Create pipeline.py

pipeline.py

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+# Copyright 2024 HunyuanDiT Authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import (
+    BertModel,
+    BertTokenizer,
+    CLIPImageProcessor,
+    MT5Tokenizer,
+    T5EncoderModel,
+)
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import AutoencoderKL, HunyuanDiT2DModel
+from diffusers.models.embeddings import get_2d_rotary_pos_embed
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from diffusers.schedulers import DDPMScheduler
+from diffusers.utils import (
+    deprecate,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import FlowMatchEulerDiscreteScheduler
+        >>> from diffusers.utils import load_image
+        >>> from PIL import Image
+        >>> from torchvision import transforms
+        >>> from pipeline_hunyuandit_differential_img2img import HunyuanDiTDifferentialImg2ImgPipeline
+        >>> pipe = HunyuanDiTDifferentialImg2ImgPipeline.from_pretrained(
+        >>>     "Tencent-Hunyuan/HunyuanDiT-Diffusers", torch_dtype=torch.float16
+        >>> ).to("cuda")
+        >>> source_image = load_image(
+        >>>     "https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png"
+        >>> )
+        >>> map = load_image(
+        >>>     "https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask_2.png"
+        >>> )
+        >>> prompt = "a green pear"
+        >>> negative_prompt = "blurry"
+        >>> image = pipe(
+        >>>     prompt=prompt,
+        >>>     negative_prompt=negative_prompt,
+        >>>     image=source_image,
+        >>>     num_inference_steps=28,
+        >>>     guidance_scale=4.5,
+        >>>     strength=1.0,
+        >>>     map=map,
+        >>> ).images[0]
+
+        ```
+"""
+
+STANDARD_RATIO = np.array(
+    [
+        1.0,  # 1:1
+        4.0 / 3.0,  # 4:3
+        3.0 / 4.0,  # 3:4
+        16.0 / 9.0,  # 16:9
+        9.0 / 16.0,  # 9:16
+    ]
+)
+STANDARD_SHAPE = [
+    [(1024, 1024), (1280, 1280)],  # 1:1
+    [(1024, 768), (1152, 864), (1280, 960)],  # 4:3
+    [(768, 1024), (864, 1152), (960, 1280)],  # 3:4
+    [(1280, 768)],  # 16:9
+    [(768, 1280)],  # 9:16
+]
+STANDARD_AREA = [np.array([w * h for w, h in shapes]) for shapes in STANDARD_SHAPE]
+SUPPORTED_SHAPE = [
+    (1024, 1024),
+    (1280, 1280),  # 1:1
+    (1024, 768),
+    (1152, 864),
+    (1280, 960),  # 4:3
+    (768, 1024),
+    (864, 1152),
+    (960, 1280),  # 3:4
+    (1280, 768),  # 16:9
+    (768, 1280),  # 9:16
+]
+
+
+def map_to_standard_shapes(target_width, target_height):
+    target_ratio = target_width / target_height
+    closest_ratio_idx = np.argmin(np.abs(STANDARD_RATIO - target_ratio))
+    closest_area_idx = np.argmin(np.abs(STANDARD_AREA[closest_ratio_idx] - target_width * target_height))
+    width, height = STANDARD_SHAPE[closest_ratio_idx][closest_area_idx]
+    return width, height
+
+
+def get_resize_crop_region_for_grid(src, tgt_size):
+    th = tw = tgt_size
+    h, w = src
+
+    r = h / w
+
+    # resize
+    if r > 1:
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor,
+    generator: Optional[torch.Generator] = None,
+    sample_mode: str = "sample",
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class HunyuanDiTDifferentialImg2ImgPipeline(DiffusionPipeline):
+    r"""
+    Differential Pipeline for English/Chinese-to-image generation using HunyuanDiT.
+
+    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.)
+
+    HunyuanDiT uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by
+    ourselves)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use
+            `sdxl-vae-fp16-fix`.
+        text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+            HunyuanDiT uses a fine-tuned [bilingual CLIP].
