def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        negative_prompt: Union[str, List[str]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        true_cfg_scale: float = 1.0,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 28,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 3.5,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        ip_adapter_image: Optional[PipelineImageInput] = None,
        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
        negative_ip_adapter_image: Optional[PipelineImageInput] = None,
        negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 512,
        editing_prompt: Optional[Union[str, List[str]]] = None,
        editing_prompt_2: Optional[Union[str, List[str]]] = None,
        editing_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_editing_prompt_embeds: Optional[torch.FloatTensor] = 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]]] = 8,
        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,
    ):
        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.
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                will be used instead.
            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 `true_cfg_scale` is
                not greater than `1`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
            true_cfg_scale (`float`, *optional*, defaults to 1.0):
                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. This is set to 1024 by default for the best results.
            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.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
                provided, embeddings are computed from the `ip_adapter_image` input argument.
            negative_ip_adapter_image:
                (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
            negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
                provided, embeddings are computed from the `ip_adapter_image` 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.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled 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.flux.FluxPipelineOutput`] instead of a plain tuple.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
            editing_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image editing. If not defined, no editing will be performed.
            editing_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image editing. If not defined, will use editing_prompt instead.
            editing_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings for editing. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, text embeddings will be generated from `editing_prompt` input argument.
            reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
                Whether to reverse the editing direction for each editing prompt.
            edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
                Guidance scale for the editing process. If provided as a list, each value corresponds to an editing prompt.
            edit_warmup_steps (`int` or `List[int]`, *optional*, defaults to 10):
                Number of warmup steps for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_cooldown_steps (`int` or `List[int]`, *optional*, defaults to None):
                Number of cooldown steps for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
                Threshold for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_momentum_scale (`float`, *optional*, defaults to 0.1):
                Scale of momentum to be added to the editing guidance at each diffusion step.
            edit_mom_beta (`float`, *optional*, defaults to 0.4):
                Beta value for momentum calculation in editing guidance.
            edit_weights (`List[float]`, *optional*):
                Weights for each editing prompt.
            sem_guidance (`List[torch.Tensor]`, *optional*):
                Pre-generated semantic guidance. If provided, it will be used instead of calculating guidance from editing prompts.

        Examples:

        Returns:
            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
            images.
        """

        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
            max_sequence_length=max_sequence_length,
        )

        self._guidance_scale = guidance_scale
        self._joint_attention_kwargs = joint_attention_kwargs
        self._interrupt = False

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        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_embeds is not None:
            enable_edit_guidance = True
            enabled_editing_prompts = editing_prompt_embeds.shape[0]
        else:
            enabled_editing_prompts = 0
            enable_edit_guidance = False

        has_neg_prompt = negative_prompt is not None or (
            negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
        )
        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt

        device = self._execution_device

        lora_scale = (
            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
        )
        (
            prompt_embeds,
            pooled_prompt_embeds,
            editing_prompts_embeds,
            pooled_editing_prompt_embeds,
            text_ids,
            edit_text_ids,
            enabled_editing_prompts,
        ) = self.encode_text_with_editing(
            prompt=prompt,
            prompt_2=prompt_2,
            pooled_prompt_embeds=pooled_prompt_embeds,
            editing_prompt=editing_prompt,
            editing_prompt_2=editing_prompt_2,
            pooled_editing_prompt_embeds=pooled_editing_prompt_embeds,
            lora_scale=lora_scale,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            max_sequence_length=max_sequence_length,
        )

        if do_true_cfg:
            (
                negative_prompt_embeds,
                negative_pooled_prompt_embeds,
                _,
            ) = self.encode_prompt(
                prompt=negative_prompt,
                prompt_2=negative_prompt_2,
                prompt_embeds=negative_prompt_embeds,
                pooled_prompt_embeds=negative_pooled_prompt_embeds,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
                max_sequence_length=max_sequence_length,
                lora_scale=lora_scale,
            )
            negative_prompt_embeds = torch.cat([negative_prompt_embeds] * batch_size, dim=0)
            negative_pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds] * batch_size, dim=0)

