stable_diffusion_custom_v4_1.py

import random
from diffusers import StableDiffusionPipeline
# from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput, AutoencoderKL, CLIPTextModel, CLIPTokenizer, UNet2DConditionModel, KarrasDiffusionSchedulers, StableDiffusionSafetyChecker, CLIPImageProcessor
from compel import Compel
from tokenizer_util import TextualInversionLoaderMixin, MultiTokenCLIPTokenizer
import torch
from typing import Any, Callable, Dict, List, Optional, Union
from dynamicprompts.generators import RandomPromptGenerator
import time
from compel import Compel
from prompt_parser import ScheduledPromptConditioning
from prompt_parser import get_learned_conditioning_prompt_schedules
from dynamicprompts.generators import RandomPromptGenerator
import tqdm
from cachetools import LRUCache
from image_processor import VaeImageProcessor


class CustomStableDiffusionPipeline4_1(TextualInversionLoaderMixin, StableDiffusionPipeline):
    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,
        prompt_cache_size: int = 1024,
        prompt_cache_ttl: int = 60 * 2,
    ) -> None:
        super().__init__(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler,
                         safety_checker=safety_checker, feature_extractor=feature_extractor, requires_safety_checker=requires_safety_checker)

        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)

        self.compel = Compel(tokenizer=self.tokenizer,
                             text_encoder=self.text_encoder, truncate_long_prompts=False)
        self.cache = LRUCache(maxsize=prompt_cache_size)

        self.cached_uc = [None, None]
        self.cached_c = [None, None]

        self.prompt_handler = None

    def build_scheduled_cond(self, prompt, steps, key):
        prompt_schedule = get_learned_conditioning_prompt_schedules([prompt], steps)[
            0]

        cached = self.cache.get(key, None)
        if cached is not None:
            return cached

        texts = [x[1] for x in prompt_schedule]
        conds = [self.compel.build_conditioning_tensor(
            text).to('cpu') for text in texts]

        cond_schedule = []
        for i, s in enumerate(prompt_schedule):
            cond_schedule.append(ScheduledPromptConditioning(s[0], conds[i]))

        self.cache[key] = cond_schedule
        return cond_schedule

    def initialize_magic_prompt_cache(self, pos_prompt_template: str, plain_prompt_template: str, neg_prompt_template: str, num_to_generate: int, steps: int):
        r"""
        Initializes the magic prompt cache for the forward pass. 
        Must be called immedaitely after Compel is loaded and embeds are initalized.
        """
        rpg = RandomPromptGenerator(ignore_whitespace=True, seed=555)
        positive_prompts = rpg.generate(
            template=pos_prompt_template, num_images=num_to_generate)
        scheduled_conds = []
        with torch.no_grad():
            cache = {}
            for i in tqdm.tqdm(range(len(positive_prompts))):
                scheduled_conds.append(self.build_scheduled_cond(
                    positive_prompts[i], steps, cache))

            plain_scheduled_cond = self.build_scheduled_cond(
                plain_prompt_template, steps, cache)

            scheduled_uncond = self.build_scheduled_cond(
                neg_prompt_template, steps, cache)

        self.scheduled_conds = scheduled_conds
        self.plain_scheduled_cond = plain_scheduled_cond
        self.scheduled_uncond = scheduled_uncond

    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `list(int)`):
                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]`):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
        """
        batch_size = len(prompt) if isinstance(prompt, list) else 1

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="np",
        )
        text_input_ids = text_inputs.input_ids
        text_input_ids = torch.from_numpy(text_input_ids)
        untruncated_ids = self.tokenizer(
            prompt, padding="max_length", return_tensors="np").input_ids
        untruncated_ids = torch.from_numpy(untruncated_ids)

        if (
            text_input_ids.shape == untruncated_ids.shape
            and text_input_ids.numel() == untruncated_ids.numel()
            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

        text_embeddings = self.text_encoder(
            text_input_ids.to(device), attention_mask=attention_mask)
        text_embeddings = text_embeddings[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(
            bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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

            max_length = text_input_ids.shape[-1]
            uncond_input = self.tokenizer(
                uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = torch.from_numpy(
                    uncond_input.attention_mask).to(device)
            else:
                attention_mask = None

            uncond_embeddings = self.text_encoder(
                torch.from_numpy(uncond_input.input_ids).to(device), attention_mask=attention_mask,
            )
            uncond_embeddings = uncond_embeddings[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(
                1, num_images_per_prompt, 1)
            uncond_embeddings = uncond_embeddings.view(
                batch_size * num_images_per_prompt, seq_len, -1)

            # 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
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        return text_embeddings

    def _encode_promptv2(
        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,
    ):

        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:
            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]
                )

            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]

        prompt_embeds = prompt_embeds.to(
            dtype=self.text_encoder.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 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

            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=self.text_encoder.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)

            negative_prompt_embeds, prompt_embeds = self.compel.pad_conditioning_tensors_to_same_length(
                [negative_prompt_embeds, prompt_embeds])
            # 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
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def _pyramid_noise_like(self, noise, device, seed, iterations=6, discount=0.4):
        gen = torch.manual_seed(seed)
        # EDIT: w and h get over-written, rename for a different variant!
        b, c, w, h = noise.shape
        u = torch.nn.Upsample(size=(w, h), mode="bilinear").to(device)
        for i in range(iterations):
            r = random.random() * 2 + 2  # Rather than always going 2x,
            wn, hn = max(1, int(w / (r**i))), max(1, int(h / (r**i)))
            noise += u(torch.randn(b, c, wn, hn,
                       generator=gen).to(device)) * discount**i
            if wn == 1 or hn == 1:
                break  # Lowest resolution is 1x1
        return noise / noise.std()  # Scaled back to roughly unit variance