+        tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]):
+            A `BertTokenizer` or `CLIPTokenizer` to tokenize text.
+        transformer ([`HunyuanDiT2DModel`]):
+            The HunyuanDiT model designed by Tencent Hunyuan.
+        text_encoder_2 (`T5EncoderModel`):
+            The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
+        tokenizer_2 (`MT5Tokenizer`):
+            The tokenizer for the mT5 embedder.
+        scheduler ([`DDPMScheduler`]):
+            A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = [
+        "safety_checker",
+        "feature_extractor",
+        "text_encoder_2",
+        "tokenizer_2",
+        "text_encoder",
+        "tokenizer",
+    ]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "prompt_embeds_2",
+        "negative_prompt_embeds_2",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: BertModel,
+        tokenizer: BertTokenizer,
+        transformer: HunyuanDiT2DModel,
+        scheduler: DDPMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+        text_encoder_2=T5EncoderModel,
+        tokenizer_2=MT5Tokenizer,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            text_encoder_2=text_encoder_2,
+        )
+
+        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.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_normalize=False,
+            do_convert_grayscale=True,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+        self.default_sample_size = (
+            self.transformer.config.sample_size
+            if hasattr(self, "transformer") and self.transformer is not None
+            else 128
+        )
+
+    # copied from diffusers.pipelines.huanyuandit.pipeline_huanyuandit.HunyuanDiTPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: str,
+        device: torch.device = None,
+        dtype: torch.dtype = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: Optional[int] = None,
+        text_encoder_index: int = 0,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            dtype (`torch.dtype`):
+                torch dtype
+            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]`, *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`).
+            prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.
+            prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
+            negative_prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
+            max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.
+            text_encoder_index (`int`, *optional*):
+                Index of the text encoder to use. `0` for clip and `1` for T5.
+        """
+        if dtype is None:
+            if self.text_encoder_2 is not None:
+                dtype = self.text_encoder_2.dtype
+            elif self.transformer is not None:
+                dtype = self.transformer.dtype
+            else:
+                dtype = None
+
+        if device is None:
+            device = self._execution_device
+
+        tokenizers = [self.tokenizer, self.tokenizer_2]
+        text_encoders = [self.text_encoder, self.text_encoder_2]
+
+        tokenizer = tokenizers[text_encoder_index]
+        text_encoder = text_encoders[text_encoder_index]
+
+        if max_sequence_length is None:
+            if text_encoder_index == 0:
+                max_length = 77
+            if text_encoder_index == 1:
+                max_length = 256
+        else:
+            max_length = max_sequence_length
+
+        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 = tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_attention_mask=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            prompt_attention_mask = text_inputs.attention_mask.to(device)
+            prompt_embeds = text_encoder(
+                text_input_ids.to(device),
+                attention_mask=prompt_attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+            prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        prompt_embeds = prompt_embeds.to(dtype=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 prompt is not None and 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 = tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            negative_prompt_attention_mask = uncond_input.attention_mask.to(device)
+            negative_prompt_embeds = text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=negative_prompt_attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        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=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)
+
+        return (
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        )
+
+    # 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.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
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_attention_mask=None,
+        negative_prompt_attention_mask=None,
+        prompt_embeds_2=None,
+        negative_prompt_embeds_2=None,
+        prompt_attention_mask_2=None,
+        negative_prompt_attention_mask_2=None,
+        callback_on_step_end_tensor_inputs=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_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        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 None and prompt_embeds_2 is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` 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 prompt_embeds is not None and prompt_attention_mask is None:
+            raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
+
+        if prompt_embeds_2 is not None and prompt_attention_mask_2 is None:
+            raise ValueError("Must provide `prompt_attention_mask_2` when specifying `prompt_embeds_2`.")
+
+        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 negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
+            raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
+
+        if negative_prompt_embeds_2 is not None and negative_prompt_attention_mask_2 is None:
+            raise ValueError(
+                "Must provide `negative_prompt_attention_mask_2` when specifying `negative_prompt_embeds_2`."
+            )
+        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}."
