Stable diffusion pipelines (original) (raw)

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 pass the scheduler argument to from_pretrained method of the pipeline. For example, to use the EulerDiscreteScheduler, you can do the following:

from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

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

How to conver 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, ... )

img2text = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") img2img = StableDiffusionImg2ImgPipeline(**img2text.components) inpaint = StableDiffusionInpaintPipeline(**img2text.components)

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] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor )

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 ofCLIP, specifically the clip-vit-large-patch14 variant.
• tokenizer (CLIPTokenizer) — Tokenizer of classCLIPTokenizer.
• 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 latens. Can be one ofDDIMScheduler, 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: int = 512 width: int = 512 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.Optional[torch._C.Generator] = 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 **kwargs ) → StableDiffusionPipelineOutput or tuple

Parameters

• prompt (str or List[str]) — The prompt or prompts to guide the image generation.
• height (int, optional, defaults to 512) — The height in pixels of the generated image.
• width (int, optional, defaults to 512) — 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 toschedulers.DDIMScheduler, will be ignored for others.
• generator (torch.Generator, optional) — A torch generator 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 betweenPIL: 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.

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

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] safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor )

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 ofCLIP, specifically the clip-vit-large-patch14 variant.
• tokenizer (CLIPTokenizer) — Tokenizer of classCLIPTokenizer.
• 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 latens. Can be one ofDDIMScheduler, 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]] init_image: typing.Union[torch.FloatTensor, PIL.Image.Image] 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.Optional[torch._C.Generator] = 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.
• init_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 init_image. Must be between 0 and 1.init_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 innum_inference_steps. A value of 1, therefore, essentially ignores init_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 toschedulers.DDIMScheduler, will be ignored for others.
• generator (torch.Generator, optional) — A torch generator to make generation deterministic.
• output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose betweenPIL: 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.

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

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: CLIPImageProcessor )

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 ofCLIP, specifically the clip-vit-large-patch14 variant.
• tokenizer (CLIPTokenizer) — Tokenizer of classCLIPTokenizer.
• 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 latens. Can be one ofDDIMScheduler, 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: int = 512 width: int = 512 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.Optional[torch._C.Generator] = 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 **kwargs ) → 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 512) — The height in pixels of the generated image.
• width (int, optional, defaults to 512) — 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 toschedulers.DDIMScheduler, will be ignored for others.
• generator (torch.Generator, optional) — A torch generator 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 betweenPIL: 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.

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