# Copyright 2024 The HuggingFace Team. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import inspect

from typing import Any, Callable, Dict, List, Optional, Union

import torch

from packaging import version

from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers.configuration_utils import FrozenDict

from diffusers.image_processor import VaeImageProcessor

from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin

from diffusers.models import AutoencoderKL, UNet2DConditionModel

from diffusers.models.lora import adjust_lora_scale_text_encoder

from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin

from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput

from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

from diffusers.schedulers import KarrasDiffusionSchedulers

from diffusers.utils import (

deprecate,

logging,

)

from diffusers.utils.torch_utils import randn_tensor

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):

"""

Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and

Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4

"""

std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)

std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)

# rescale the results from guidance (fixes overexposure)

noise_pred_rescaled = noise_cfg * (std_text / std_cfg)

# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images

noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg

return noise_cfg

class InstaFlowPipeline(

DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin

):

r"""

Pipeline for text-to-image generation using Rectified Flow and Euler discretization.

This customized pipeline is based on StableDiffusionPipeline from the official Diffusers library (0.21.4)

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

- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights

- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights

- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files

Args:

vae ([`AutoencoderKL`]):

Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.

text_encoder ([`~transformers.CLIPTextModel`]):

Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).

tokenizer ([`~transformers.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](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details

about a model's potential harms.

feature_extractor ([`~transformers.CLIPImageProcessor`]):

A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

"""

model_cpu_offload_seq = "text_encoder->unet->vae"

_optional_components = ["safety_checker", "feature_extractor"]

_exclude_from_cpu_offload = ["safety_checker"]

def __init__(

self,

vae: AutoencoderKL,

text_encoder: CLIPTextModel,

tokenizer: CLIPTokenizer,

unet: UNet2DConditionModel,

scheduler: KarrasDiffusionSchedulers,

safety_checker: StableDiffusionSafetyChecker,

feature_extractor: CLIPImageProcessor,

requires_safety_checker: bool = True,

):

super().__init__()

if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:

deprecation_message = (

f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"

f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "

"to update the config accordingly as leaving `steps_offset` might led to incorrect results"

" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"

" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"

" file"

)

deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)

new_config = dict(scheduler.config)

new_config["steps_offset"] = 1

scheduler._internal_dict = FrozenDict(new_config)

if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:

deprecation_message = (

f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."

" `clip_sample` should be set to False in the configuration file. Please make sure to update the"

" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"

" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"

" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"

)

deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)

new_config = dict(scheduler.config)

new_config["clip_sample"] = False

scheduler._internal_dict = FrozenDict(new_config)

if safety_checker is None and requires_safety_checker:

logger.warning(

f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"

" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"

" results in services or applications open to the public. Both the diffusers team and Hugging Face"

" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"

" it only for use-cases that involve analyzing network behavior or auditing its results. For more"

" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."

)

if safety_checker is not None and feature_extractor is None:

raise ValueError(

"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"

" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."

)

is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(

version.parse(unet.config._diffusers_version).base_version

) < version.parse("0.9.0.dev0")

is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64

if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:

deprecation_message = (

"The configuration file of the unet has set the default `sample_size` to smaller than"

" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"

" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"

" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"

" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"

" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"

" in the config might lead to incorrect results in future versions. If you have downloaded this"

" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

" the `unet/config.json` file"

)

deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)

new_config = dict(unet.config)

new_config["sample_size"] = 64

unet._internal_dict = FrozenDict(new_config)

self.register_modules(

vae=vae,

text_encoder=text_encoder,

tokenizer=tokenizer,

unet=unet,

scheduler=scheduler,

safety_checker=safety_checker,

feature_extractor=feature_extractor,

)

self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

self.register_to_config(requires_safety_checker=requires_safety_checker)

def _encode_prompt(

self,

prompt,

device,

num_images_per_prompt,

do_classifier_free_guidance,

negative_prompt=None,

prompt_embeds: Optional[torch.FloatTensor] = None,

negative_prompt_embeds: Optional[torch.FloatTensor] = None,

lora_scale: Optional[float] = None,

):

deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."

deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

prompt_embeds_tuple = self.encode_prompt(

prompt=prompt,

device=device,

num_images_per_prompt=num_images_per_prompt,

do_classifier_free_guidance=do_classifier_free_guidance,

negative_prompt=negative_prompt,

prompt_embeds=prompt_embeds,

negative_prompt_embeds=negative_prompt_embeds,

lora_scale=lora_scale,

)

# concatenate for backwards comp

prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

return prompt_embeds

def encode_prompt(

self,

prompt,

device,

num_images_per_prompt,

do_classifier_free_guidance,

negative_prompt=None,

prompt_embeds: Optional[torch.FloatTensor] = None,

negative_prompt_embeds: Optional[torch.FloatTensor] = None,

lora_scale: Optional[float] = None,

):

r"""

Encodes the prompt into text encoder hidden states.

Args:

prompt (`str` or `List[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` 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`).

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.

lora_scale (`float`, *optional*):

A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

"""

# set lora scale so that monkey patched LoRA

# function of text encoder can correctly access it

if lora_scale is not None and isinstance(self, LoraLoaderMixin):

self._lora_scale = lora_scale

# dynamically adjust the LoRA scale

adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)

if prompt is not None and isinstance(prompt, str):

batch_size = 1

elif prompt is not None and isinstance(prompt, list):

batch_size = len(prompt)

else:

batch_size = prompt_embeds.shape[0]

if prompt_embeds is None:

# textual inversion: procecss multi-vector tokens if necessary

if isinstance(self, TextualInversionLoaderMixin):

prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

text_inputs = self.tokenizer(

prompt,

padding="max_length",

max_length=self.tokenizer.model_max_length,

truncation=True,

return_tensors="pt",

)

text_input_ids = text_inputs.input_ids

untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(

text_input_ids, untruncated_ids

):

removed_text = self.tokenizer.batch_decode(

untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]

)

logger.warning(

"The following part of your input was truncated because CLIP can only handle sequences up to"

f" {self.tokenizer.model_max_length} tokens: {removed_text}"

)

if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:

attention_mask = text_inputs.attention_mask.to(device)

else:

attention_mask = None

prompt_embeds = self.text_encoder(

text_input_ids.to(device),

attention_mask=attention_mask,

)

prompt_embeds = prompt_embeds[0]

if self.text_encoder is not None:

prompt_embeds_dtype = self.text_encoder.dtype

elif self.unet is not None:

prompt_embeds_dtype = self.unet.dtype

else:

prompt_embeds_dtype = prompt_embeds.dtype

prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

bs_embed, seq_len, _ = prompt_embeds.shape

# duplicate text embeddings for each generation per prompt, using mps friendly method

prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)

prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

# get unconditional embeddings for classifier free guidance

if do_classifier_free_guidance and negative_prompt_embeds is None:

uncond_tokens: List[str]

if negative_prompt is None:

uncond_tokens = [""] * batch_size

elif prompt is not None and type(prompt) is not type(negative_prompt):

raise TypeError(

f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="

f" {type(prompt)}."

)

elif isinstance(negative_prompt, str):

uncond_tokens = [negative_prompt]

elif batch_size != len(negative_prompt):

raise ValueError(

f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"

f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"

" the batch size of `prompt`."