        # 4. Prepare latent variables
        num_channels_latents = self.transformer.config.in_channels // 4
        latents, latent_image_ids = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 5. Prepare timesteps
        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
        image_seq_len = latents.shape[1]
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.get("base_image_seq_len", 256),
            self.scheduler.config.get("max_image_seq_len", 4096),
            self.scheduler.config.get("base_shift", 0.5),
            self.scheduler.config.get("max_shift", 1.15),
        )
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            device,
            sigmas=sigmas,
            mu=mu,
        )
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        self._num_timesteps = len(timesteps)

        edit_momentum = None
        if edit_warmup_steps:
            tmp_e_warmup_steps = edit_warmup_steps if isinstance(edit_warmup_steps, list) else [edit_warmup_steps]
            min_edit_warmup_steps = min(tmp_e_warmup_steps)
        else:
            min_edit_warmup_steps = 0

        if edit_cooldown_steps:
            tmp_e_cooldown_steps = (
                edit_cooldown_steps if isinstance(edit_cooldown_steps, list) else [edit_cooldown_steps]
            )
            max_edit_cooldown_steps = min(max(tmp_e_cooldown_steps), num_inference_steps)
        else:
            max_edit_cooldown_steps = num_inference_steps

        # handle guidance
        if self.transformer.config.guidance_embeds:
            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
            guidance = guidance.expand(latents.shape[0])
        else:
            guidance = None

        if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
            negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
        ):
            negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
        elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
            negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
        ):
            ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)

        if self.joint_attention_kwargs is None:
            self._joint_attention_kwargs = {}

        image_embeds = None
        negative_image_embeds = None
        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
            )
        if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
            negative_image_embeds = self.prepare_ip_adapter_image_embeds(
                negative_ip_adapter_image,
                negative_ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
            )

        # 6. Denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                if image_embeds is not None:
                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.expand(latents.shape[0]).to(latents.dtype)

                # handle guidance
                if self.transformer.config.guidance_embeds:
                    guidance = torch.tensor([guidance_scale], device=device)
                    guidance = guidance.expand(latents.shape[0])
                else:
                    guidance = None

                noise_pred = self.transformer(
                    hidden_states=latents,
                    timestep=timestep / 1000,
                    guidance=guidance,
                    pooled_projections=pooled_prompt_embeds,
                    encoder_hidden_states=prompt_embeds,
                    txt_ids=text_ids,
                    img_ids=latent_image_ids,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    return_dict=False,
                )[0]

                if enable_edit_guidance and max_edit_cooldown_steps >= i >= min_edit_warmup_steps:
                    noise_pred_edit_concepts = []
                    for e_embed, pooled_e_embed, e_text_id in zip(
                        editing_prompts_embeds, pooled_editing_prompt_embeds, edit_text_ids
                    ):
                        noise_pred_edit = self.transformer(
                            hidden_states=latents,
                            timestep=timestep / 1000,
                            guidance=guidance,
                            pooled_projections=pooled_e_embed,
                            encoder_hidden_states=e_embed,
                            txt_ids=e_text_id,
                            img_ids=latent_image_ids,
                            joint_attention_kwargs=self.joint_attention_kwargs,
                            return_dict=False,
                        )[0]
                        noise_pred_edit_concepts.append(noise_pred_edit)

                if do_true_cfg:
                    if negative_image_embeds is not None:
                        self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
                    noise_pred_uncond = self.transformer(
                        hidden_states=latents,
                        timestep=timestep / 1000,
                        guidance=guidance,
                        pooled_projections=negative_pooled_prompt_embeds,
                        encoder_hidden_states=negative_prompt_embeds,
                        txt_ids=text_ids,
                        img_ids=latent_image_ids,
                        joint_attention_kwargs=self.joint_attention_kwargs,
                        return_dict=False,
                    )[0]
                    noise_guidance = true_cfg_scale * (noise_pred - noise_pred_uncond)
                else:
                    noise_pred_uncond = noise_pred
                    noise_guidance = noise_pred

                if edit_momentum is None:
                    edit_momentum = torch.zeros_like(noise_guidance)

                if enable_edit_guidance and max_edit_cooldown_steps >= i >= min_edit_warmup_steps:
                    concept_weights = torch.zeros(
                        (enabled_editing_prompts, noise_guidance.shape[0]),
                        device=device,
                        dtype=noise_guidance.dtype,
                    )
                    noise_guidance_edit = torch.zeros(
                        (enabled_editing_prompts, *noise_guidance.shape),
                        device=device,
                        dtype=noise_guidance.dtype,
                    )

                    warmup_inds = []
                    for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
                        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

