Attend-and-Excite¶
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 image generation.
The abstract from the paper is:
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of catastrophic neglect, where the model fails to generate one or more of the subjects from the input prompt. Moreover, we find that in some cases the model also fails to correctly bind attributes (e.g., colors) to their corresponding subjects. To help mitigate these failure cases, we introduce the concept of Generative Semantic Nursing (GSN), where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness of the generated images. Using an attention-based formulation of GSN, dubbed Attend-and-Excite, we guide the model to refine the cross-attention units to attend to all subject tokens in the text prompt and strengthen - or excite - their activations, encouraging the model to generate all subjects described in the text prompt. We compare our approach to alternative approaches and demonstrate that it conveys the desired concepts more faithfully across a range of text prompts.
You can find additional information about Attend-and-Excite on the project page, the original codebase, or try it out in a demo.
Tip
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
mindone.diffusers.StableDiffusionAttendAndExcitePipeline
¶
Bases: DiffusionPipeline
, StableDiffusionMixin
, TextualInversionLoaderMixin
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
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods
- [
~loaders.TextualInversionLoaderMixin.load_textual_inversion
] for loading textual inversion embeddings
PARAMETER | DESCRIPTION |
---|---|
vae |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
TYPE:
|
text_encoder |
Frozen text-encoder (clip-vit-large-patch14).
TYPE:
|
tokenizer |
A
TYPE:
|
unet |
A
TYPE:
|
scheduler |
A scheduler to be used in combination with
TYPE:
|
safety_checker |
Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model's potential harms.
TYPE:
|
feature_extractor |
A
TYPE:
|
Source code in mindone/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py
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|
mindone.diffusers.StableDiffusionAttendAndExcitePipeline.__call__(prompt, token_indices, height=None, width=None, num_inference_steps=50, guidance_scale=7.5, negative_prompt=None, num_images_per_prompt=1, eta=0.0, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, output_type='pil', return_dict=False, callback=None, callback_steps=1, cross_attention_kwargs=None, max_iter_to_alter=25, thresholds={0: 0.05, 10: 0.5, 20: 0.8}, scale_factor=20, attn_res=(16, 16), clip_skip=None)
¶
The call function to the pipeline for generation.
PARAMETER | DESCRIPTION |
---|---|
prompt |
The prompt or prompts to guide image generation. If not defined, you need to pass
TYPE:
|
token_indices |
The token indices to alter with attend-and-excite.
TYPE:
|
height |
The height in pixels of the generated image.
TYPE:
|
width |
The width in pixels of the generated image.
TYPE:
|
num_inference_steps |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
TYPE:
|
guidance_scale |
A higher guidance scale value encourages the model to generate images closely linked to the text
TYPE:
|
negative_prompt |
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass
TYPE:
|
num_images_per_prompt |
The number of images to generate per prompt.
TYPE:
|
eta |
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the [
TYPE:
|
generator |
A
TYPE:
|
latents |
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
TYPE:
|
prompt_embeds |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the
TYPE:
|
negative_prompt_embeds |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided,
TYPE:
|
output_type |
The output format of the generated image. Choose between
TYPE:
|
return_dict |
Whether or not to return a [
TYPE:
|
callback |
A function that calls every
TYPE:
|
callback_steps |
The frequency at which the
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the [
TYPE:
|
max_iter_to_alter |
Number of denoising steps to apply attend-and-excite. The
TYPE:
|
thresholds |
0.05, 10: 0.5, 20: 0.8}`): Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.
TYPE:
|
scale_factor |
Scale factor to control the step size of each attend-and-excite update.
TYPE:
|
attn_res |
The 2D resolution of the semantic attention map.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
Source code in mindone/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py
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|
mindone.diffusers.StableDiffusionAttendAndExcitePipeline.encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, lora_scale=None, clip_skip=None)
¶
Encodes the prompt into text encoder hidden states.
PARAMETER | DESCRIPTION |
---|---|
prompt |
prompt to be encoded
TYPE:
|
num_images_per_prompt |
number of images that should be generated per prompt
TYPE:
|
do_classifier_free_guidance |
whether to use classifier free guidance or not
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
prompt_embeds |
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
TYPE:
|
negative_prompt_embeds |
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
TYPE:
|
lora_scale |
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
Source code in mindone/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py
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|
mindone.diffusers.StableDiffusionAttendAndExcitePipeline.get_indices(prompt)
¶
Utility function to list the indices of the tokens you wish to alte
Source code in mindone/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py
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|
mindone.diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
dataclass
¶
Bases: BaseOutput
Output class for Stable Diffusion pipelines.
Source code in mindone/diffusers/pipelines/stable_diffusion/pipeline_output.py
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