)

else:

uncond_tokens = negative_prompt

# textual inversion: procecss multi-vector tokens if necessary

if isinstance(self, TextualInversionLoaderMixin):

uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

max_length = prompt_embeds.shape[1]

uncond_input = self.tokenizer(

uncond_tokens,

padding="max_length",

max_length=max_length,

truncation=True,

return_tensors="pt",

)

if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:

attention_mask = uncond_input.attention_mask.to(device)

else:

attention_mask = None

negative_prompt_embeds = self.text_encoder(

uncond_input.input_ids.to(device),

attention_mask=attention_mask,

)

negative_prompt_embeds = negative_prompt_embeds[0]

if do_classifier_free_guidance:

# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

seq_len = negative_prompt_embeds.shape[1]

negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)

negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

return prompt_embeds, negative_prompt_embeds

def run_safety_checker(self, image, device, dtype):

if self.safety_checker is None:

has_nsfw_concept = None

else:

if torch.is_tensor(image):

feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")

else:

feature_extractor_input = self.image_processor.numpy_to_pil(image)

safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)

image, has_nsfw_concept = self.safety_checker(

images=image, clip_input=safety_checker_input.pixel_values.to(dtype)

)

return image, has_nsfw_concept

def decode_latents(self, latents):

deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"

deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)

latents = 1 / self.vae.config.scaling_factor * latents

image = self.vae.decode(latents, return_dict=False)[0]

image = (image / 2 + 0.5).clamp(0, 1)

# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16

image = image.cpu().permute(0, 2, 3, 1).float().numpy()

return image

def merge_dW_to_unet(pipe, dW_dict, alpha=1.0):

_tmp_sd = pipe.unet.state_dict()

for key in dW_dict.keys():

_tmp_sd[key] += dW_dict[key] * alpha

pipe.unet.load_state_dict(_tmp_sd, strict=False)

return pipe

def prepare_extra_step_kwargs(self, generator, eta):

# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature

# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.

# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502

# and should be between [0, 1]

accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())

extra_step_kwargs = {}

if accepts_eta:

extra_step_kwargs["eta"] = eta

# check if the scheduler accepts generator

accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())

if accepts_generator:

extra_step_kwargs["generator"] = generator

return extra_step_kwargs

def check_inputs(

self,

prompt,

height,

width,

callback_steps,

negative_prompt=None,

prompt_embeds=None,

negative_prompt_embeds=None,

):

if height % 8 != 0 or width % 8 != 0:

raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

if (callback_steps is None) or (

callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)

):

raise ValueError(

f"`callback_steps` has to be a positive integer but is {callback_steps} of type"

f" {type(callback_steps)}."

)

if prompt is not None and prompt_embeds is not None:

raise ValueError(

f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"

" only forward one of the two."

)

elif prompt is None and prompt_embeds is None:

raise ValueError(

"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."

)

elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):

raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

if negative_prompt is not None and negative_prompt_embeds is not None:

raise ValueError(

f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"

f" {negative_prompt_embeds}. Please make sure to only forward one of the two."

)

if prompt_embeds is not None and negative_prompt_embeds is not None:

if prompt_embeds.shape != negative_prompt_embeds.shape:

raise ValueError(

"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"

f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"

f" {negative_prompt_embeds.shape}."

)

def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):

shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)

if isinstance(generator, list) and len(generator) != batch_size:

raise ValueError(

f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

f" size of {batch_size}. Make sure the batch size matches the length of the generators."

)

if latents is None:

latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

else:

latents = latents.to(device)

# scale the initial noise by the standard deviation required by the scheduler

latents = latents * self.scheduler.init_noise_sigma

return latents

@torch.no_grad()

def __call__(

self,

prompt: Union[str, List[str]] = None,

height: Optional[int] = None,

width: Optional[int] = None,

num_inference_steps: int = 50,

guidance_scale: float = 7.5,

negative_prompt: Optional[Union[str, List[str]]] = None,

num_images_per_prompt: Optional[int] = 1,

eta: float = 0.0,

generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,

latents: Optional[torch.FloatTensor] = None,

prompt_embeds: Optional[torch.FloatTensor] = None,

negative_prompt_embeds: Optional[torch.FloatTensor] = None,

output_type: Optional[str] = "pil",

return_dict: bool = True,

callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,

callback_steps: int = 1,

cross_attention_kwargs: Optional[Dict[str, Any]] = None,

guidance_rescale: float = 0.0,

):

r"""

The call function to the pipeline for generation.

