Attend-and-Excite
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/pipelines/attend_and_excite
原始地址:https://huggingface.co/docs/diffusers/api/pipelines/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:
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.
You can find additional information about Attend-and-Excite on the project page , the original codebase , or try it out in a demo .
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.
StableDiffusionAttendAndExcitePipeline
class
diffusers.
StableDiffusionAttendAndExcitePipeline
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L173)
(
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 ( clip-vit-large-patch14 ).
- tokenizer
(
CLIPTokenizer
) —
A
CLIPTokenizer
to tokenize text. - unet
(
UNet2DConditionModel
) —
A
UNet2DConditionModel
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 more details about a model’s potential harms. - feature_extractor
(
CLIPImageProcessor
) —
A
CLIPImageProcessor
to extract features from generated images; used as inputs to thesafety_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 implemented for all pipelines (downloading, saving, running on a particular device, etc.).
__call__
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L742)
(
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[typing.List[str], 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)
clip_skip
: typing.Optional[int] = None
)
→
export const metadata = 'undefined';
StableDiffusionPipelineOutput
or
tuple
Parameters
- prompt
(
str
orList[str]
, optional ) — The prompt or prompts to guide image generation. If not defined, you need to passprompt_embeds
. - token_indices
(
List[int]
) — The token indices to alter with attend-and-excite. - height
(
int
, optional , defaults toself.unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. - width
(
int
, optional , defaults toself.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) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. - negative_prompt
(
str
orList[str]
, optional ) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 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) from the DDIM paper. Only applies to the DDIMScheduler , and is ignored in other schedulers. - generator
(
torch.Generator
orList[torch.Generator]
, optional ) — Atorch.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 is generated by sampling using the supplied randomgenerator
. - prompt_embeds
(
torch.FloatTensor
, optional ) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from theprompt
input argument. - negative_prompt_embeds
(
torch.FloatTensor
, optional ) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided,negative_prompt_embeds
are generated from thenegative_prompt
input argument. - output_type
(
str
, optional , defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.Image
ornp.array
. - return_dict
(
bool
, optional , defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. - callback
(
Callable
, optional ) — A function that calls everycallback_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) — The frequency at which thecallback
function is called. If not specified, the callback is called at every step. - cross_attention_kwargs
(
dict
, optional ) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined inself.processor
. - max_iter_to_alter
(
int
, optional , defaults to25
) — Number of denoising steps to apply attend-and-excite. Themax_iter_to_alter
denoising steps are when attend-and-excite is applied. For example, ifmax_iter_to_alter
is25
and there are a total of30
denoising steps, the first25
denoising steps applies attend-and-excite and the last5
will not. - 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 to control 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. - clip_skip
(
int
, optional ) — 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.
Returns
export const metadata = 'undefined';
StableDiffusionPipelineOutput
or
tuple
export const metadata = 'undefined';
If
return_dict
is
True
,
StableDiffusionPipelineOutput
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
bool
s indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.
The call function to 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
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L254)
(
)
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
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L246)
(
)
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
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L295)
(
prompt
device
num_images_per_prompt
do_classifier_free_guidance
negative_prompt
= None
prompt_embeds
: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds
: typing.Optional[torch.FloatTensor] = None
lora_scale
: typing.Optional[float] = None
clip_skip
: typing.Optional[int] = None
)
Parameters
- prompt
(
str
orList[str]
, optional ) — prompt to be encoded device — (torch.device
): torch device - num_images_per_prompt
(
int
) — number of images that should be generated per prompt - do_classifier_free_guidance
(
bool
) — whether to use classifier free guidance or not - negative_prompt
(
str
orList[str]
, optional ) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 fromprompt
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 fromnegative_prompt
input argument. - lora_scale
(
float
, optional ) — A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. - clip_skip
(
int
, optional ) — 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.
Encodes the prompt into text encoder hidden states.
get_indices
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py#L736)
(
prompt
: str
)
Utility function to list the indices of the tokens you wish to alte
StableDiffusionPipelineOutput
class
diffusers.pipelines.stable_diffusion.
StableDiffusionPipelineOutput
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/stable_diffusion/pipeline_output.py#L11)
(
images
: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]
nsfw_content_detected
: typing.Optional[typing.List[bool]]
)
Parameters
- images
(
List[PIL.Image.Image]
ornp.ndarray
) — List of denoised PIL images of lengthbatch_size
or NumPy array of shape(batch_size, height, width, num_channels)
. - nsfw_content_detected
(
List[bool]
) — List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content orNone
if safety checking could not be performed.
Output class for Stable Diffusion pipelines.