vyroAI
/

ImagineV4.1

@@ -1,795 +0,0 @@
-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 onediff.utils.tokenizer 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 onediff.utils.prompt_parser import ScheduledPromptConditioning
-from onediff.utils.prompt_parser import get_learned_conditioning_prompt_schedules
-from dynamicprompts.generators import RandomPromptGenerator
-import tqdm
-from cachetools import LRUCache
-from onediff.utils.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)