Args:

prompt (`str` or `List[str]`, *optional*):

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`):

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):

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`.

negative_prompt (`str` or `List[str]`, *optional*):

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):

The number of images to generate per prompt.

eta (`float`, *optional*, defaults to 0.0):

Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies

to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.

generator (`torch.Generator` or `List[torch.Generator]`, *optional*):

A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) 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 random `generator`.

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 the `prompt` 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 the `negative_prompt` input argument.

output_type (`str`, *optional*, defaults to `"pil"`):

The output format of the generated image. Choose between `PIL.Image` or `np.array`.

return_dict (`bool`, *optional*, defaults to `True`):

Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a

plain tuple.

callback (`Callable`, *optional*):

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):

The frequency at which the `callback` 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 the [`AttentionProcessor`] as defined in

[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

guidance_rescale (`float`, *optional*, defaults to 0.7):

Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are

Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when

using zero terminal SNR.

Examples:

Returns:

[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:

If `return_dict` is `True`, [`~pipelines.stable_diffusion.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.

"""

# 0. Default height and width to unet

height = height or self.unet.config.sample_size * self.vae_scale_factor

width = width or self.unet.config.sample_size * self.vae_scale_factor

# 1. Check inputs. Raise error if not correct

self.check_inputs(

prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds

)

# 2. Define call parameters

if prompt is not None and isinstance(prompt, str):

batch_size = 1

elif prompt is not None and isinstance(prompt, list):

batch_size = len(prompt)

else:

batch_size = prompt_embeds.shape[0]

device = self._execution_device

# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)

# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`

# corresponds to doing no classifier free guidance.

do_classifier_free_guidance = guidance_scale > 1.0

# 3. Encode input prompt

text_encoder_lora_scale = (

cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None

)

prompt_embeds, negative_prompt_embeds = self.encode_prompt(

prompt,

device,

num_images_per_prompt,

do_classifier_free_guidance,

negative_prompt,

prompt_embeds=prompt_embeds,

negative_prompt_embeds=negative_prompt_embeds,

lora_scale=text_encoder_lora_scale,

)

# For classifier free guidance, we need to do two forward passes.

# Here we concatenate the unconditional and text embeddings into a single batch

# to avoid doing two forward passes

if do_classifier_free_guidance:

prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

# 4. Prepare timesteps

timesteps = [(1.0 - i / num_inference_steps) * 1000.0 for i in range(num_inference_steps)]

# 5. Prepare latent variables

num_channels_latents = self.unet.config.in_channels

latents = self.prepare_latents(

batch_size * num_images_per_prompt,

num_channels_latents,

height,

width,

prompt_embeds.dtype,

device,

generator,

latents,

)

# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline

dt = 1.0 / num_inference_steps

# 7. Denoising loop of Euler discretization from t = 0 to t = 1

with self.progress_bar(total=num_inference_steps) as progress_bar:

for i, t in enumerate(timesteps):

# expand the latents if we are doing classifier free guidance

latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

vec_t = torch.ones((latent_model_input.shape[0],), device=latents.device) * t

v_pred = self.unet(latent_model_input, vec_t, encoder_hidden_states=prompt_embeds).sample

# perform guidance

if do_classifier_free_guidance:

v_pred_neg, v_pred_text = v_pred.chunk(2)

v_pred = v_pred_neg + guidance_scale * (v_pred_text - v_pred_neg)

latents = latents + dt * v_pred

# call the callback, if provided

if i == len(timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):

progress_bar.update()

if callback is not None and i % callback_steps == 0:

step_idx = i // getattr(self.scheduler, "order", 1)

callback(step_idx, t, latents)

if not output_type == "latent":

image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

else:

image = latents

has_nsfw_concept = None

if has_nsfw_concept is None:

do_denormalize = [True] * image.shape[0]

else:

do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

# Offload all models

self.maybe_free_model_hooks()

if not return_dict:

return (image, has_nsfw_concept)

return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)