Self-Attention Guidance
Last updated
Last updated
is by Susung Hong et al.
The abstract from the paper is:
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive perspective that goes beyond the traditional guidance methods. From this generalized perspective, we introduce novel condition- and training-free strategies to enhance the quality of generated images. As a simple solution, blur guidance improves the suitability of intermediate samples for their fine-scale information and structures, enabling diffusion models to generate higher quality samples with a moderate guidance scale. Improving upon this, Self-Attention Guidance (SAG) uses the intermediate self-attention maps of diffusion models to enhance their stability and efficacy. Specifically, SAG adversarially blurs only the regions that diffusion models attend to at each iteration and guides them accordingly. Our experimental results show that our SAG improves the performance of various diffusion models, including ADM, IDDPM, Stable Diffusion, and DiT. Moreover, combining SAG with conventional guidance methods leads to further improvement.
You can find additional information about Self-Attention Guidance on the , , and try it out in a or .
Make sure to check out the Schedulers to learn how to explore the tradeoff between scheduler speed and quality, and see the section to learn how to efficiently load the same components into multiple pipelines.
( vae: AutoencoderKLtext_encoder: CLIPTextModeltokenizer: CLIPTokenizerunet: UNet2DConditionModelscheduler: KarrasDiffusionSchedulerssafety_checker: StableDiffusionSafetyCheckerfeature_extractor: CLIPImageProcessorrequires_safety_checker: bool = True )
Parameters
vae () β Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder (CLIPTextModel) β Frozen text-encoder ().
tokenizer (CLIPTokenizer) β A CLIPTokenizer to tokenize text.
unet () β A UNet2DConditionModel to denoise the encoded image latents.
scheduler () β A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of , , or .
safety_checker (StableDiffusionSafetyChecker) β Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the for more details about a modelβs potential harms.
feature_extractor (CLIPImageProcessor) β A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker.
Pipeline for text-to-image generation using Stable Diffusion.
__call__
Parameters
prompt (str or List[str], optional) β The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.
height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β The height in pixels of the generated image.
width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β The width in pixels of the generated image.
num_inference_steps (int, optional, defaults to 50) β 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) β A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
sag_scale (float, optional, defaults to 0.75) β Chosen between [0, 1.0] for better quality.
negative_prompt (str or List[str], optional) β The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
num_images_per_prompt (int, optional, defaults to 1) β The number of images to generate per prompt.
latents (torch.FloatTensor, optional) β 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 is generated by sampling using the supplied random generator.
prompt_embeds (torch.FloatTensor, optional) β Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
negative_prompt_embeds (torch.FloatTensor, optional) β Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
output_type (str, optional, defaults to "pil") β The output format of the generated image. Choose between PIL.Image or np.array.
callback (Callable, optional) β A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).
callback_steps (int, optional, defaults to 1) β The frequency at which the callback function is called. If not specified, the callback is called at every step.
Returns
The call function to the pipeline for generation.
Examples:
Copied
disable_vae_slicing
( )
Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding 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.
encode_prompt
( promptdevicenum_images_per_promptdo_classifier_free_guidancenegative_prompt = Noneprompt_embeds: typing.Optional[torch.FloatTensor] = Nonenegative_prompt_embeds: typing.Optional[torch.FloatTensor] = Nonelora_scale: typing.Optional[float] = None )
Parameters
prompt (str or List[str], optional) β prompt to be encoded device β (torch.device): torch device
num_images_per_prompt (int) β number of images that should be generated per prompt
do_classifier_free_guidance (bool) β whether to use classifier free guidance or not
negative_prompt (str or List[str], optional) β The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
prompt_embeds (torch.FloatTensor, optional) β 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) β 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.
lora_scale (float, optional) β A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
Encodes the prompt into text encoder hidden states.
( 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) β List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels).
nsfw_content_detected (List[bool]) β List indicating whether the corresponding generated image contains βnot-safe-for-workβ (nsfw) content or None if safety checking could not be performed.
Output class for Stable Diffusion pipelines.
This model inherits from . Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
( prompt: typing.Union[str, typing.List[str]] = Noneheight: typing.Optional[int] = Nonewidth: typing.Optional[int] = Nonenum_inference_steps: int = 50guidance_scale: float = 7.5sag_scale: float = 0.75negative_prompt: typing.Union[str, typing.List[str], NoneType] = Nonenum_images_per_prompt: typing.Optional[int] = 1eta: float = 0.0generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = Nonelatents: typing.Optional[torch.FloatTensor] = Noneprompt_embeds: typing.Optional[torch.FloatTensor] = Nonenegative_prompt_embeds: typing.Optional[torch.FloatTensor] = Noneoutput_type: typing.Optional[str] = 'pil'return_dict: bool = Truecallback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = Nonecallback_steps: typing.Optional[int] = 1cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) β or tuple
eta (float, optional, defaults to 0.0) β Corresponds to parameter eta (Ξ·) from the paper. Only applies to the , and is ignored in other schedulers.
generator (torch.Generator or List[torch.Generator], optional) β A to make generation deterministic.
return_dict (bool, optional, defaults to True) β Whether or not to return a instead of a plain tuple.
cross_attention_kwargs (dict, optional) β A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in .
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains βnot-safe-for-workβ (nsfw) content.