    @torch.no_grad()
    def inferV4(
        self,
        prompt: Union[str, List[str]],
        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[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[
            int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        compile_unet: bool = True,
        compile_vae: bool = True,
        compile_tenc: bool = True,
        max_tokens=0,
        seed=-1,
        flags=[],
        og_prompt=None,
        og_neg_prompt=None,
        disc=0.4,
        iter=6,
        pyramid=0,  # disabled by default unless specified
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            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](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). 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. 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`, *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 will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.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 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.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.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`.
        """
        # 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

        self.check_inputs(prompt, height, width, callback_steps)
        if negative_prompt == None:
            negative_prompt = ['']
        # 2. Define call parameters
        batch_size = 1 if isinstance(prompt, str) else len(prompt)
        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

        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # Cache key for flags
        plain = "plain" in flags
        flair = None
        for flag in flags:
            if "flair" in flag:
                flair = flag
                break

        with torch.no_grad():
            c_time = time.time()
            user_cond = self.build_scheduled_cond(
                prompt[0], num_inference_steps, ('pos', og_prompt, seed, plain, flair))
            c_time = time.time()
            user_uncond = self.build_scheduled_cond(
                negative_prompt[0], num_inference_steps, ('neg', negative_prompt[0], 0))

        c = []
        c.extend(user_cond)
        uc = []
        uc.extend(user_uncond)
        max_token_count = 0

        for cond in uc:
            if cond.cond.shape[1] > max_token_count:
                max_token_count = cond.cond.shape[1]
        for cond in c:
            if cond.cond.shape[1] > max_token_count:
                max_token_count = cond.cond.shape[1]

        def pad_tensor(conditionings: List[ScheduledPromptConditioning], max_token_count: int) -> List[ScheduledPromptConditioning]:

            c0_shape = conditionings[0].cond.shape
            if not all([len(c.cond.shape) == len(c0_shape) for c in conditionings]):
                raise ValueError(
                    "Conditioning tensors must all have either 2 dimensions (unbatched) or 3 dimensions (batched)")

            if len(c0_shape) == 2:
                # need to be unsqueezed
                for c in conditionings:
                    c.cond = c.cond.unsqueeze(0)
                c0_shape = conditionings[0].cond.shape
            if len(c0_shape) != 3:
                raise ValueError(
                    f"All conditioning tensors must have the same number of dimensions (2 or 3)")

            if not all([c.cond.shape[0] == c0_shape[0] and c.cond.shape[2] == c0_shape[2] for c in conditionings]):
                raise ValueError(
                    f"All conditioning tensors must have the same batch size ({c0_shape[0]}) and number of embeddings per token ({c0_shape[1]}")

            # if necessary, pad shorter tensors out with an emptystring tensor
            empty_z = torch.cat(
                [self.compel.build_conditioning_tensor("")] * c0_shape[0])
            for i, c in enumerate(conditionings):
                cond = c.cond.to(self.device)
                while cond.shape[1] < max_token_count:
                    cond = torch.cat([cond, empty_z], dim=1)
                conditionings[i] = ScheduledPromptConditioning(
                    c.end_at_step, cond)
            return conditionings

        uc = pad_tensor(uc, max_token_count)
        c = pad_tensor(c, max_token_count)

        next_uc = uc.pop(0)
        next_c = c.pop(0)
        prompt_embeds = None
        new_embeds = True
        embed_per_step = []
        for i in range(len(timesteps)):
            if i > next_uc.end_at_step:
                next_uc = uc.pop(0)
                new_embeds = True
            if i > next_c.end_at_step:
                next_c = c.pop(0)
                new_embeds = True

            if new_embeds:
                negative_prompt_embeds, prompt_embeds = self.compel.pad_conditioning_tensors_to_same_length([
                                                                                                            next_uc.cond, next_c.cond])
                prompt_embeds = torch.cat(
                    [negative_prompt_embeds, prompt_embeds])
                new_embeds = False

            embed_per_step.append(prompt_embeds)

        # 5. Prepare latent variables
        num_channels_latents = self.unet.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
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - \
            num_inference_steps * self.scheduler.order
        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
                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t)

                prompt_embeds = embed_per_step[i]
                # predict the noise residual

                noise_pred = self.unet(
                    latent_model_input, t, encoder_hidden_states=prompt_embeds).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * \
                        (noise_pred_text - noise_pred_uncond)

                    if (i < pyramid*num_inference_steps):
                        noise_pred = self._pyramid_noise_like(
                            noise_pred, device, seed, iterations=iter, discount=disc)

                    # compute the previous noisy sample x_t -> x_t-1
                    latents = self.scheduler.step(
                        noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # call the callback, if provided
                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, 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 last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    @torch.no_grad()
    def inferPipe(
        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,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            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.
            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](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). 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)](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 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](https://pillow.readthedocs.io/en/stable/): `PIL.Image.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 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.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`.
        """
        # 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)

        # 2. Define call parameters
        batch_size = 1 if isinstance(prompt, str) else len(prompt)
        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_embeddings = self._encode_prompt(
            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            text_embeddings.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - \
            num_inference_steps * self.scheduler.order
        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
                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t)

                noise_pred = self.unet(
                    latent_model_input, t, encoder_hidden_states=text_embeddings).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * \
                        (noise_pred_text - noise_pred_uncond)

                    # compute the previous noisy sample x_t -> x_t-1
                    latents = self.scheduler.step(
                        noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # call the callback, if provided
                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, 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, text_embeddings.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 last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)