+                )
+        if prompt_embeds_2 is not None and negative_prompt_embeds_2 is not None:
+            if prompt_embeds_2.shape != negative_prompt_embeds_2.shape:
+                raise ValueError(
+                    "`prompt_embeds_2` and `negative_prompt_embeds_2` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`"
+                    f" {negative_prompt_embeds_2.shape}."
+                )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        image,
+        timestep,
+        dtype,
+        device,
+        generator=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+
+        image = image.to(device=device, dtype=dtype)
+        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."
+            )
+        elif isinstance(generator, list):
+            init_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+
+        else:
+            init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+        init_latents = init_latents * self.vae.config.scaling_factor
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            deprecation_message = (
+                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
+                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+                " your script to pass as many initial images as text prompts to suppress this warning."
+            )
+            deprecate(
+                "len(prompt) != len(image)",
+                "1.0.0",
+                deprecation_message,
+                standard_warn=False,
+            )
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        shape = init_latents.shape
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    # 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.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: PipelineImageInput = None,
+        strength: float = 0.8,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: Optional[int] = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        guidance_scale: Optional[float] = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        prompt_attention_mask_2: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask_2: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback_on_step_end: Optional[
+            Union[
+                Callable[[int, int, Dict], None],
+                PipelineCallback,
+                MultiPipelineCallbacks,
+            ]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = (1024, 1024),
+        target_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        use_resolution_binning: bool = True,
+        map: PipelineImageInput = None,
+        denoising_start: Optional[float] = None,
+    ):
+        r"""
+        The call function to the pipeline for generation with HunyuanDiT.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            strength (`float`, *optional*, defaults to 0.8):
+                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+                essentially ignores `image`.
+            height (`int`):
+                The height in pixels of the generated image.
+            width (`int`):
+                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. This parameter is modulated by `strength`.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            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 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            negative_prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
+            prompt_attention_mask_2 (`torch.Tensor`, *optional*):
+                Attention mask for the prompt. Required when `prompt_embeds_2` is passed directly.
+            negative_prompt_attention_mask (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
+            negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*):
+                Attention mask for the negative prompt. Required when `negative_prompt_embeds_2` is passed directly.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.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_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A callback function or a list of callback functions to be called at the end of each denoising step.
+            callback_on_step_end_tensor_inputs (`List[str]`, *optional*):
+                A list of tensor inputs that should be passed to the callback function. If not defined, all tensor
+                inputs will be passed.
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Rescale the noise_cfg according to `guidance_rescale`. Based on findings of [Common Diffusion Noise
+                Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+            original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`):
+                The original size of the image. Used to calculate the time ids.
+            target_size (`Tuple[int, int]`, *optional*):
+                The target size of the image. Used to calculate the time ids.
+            crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`):
+                The top left coordinates of the crop. Used to calculate the time ids.
+            use_resolution_binning (`bool`, *optional*, defaults to `True`):
+                Whether to use resolution binning or not. If `True`, the input resolution will be mapped to the closest
+                standard resolution. Supported resolutions are 1024x1024, 1280x1280, 1024x768, 1152x864, 1280x960,
+                768x1024, 864x1152, 960x1280, 1280x768, and 768x1280. It is recommended to set this to `True`.
