#
Attend and Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models

##
Overview

Attend and Excite for Stable Diffusion was proposed in Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models and provides textual attention control over the image generation.

The abstract of the paper is the following:

Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user’s intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA’s effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.

Resources

- Project Page
- Paper
- Original Code
- Demo

##
Available Pipelines:

Pipeline | Tasks | Colab | Demo
--- | --- | --- | ---
pipeline_semantic_stable_diffusion_attend_and_excite.py | Text-to-Image Generation | - | https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite

###
Usage example

    import torch
    from diffusers import StableDiffusionAttendAndExcitePipeline

    model_id = "CompVis/stable-diffusion-v1-4"
    pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
    pipe = pipe.to("cuda")

    prompt = "a cat and a frog"

    # use get_indices function to find out indices of the tokens you want to alter
    pipe.get_indices(prompt)

    token_indices = [2, 5]
    seed = 6141
    generator = torch.Generator("cuda").manual_seed(seed)

    images = pipe(
        prompt=prompt,
        token_indices=token_indices,
        guidance_scale=7.5,
        generator=generator,
        num_inference_steps=50,
        max_iter_to_alter=25,
    ).images

    image = images[0]
    image.save(f"../images/{prompt}_{seed}.png")

##
StableDiffusionAttendAndExcitePipeline

###
class diffusers.StableDiffusionAttendAndExcitePipeline

< source >

( 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 and Attend and Excite.

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]] token_indices: typing.Union[typing.List[int], typing.List[typing.List[int]]] 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: 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 max_iter_to_alter: int = 25 thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8} scale_factor: int = 20 attn_res: typing.Optional[typing.Tuple[int]] = (16, 16) ) → 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.
- token_indices (`List[int]`) — The token indices to alter with attend-and-excite.
- 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. 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.
- max_iter_to_alter (`int`, optional, defaults to `25`) — Number of denoising steps to apply attend-and-excite. The first denoising steps are where the attend-and-excite is applied. I.e. if `max_iter_to_alter` is 25 and there are a total of `30` denoising steps, the first 25 denoising steps will apply attend-and-excite and the last 5 will not apply attend-and-excite.
- thresholds (`dict`, optional, defaults to `{0 -- 0.05, 10: 0.5, 20: 0.8}`): Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.
- scale_factor (`int`, optional, default to 20) — Scale factor that controls the step size of each Attend and Excite update.
- attn_res (`tuple`, optional, default computed from width and height) — The 2D resolution of the semantic attention map.

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`. :type attention_store: object

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionAttendAndExcitePipeline

    >>> pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
    ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
    ... ).to("cuda")

    >>> prompt = "a cat and a frog"

    >>> # use get_indices function to find out indices of the tokens you want to alter
    >>> pipe.get_indices(prompt)
    {0: '<|startoftext|>', 1: 'a</w>', 2: 'cat</w>', 3: 'and</w>', 4: 'a</w>', 5: 'frog</w>', 6: '<|endoftext|>'}

    >>> token_indices = [2, 5]
    >>> seed = 6141
    >>> generator = torch.Generator("cuda").manual_seed(seed)

    >>> images = pipe(
    ...     prompt=prompt,
    ...     token_indices=token_indices,
    ...     guidance_scale=7.5,
    ...     generator=generator,
    ...     num_inference_steps=50,
    ...     max_iter_to_alter=25,
    ... ).images

    >>> image = images[0]
    >>> image.save(f"../images/{prompt}_{seed}.png")

####
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_sequential_cpu_offload

< source >

( 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` forward`method called. Note that offloading happens on a submodule basis. Memory savings are higher than with`enable_model_cpu_offload`, but performance is lower.

####
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.

####
get_indices

< source >

( prompt: str )

Utility function to list the indices of the tokens you wish to alte