Upload stable_diffusion_custom_v4_1.py

stable_diffusion_custom_v4_1.py

@@ -0,0 +1,795 @@
+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)