+            denoising_start (`float`, *optional*):
+                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
+                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
+                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
+                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
+                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. default height and width
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+        height = int((height // 16) * 16)
+        width = int((width // 16) * 16)
+
+        if use_resolution_binning and (height, width) not in SUPPORTED_SHAPE:
+            width, height = map_to_standard_shapes(width, height)
+            height = int(height)
+            width = int(width)
+            logger.warning(f"Reshaped to (height, width)=({height}, {width}), Supported shapes are {SUPPORTED_SHAPE}")
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+            prompt_embeds_2,
+            negative_prompt_embeds_2,
+            prompt_attention_mask_2,
+            negative_prompt_attention_mask_2,
+            callback_on_step_end_tensor_inputs,
+        )
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        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]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            max_sequence_length=77,
+            text_encoder_index=0,
+        )
+        (
+            prompt_embeds_2,
+            negative_prompt_embeds_2,
+            prompt_attention_mask_2,
+            negative_prompt_attention_mask_2,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds_2,
+            negative_prompt_embeds=negative_prompt_embeds_2,
+            prompt_attention_mask=prompt_attention_mask_2,
+            negative_prompt_attention_mask=negative_prompt_attention_mask_2,
+            max_sequence_length=256,
+            text_encoder_index=1,
+        )
+
+        # 4. Preprocess image
+        init_image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        map = self.mask_processor.preprocess(
+            map,
+            height=height // self.vae_scale_factor,
+            width=width // self.vae_scale_factor,
+        ).to(device)
+
+        # 5. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # begin diff diff change
+        total_time_steps = num_inference_steps
+        # end diff diff change
+
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            init_image,
+            latent_timestep,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        # 7. 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)
+
+        # 8. create image_rotary_emb, style embedding & time ids
+        grid_height = height // 8 // self.transformer.config.patch_size
+        grid_width = width // 8 // self.transformer.config.patch_size
+        base_size = 512 // 8 // self.transformer.config.patch_size
+        grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
+        image_rotary_emb = get_2d_rotary_pos_embed(
+            self.transformer.inner_dim // self.transformer.num_heads,
+            grid_crops_coords,
+            (grid_height, grid_width),
+        )
+
+        style = torch.tensor([0], device=device)
+
+        target_size = target_size or (height, width)
+        add_time_ids = list(original_size + target_size + crops_coords_top_left)
+        add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])
+            prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])
+            prompt_attention_mask_2 = torch.cat([negative_prompt_attention_mask_2, prompt_attention_mask_2])
+            add_time_ids = torch.cat([add_time_ids] * 2, dim=0)
+            style = torch.cat([style] * 2, dim=0)
+
+        prompt_embeds = prompt_embeds.to(device=device)
+        prompt_attention_mask = prompt_attention_mask.to(device=device)
+        prompt_embeds_2 = prompt_embeds_2.to(device=device)
+        prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device)
+        add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=device).repeat(
+            batch_size * num_images_per_prompt, 1
+        )
+        style = style.to(device=device).repeat(batch_size * num_images_per_prompt)
+        # 9. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        # preparations for diff diff
+        original_with_noise = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            init_image,
+            timesteps,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+        thresholds = torch.arange(total_time_steps, dtype=map.dtype) / total_time_steps
+        thresholds = thresholds.unsqueeze(1).unsqueeze(1).to(device)
+        masks = map.squeeze() > (thresholds + (denoising_start or 0))
+        # end diff diff preparations
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                # diff diff
+                if i == 0 and denoising_start is None:
+                    latents = original_with_noise[:1]
+                else:
+                    mask = masks[i].unsqueeze(0).to(latents.dtype)
+                    mask = mask.unsqueeze(1)  # fit shape
+                    latents = original_with_noise[i] * mask + latents * (1 - mask)
+                # end diff diff
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input
+                t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to(
+                    dtype=latent_model_input.dtype
+                )
+
+                # predict the noise residual
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    t_expand,
+                    encoder_hidden_states=prompt_embeds,
+                    text_embedding_mask=prompt_attention_mask,
+                    encoder_hidden_states_t5=prompt_embeds_2,
+                    text_embedding_mask_t5=prompt_attention_mask_2,
+                    image_meta_size=add_time_ids,
+                    style=style,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                )[0]
+
+                noise_pred, _ = noise_pred.chunk(2, dim=1)
+
+                # perform guidance
+                if self.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 self.do_classifier_free_guidance and guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                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)
+
+                    if callback_outputs is not None:
+                        latents = callback_outputs.pop("latents", latents)
+                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                        prompt_embeds_2 = callback_outputs.pop("prompt_embeds_2", prompt_embeds_2)
+                        negative_prompt_embeds_2 = callback_outputs.pop(
+                            "negative_prompt_embeds_2", negative_prompt_embeds_2
+                        )
+
+                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 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 all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)