Diffusers documentation

Text-to-Image Generation

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.14.0).
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

Text-to-Image Generation

StableDiffusionPipeline

The Stable Diffusion model was created by the researchers and engineers from CompVis, Stability AI, runway, and LAION. The StableDiffusionPipeline is capable of generating photo-realistic images given any text input using Stable Diffusion.

The original codebase can be found here:

Available Checkpoints are:

class diffusers.StableDiffusionPipeline

< >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor 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 (CLIPImageProcessor) — 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__

< >

( prompt: typing.Union[str, typing.List[str]] = None 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 prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: 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: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → StableDiffusionPipelineOutput or tuple

Parameters

  • prompt (str or List[str], optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.
  • 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. If not defined, one has to pass negative_prompt_embeds. instead. If not defined, one has to pass negative_prompt_embeds. instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
  • 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 or List[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.
  • prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
  • negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
  • 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.
  • cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.cross_attention.

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.

Examples:

>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")

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

enable_attention_slicing

< >

( 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", maximum 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

< >

( )

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

< >

( )

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

< >

( )

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

< >

( attention_op: typing.Optional[typing.Callable] = None )

Parameters

  • attention_op (Callable, optional) — Override the default None operator for use as op argument to the memory_efficient_attention() function of xFormers.

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.

Examples:

>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
>>> # Workaround for not accepting attention shape using VAE for Flash Attention
>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)

disable_xformers_memory_efficient_attention

< >

( )

Disable memory efficient attention as implemented in xformers.

enable_vae_tiling

< >

( )

Enable tiled VAE decoding.

When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow the processing of larger images.

disable_vae_tiling

< >

( )

Disable tiled VAE decoding. If enable_vae_tiling was previously invoked, this method will go back to computing decoding in one step.

enable_model_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.

enable_sequential_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.

class diffusers.FlaxStableDiffusionPipeline

< >

( vae: FlaxAutoencoderKL text_encoder: FlaxCLIPTextModel tokenizer: CLIPTokenizer unet: FlaxUNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim_flax.FlaxDDIMScheduler, diffusers.schedulers.scheduling_pndm_flax.FlaxPNDMScheduler, diffusers.schedulers.scheduling_lms_discrete_flax.FlaxLMSDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep_flax.FlaxDPMSolverMultistepScheduler] safety_checker: FlaxStableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor dtype: dtype = <class 'jax.numpy.float32'> )

Parameters

  • vae (FlaxAutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
  • text_encoder (FlaxCLIPTextModel) — 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 (FlaxUNet2DConditionModel) — 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 FlaxDDIMScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, or FlaxDPMSolverMultistepScheduler.
  • safety_checker (FlaxStableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
  • feature_extractor (CLIPImageProcessor) — 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 FlaxDiffusionPipeline. 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__

< >

( prompt_ids: array params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] prng_seed: PRNGKeyArray num_inference_steps: int = 50 height: typing.Optional[int] = None width: typing.Optional[int] = None guidance_scale: typing.Union[float, array] = 7.5 latents: array = None neg_prompt_ids: array = None return_dict: bool = True jit: bool = False ) → FlaxStableDiffusionPipelineOutput 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.
  • latents (jnp.array, 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. tensor will ge generated by sampling using the supplied random generator.
  • jit (bool, defaults to False) — Whether to run pmap versions of the generation and safety scoring functions. NOTE: This argument exists because __call__ is not yet end-to-end pmap-able. It will be removed in a future release.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a FlaxStableDiffusionPipelineOutput instead of a plain tuple.

Returns

FlaxStableDiffusionPipelineOutput or tuple

FlaxStableDiffusionPipelineOutput 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.

Examples:

>>> import jax
>>> import numpy as np
>>> from flax.jax_utils import replicate
>>> from flax.training.common_utils import shard

>>> from diffusers import FlaxStableDiffusionPipeline

>>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
...     "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
... )

>>> prompt = "a photo of an astronaut riding a horse on mars"

>>> prng_seed = jax.random.PRNGKey(0)
>>> num_inference_steps = 50

>>> num_samples = jax.device_count()
>>> prompt = num_samples * [prompt]
>>> prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng

>>> params = replicate(params)
>>> prng_seed = jax.random.split(prng_seed, jax.device_count())
>>> prompt_ids = shard(prompt_ids)

>>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
>>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))