Create modified_pipeline_semantic_stable_diffusion.py

modified_pipeline_semantic_stable_diffusion.py

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+
+import inspect
+import warnings
+from itertools import repeat
+from typing import Callable, List, Optional, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import logging, randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+# from . import SemanticStableDiffusionPipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class SemanticStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation with latent editing.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    This model builds on the implementation of ['StableDiffusionPipeline']
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`Q16SafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents, return_dict=False)[0]
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        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: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[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: int = 1,
+        editing_prompt: Optional[Union[str, List[str]]] = None,
+        editing_prompt_embeddings: Optional[torch.Tensor] = None,
+        reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
+        edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
+        edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
+        edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
+        edit_threshold: Optional[Union[float, List[float]]] = 0.9,
+        edit_momentum_scale: Optional[float] = 0.1,
+        edit_mom_beta: Optional[float] = 0.4,
+        edit_weights: Optional[List[float]] = None,
+        sem_guidance: Optional[List[torch.Tensor]] = None,
+
+        # DDPM additions
+        use_ddpm: bool = False,
+        wts: Optional[List[torch.Tensor]] = None,
+        zs: Optional[List[torch.Tensor]] = None
+    ):
+        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*):
+                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`.
+            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.
+            editing_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
+                `editing_prompt = None`. Guidance direction of prompt should be specified via
+                `reverse_editing_direction`.
+            editing_prompt_embeddings (`torch.Tensor>`, *optional*):
+                Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
+                specified via `reverse_editing_direction`.
+            reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
+                Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
+            edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
+                Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`.
+                `edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
+                Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
+                will still be calculated for those steps and applied once all warmup periods are over.
+                `edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
+                Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
+            edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
+                Threshold of semantic guidance.
+            edit_momentum_scale (`float`, *optional*, defaults to 0.1):
+                Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
+                momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
+                than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are
+                finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_mom_beta (`float`, *optional*, defaults to 0.4):
+                Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
+                momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
+                than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_weights (`List[float]`, *optional*, defaults to `None`):
+                Indicates how much each individual concept should influence the overall guidance. If no weights are
+                provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            sem_guidance (`List[torch.Tensor]`, *optional*):
+                List of pre-generated guidance vectors to be applied at generation. Length of the list has to
+                correspond to `num_inference_steps`.
+
+        Returns:
+            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] 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)
+
+        if editing_prompt:
+            enable_edit_guidance = True
+            if isinstance(editing_prompt, str):
+                editing_prompt = [editing_prompt]
+            enabled_editing_prompts = len(editing_prompt)
+        elif editing_prompt_embeddings is not None:
+            enable_edit_guidance = True
+            enabled_editing_prompts = editing_prompt_embeddings.shape[0]
+        else:
+            enabled_editing_prompts = 0
+            enable_edit_guidance = False
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            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}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[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)
+
+        if enable_edit_guidance:
+            # get safety text embeddings
+            if editing_prompt_embeddings is None:
+                edit_concepts_input = self.tokenizer(
+                    [x for item in editing_prompt for x in repeat(item, batch_size)],
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    return_tensors="pt",
+                )
+
+                edit_concepts_input_ids = edit_concepts_input.input_ids
+
+                if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                    removed_text = self.tokenizer.batch_decode(
+                        edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
+                    )
+                    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}"
+                    )
+                    edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
+                edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
+            else:
+                edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
+
+            # duplicate text embeddings for each generation per prompt, using mps friendly method
+            bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
+            edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
+            edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
+
+        # 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
+        # get unconditional embeddings for classifier free guidance
+
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            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="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, 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
+            if enable_edit_guidance:
+                text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
+            else:
+                text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+        # get the initial random noise unless the user supplied it
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
+        timesteps = self.scheduler.timesteps
+        if use_ddpm:
+          t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
+          timesteps = timesteps[-zs.shape[0]:] 
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            self.device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # Initialize edit_momentum to None
+        edit_momentum = None
+
+        self.uncond_estimates = None
+        self.text_estimates = None
+        self.edit_estimates = None
+        self.sem_guidance = None
+
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
+            )
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts)  # [b,4, 64, 64]
+                noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
+                noise_pred_edit_concepts = noise_pred_out[2:]
+
+                # default text guidance
+                noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
+                # noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
+
+                if self.uncond_estimates is None:
+                    self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
+                self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
+
+                if self.text_estimates is None:
+                    self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
+                self.text_estimates[i] = noise_pred_text.detach().cpu()
+
+                if self.edit_estimates is None and enable_edit_guidance:
+                    self.edit_estimates = torch.zeros(
+                        (num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
+                    )
+
+                if self.sem_guidance is None:
+                    self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
+
+                if edit_momentum is None:
+                    edit_momentum = torch.zeros_like(noise_guidance)
+
+                if enable_edit_guidance:
+                    concept_weights = torch.zeros(
+                        (len(noise_pred_edit_concepts), noise_guidance.shape[0]),
+                        device=self.device,
+                        dtype=noise_guidance.dtype,
+                    )
+                    noise_guidance_edit = torch.zeros(
+                        (len(noise_pred_edit_concepts), *noise_guidance.shape),