                        if do_true_cfg:
                            noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
                        else:  # simple sega
                            noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred
                        tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2))

                        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],
                            noise_guidance_edit_tmp,
                            torch.zeros_like(noise_guidance_edit_tmp),
                        )

                        noise_guidance_edit[c, :, :, :] = noise_guidance_edit_tmp

                    warmup_inds = torch.tensor(warmup_inds).to(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(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)

                        noise_guidance_edit_tmp = torch.index_select(noise_guidance_edit.to(device), 0, warmup_inds)
                        noise_guidance_edit_tmp = torch.einsum(
                            "cb,cbij->bij", concept_weights_tmp, noise_guidance_edit_tmp
                        )
                        noise_guidance_edit_tmp = noise_guidance_edit_tmp
                        noise_guidance = noise_guidance + noise_guidance_edit_tmp

                        del noise_guidance_edit_tmp
                        del concept_weights_tmp
                        concept_weights = concept_weights.to(device)
                        noise_guidance_edit = noise_guidance_edit.to(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,cbij->bij", 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

                if sem_guidance is not None:
                    edit_guidance = sem_guidance[i].to(device)
                    noise_guidance = noise_guidance + edit_guidance

                if do_true_cfg:
                    noise_pred = noise_guidance + noise_pred_uncond
                else:
                    noise_pred = noise_guidance

                # compute the previous noisy sample x_t -> x_t-1
                latents_dtype = latents.dtype
                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

                if latents.dtype != latents_dtype:
                    if torch.backends.mps.is_available():
                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                        latents = latents.to(latents_dtype)

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()

                if XLA_AVAILABLE:
                    xm.mark_step()

        if output_type == "latent":
            image = latents

        else:
            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
            image = self.vae.decode(latents, return_dict=False)[0]
            image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return FluxPipelineOutput(
            image,
        )

        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.
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                will be used instead.
            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 `true_cfg_scale` is
                not greater than `1`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
            true_cfg_scale (`float`, *optional*, defaults to 1.0):
                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. This is set to 1024 by default for the best results.
            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.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
                provided, embeddings are computed from the `ip_adapter_image` input argument.
            negative_ip_adapter_image:
                (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
            negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
                provided, embeddings are computed from the `ip_adapter_image` 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.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled 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.flux.FluxPipelineOutput`] instead of a plain tuple.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
            editing_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image editing. If not defined, no editing will be performed.
            editing_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image editing. If not defined, will use editing_prompt instead.
            editing_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings for editing. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, text embeddings will be generated from `editing_prompt` input argument.
            reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
                Whether to reverse the editing direction for each editing prompt.
            edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
                Guidance scale for the editing process. If provided as a list, each value corresponds to an editing prompt.
            edit_warmup_steps (`int` or `List[int]`, *optional*, defaults to 10):
                Number of warmup steps for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_cooldown_steps (`int` or `List[int]`, *optional*, defaults to None):
                Number of cooldown steps for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
                Threshold for editing guidance. If provided as a list, each value corresponds to an editing prompt.
            edit_momentum_scale (`float`, *optional*, defaults to 0.1):
                Scale of momentum to be added to the editing guidance at each diffusion step.
            edit_mom_beta (`float`, *optional*, defaults to 0.4):
                Beta value for momentum calculation in editing guidance.
            edit_weights (`List[float]`, *optional*):
                Weights for each editing prompt.
            sem_guidance (`List[torch.Tensor]`, *optional*):
                Pre-generated semantic guidance. If provided, it will be used instead of calculating guidance from editing prompts.

        Examples:

        Returns:
            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
            images.
        
examples/community/pipeline_flux_semantic_guidance.py