength: float = 0.8 num_inference_steps: typing.Optional[int] = 50 guidance_scale: typing.Optional[float] = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: typing.Optional[float] = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 **kwargs ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image generation.
- image (`torch.FloatTensor` or `PIL.Image.Image`) — `Image`, or tensor representing an image batch, that will be used as the starting point for the process.
- strength (`float`, optional, defaults to 0.8) — 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`.
- 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. This parameter will be modulated by `strength`.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- 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.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

##
StableDiffusionInpaintPipeline

###
class diffusers.StableDiffusionInpaintPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )

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 uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — 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.
- safety_checker (`StableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
- feature_extractor (`CLIPFeatureExtractor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

Pipeline for text-guided image inpainting using Stable Diffusion. This is an experimental feature.

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.)

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] image: typing.Union[torch.FloatTensor, PIL.Image.Image] mask_image: typing.Union[torch.FloatTensor, PIL.Image.Image] height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image generation.
- 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.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- 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.FloatTensor`, 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 random `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

##
StableDiffusionDepth2ImgPipeline

###
class diffusers.StableDiffusionDepth2ImgPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] depth_estimator: DPTForDepthEstimation feature_extractor: DPTFeatureExtractor )

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 uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — 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.

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

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.)

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] image: typing.Union[torch.FloatTensor, PIL.Image.Image] depth_map: typing.Optional[torch.FloatTensor] = None strength: float = 0.8 num_inference_steps: typing.Optional[int] = 50 guidance_scale: typing.Optional[float] = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: typing.Optional[float] = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image generation.
- image (`torch.FloatTensor` or `PIL.Image.Image`) — `Image`, or tensor representing an image batch, that will be used as the starting point for the process.
- strength (`float`, optional, defaults to 0.8) — 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`.
- 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. This parameter will be modulated by `strength`.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- 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.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

##
StableDiffusionImageVariationPipeline

###
class diffusers.StableDiffusionImageVariationPipeline

< source >

( vae: AutoencoderKL image_encoder: CLIPVisionModelWithProjection unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- image_encoder (`CLIPVisionModelWithProjection`) — Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of CLIP, specifically the clip-vit-large-patch14 variant.
- 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.
- safety_checker (`StableDiffusionSafetyChecker`) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
- feature_extractor (`CLIPFeatureExtractor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

Pipeline to generate variations from an input image using Stable Diffusion.

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.)

####
__call__

< source >

( image: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.FloatTensor] height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The image or images to guide the image generation. If you provide a tensor, it needs to comply with the configuration of this `CLIPFeatureExtractor`
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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.
- 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.FloatTensor`, 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 random `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

##
StableDiffusionUpscalePipeline

###
class diffusers.StableDiffusionUpscalePipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel low_res_scheduler: DDPMScheduler scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] max_noise_level: int = 350 )

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 uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
- low_res_scheduler (SchedulerMixin) — A scheduler used to add initial noise to the low res conditioning image. It must be an instance of DDPMScheduler.
- 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.

Pipeline for text-guided image super-resolution using Stable Diffusion 2.

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.)

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] image: typing.Union[torch.FloatTensor, PIL.Image.Image, typing.List[PIL.Image.Image]] num_inference_steps: int = 75 guidance_scale: float = 9.0 noise_level: int = 20 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image generation.
- image (`PIL.Image.Image` or List`PIL.Image.Image` or `torch.FloatTensor`) — `Image`, or tensor representing an image batch which will be upscaled. *
- 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.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- 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.FloatTensor`, 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 random `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of` bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the` safety_checker`.

Function invoked when calling the pipeline for generation.

####
enable_attention_slicing

< source >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `int`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
disable_attention_slicing

< source >

( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

####
enable_xformers_memory_efficient_attention

< source >

( )

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.