+                        device=self.device,
+                        dtype=noise_guidance.dtype,
+                    )
+                    # noise_guidance_edit = torch.zeros_like(noise_guidance)
+                    warmup_inds = []
+                    for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
+                        self.edit_estimates[i, c] = noise_pred_edit_concept
+                        if isinstance(edit_guidance_scale, list):
+                            edit_guidance_scale_c = edit_guidance_scale[c]
+                        else:
+                            edit_guidance_scale_c = edit_guidance_scale
+
+                        if isinstance(edit_threshold, list):
+                            edit_threshold_c = edit_threshold[c]
+                        else:
+                            edit_threshold_c = edit_threshold
+                        if isinstance(reverse_editing_direction, list):
+                            reverse_editing_direction_c = reverse_editing_direction[c]
+                        else:
+                            reverse_editing_direction_c = reverse_editing_direction
+                        if edit_weights:
+                            edit_weight_c = edit_weights[c]
+                        else:
+                            edit_weight_c = 1.0
+                        if isinstance(edit_warmup_steps, list):
+                            edit_warmup_steps_c = edit_warmup_steps[c]
+                        else:
+                            edit_warmup_steps_c = edit_warmup_steps
+
+                        if isinstance(edit_cooldown_steps, list):
+                            edit_cooldown_steps_c = edit_cooldown_steps[c]
+                        elif edit_cooldown_steps is None:
+                            edit_cooldown_steps_c = i + 1
+                        else:
+                            edit_cooldown_steps_c = edit_cooldown_steps
+                        if i >= edit_warmup_steps_c:
+                            warmup_inds.append(c)
+                        if i >= edit_cooldown_steps_c:
+                            noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
+                            continue
+
+                        noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
+                        # tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
+                        tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
+
+                        tmp_weights = torch.full_like(tmp_weights, edit_weight_c)  # * (1 / enabled_editing_prompts)
+                        if reverse_editing_direction_c:
+                            noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
+                        concept_weights[c, :] = tmp_weights
+
+                        noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
+
+                        # torch.quantile function expects float32
+                        if noise_guidance_edit_tmp.dtype == torch.float32:
+                            tmp = torch.quantile(
+                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
+                                edit_threshold_c,
+                                dim=2,
+                                keepdim=False,
+                            )
+                        else:
+                            tmp = torch.quantile(
+                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
+                                edit_threshold_c,
+                                dim=2,
+                                keepdim=False,
+                            ).to(noise_guidance_edit_tmp.dtype)
+
+                        noise_guidance_edit_tmp = torch.where(
+                            torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
+                            noise_guidance_edit_tmp,
+                            torch.zeros_like(noise_guidance_edit_tmp),
+                        )
+                        noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
+
+                        # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
+
+                    warmup_inds = torch.tensor(warmup_inds).to(self.device)
+                    if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
+                        concept_weights = concept_weights.to("cpu")  # Offload to cpu
+                        noise_guidance_edit = noise_guidance_edit.to("cpu")
+
+                        concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
+                        concept_weights_tmp = torch.where(
+                            concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
+                        )
+                        concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
+                        # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
+
+                        noise_guidance_edit_tmp = torch.index_select(
+                            noise_guidance_edit.to(self.device), 0, warmup_inds
+                        )
+                        noise_guidance_edit_tmp = torch.einsum(
+                            "cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
+                        )
+                        noise_guidance_edit_tmp = noise_guidance_edit_tmp
+                        noise_guidance = noise_guidance + noise_guidance_edit_tmp
+
+                        self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
+
+                        del noise_guidance_edit_tmp
+                        del concept_weights_tmp
+                        concept_weights = concept_weights.to(self.device)
+                        noise_guidance_edit = noise_guidance_edit.to(self.device)
+
+                    concept_weights = torch.where(
+                        concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
+                    )
+
+                    concept_weights = torch.nan_to_num(concept_weights)
+
+                    noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
+
+                    noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
+
+                    edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
+
+                    if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
+                        noise_guidance = noise_guidance + noise_guidance_edit
+                        self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
+
+                if sem_guidance is not None:
+                    edit_guidance = sem_guidance[i].to(self.device)
+                    noise_guidance = noise_guidance + edit_guidance
+
+                noise_pred = noise_pred_uncond + noise_guidance
+            ## ddpm ###########################################################
+            if use_ddpm:
+              
+              idx = t_to_idx[int(t)]
+              z = zs[idx] if not zs is None else None
+
+              # 1. get previous step value (=t-1)
+              prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
+              # 2. compute alphas, betas
+              alpha_prod_t = self.scheduler.alphas_cumprod[t]
+              alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
+              beta_prod_t = 1 - alpha_prod_t
+              
+              # 3. compute predicted original sample from predicted noise also called
+              # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+              pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+              
+              
+              # 5. compute variance: "sigma_t(η)" -> see formula (16)
+              # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)    
+              # variance = self.scheduler._get_variance(timestep, prev_timestep)
+              # variance = get_variance(model, t) #, prev_timestep)
+              prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
+              alpha_prod_t = self.scheduler.alphas_cumprod[t]
+              alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
+              beta_prod_t = 1 - alpha_prod_t
+              beta_prod_t_prev = 1 - alpha_prod_t_prev
+              variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
+              
+            
+              
+              std_dev_t = eta * variance ** (0.5)
+              # Take care of asymetric reverse process (asyrp)
+              noise_pred_direction = noise_pred
+              
+              # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+              # pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
+              pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * noise_pred_direction
+              
+              # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+              prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+              # 8. Add noice if eta > 0
+              if eta > 0:
+                  if z is None:
+                      z = torch.randn(noise_pred.shape, device=self.device)
+                  sigma_z =  eta * variance ** (0.5) * z
+                  latents = prev_sample + sigma_z
+
+            ## ddpm ##########################################################
+                # compute the previous noisy sample x_t -> x_t-1
+            if not use_ddpm:
+              latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        
+         # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        #return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+        
+        # 8. Post-processing
+        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, self.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)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)