automatic/pipelines/step1x/pipeline_step1x_edit.py

# Copyright 2025 Step1X-Edit Team 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 Any, Callable, Dict, List, Optional, Union

import numpy as np
from PIL import Image
import torch
import math
from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor

from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from pipelines.step1x.transformer_step1x_edit import Step1XEditTransformer2DModel
from pipelines.step1x.pipeline_output import Step1XEditPipelineOutput

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 Step1XEditPipeline
        >>> from diffusers.utils import load_image

        >>> pipe = Step1XEditPipeline.from_pretrained("stepfun-ai/Step1X-Edit-v1p1-diffusers", torch_dtype=torch.bfloat16)
        >>> pipe.to("cuda")
        >>> image = load_image(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png"
        ... ).convert("RGB")
        >>> prompt = "Make Pikachu hold a sign that says 'Step1X-Edit is awesome', yarn art style, detailed, vibrant colors"

        >>> image = pipe(
                image=image,
            prompt=prompt,
                num_inference_steps=28,
                true_cfg_scale=6.0,
                generator=torch.Generator().manual_seed(42),
            ).images[0]
        >>> image.save("output.png")
        ```
"""


 # Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu


# 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,
):
    r"""
    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


# 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")


class Step1XEditPipeline(DiffusionPipeline):
    r"""
    The Step1X-Edit pipeline for image-to-image and text-to-image generation.

    Reference: https://arxiv.org/abs/2504.17761

    Args:
        transformer ([`Step1XEditTransformer2DModel`]):
            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`Qwen2.5-VL-7B-Instruct`]):
            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
        processor (`Qwen2_5_VLProcessor`):
            [Qwen2_5_VLProcessor](https://huggingface.co/docs/transformers/v4.53.3/en/model_doc/qwen2_5_vl#transformers.Qwen2_5_VLProcessor).
    """

    model_cpu_offload_seq = "text_encoder->transformer->vae"
    _callback_tensor_inputs = ["latents", "prompt_embeds"]

    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: Qwen2_5_VLForConditionalGeneration,
        processor: Qwen2_5_VLProcessor,
        transformer: Step1XEditTransformer2DModel,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            processor=processor,
            transformer=transformer,
            scheduler=scheduler,
        )
        self.image_encoder=None
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        # Step1X-Edit latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible
        # by the patch size. So the vae scale factor is multiplied by the patch size to account for this
        self.latent_channels = self.vae.config.latent_channels if getattr(self, "vae", None) else 16
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
        self.max_token_length = 640
        self.default_sample_size = 128
        self.QWEN25VL_PREFIX = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:
- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.
- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n
Here are examples of how to transform or refine prompts:
- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.
- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n
Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:
User Prompt:'''

    def _split_string(self, s):
        s = s.replace("'", '"').replace("“", '"').replace("”", '"')  # use english quotes
        result = []
        in_quotes = False
        temp = ""

        for idx,char in enumerate(s):
            if char == '"' and idx>155: # system token
                temp += char
                if not in_quotes:
                    result.append(temp)
                    temp = ""

                in_quotes = not in_quotes
                continue
            if in_quotes:
                if char.isspace():
                    pass  # have space token

                result.append("“" + char + "”")
            else:
                temp += char

        if temp:
            result.append(temp)

        return result

    def _get_qwenvl_embeds(
        self,
        prompt: Union[str, List[str]],
        ref_image: Optional[torch.Tensor],
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = torch.bfloat16,
    ):
        text_list = prompt
        embs = torch.zeros(
            len(text_list),
            self.max_token_length,
            self.text_encoder.config.text_config.hidden_size,
            dtype=dtype,
            device=device,
        )
        masks = torch.zeros(
            len(text_list),
            self.max_token_length,
            dtype=torch.long,
            device=device,
        )

        if isinstance(ref_image, list):
            image_list = ref_image
        else:
            image_list = [ref_image] * len(text_list)

        if len(image_list) == 1 and len(text_list) > 1:
            image_list = image_list * len(text_list)
        if len(image_list) != len(text_list):
            raise ValueError(
                f"Mismatch between prompts ({len(text_list)}) and reference images ({len(image_list)})."
            )

        for idx, (txt, imgs) in enumerate(zip(text_list, image_list)):

            messages = [
                {
                    "role": "user",
                    "content": []
                }
            ]

            messages[0]["content"].append({"type": "text", "text": f"{self.QWEN25VL_PREFIX}"})
            messages[0]['content'].append({"type": "image", "image": imgs})
            messages[0]["content"].append({"type": "text", "text": f"{txt}"})

            # Preparation for inference
            text = self.processor.apply_chat_template(
                messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
            )
            imgs = imgs.convert("RGB")
            min_pixels = 4 * 28 * 28
            max_pixels = 16384 * 28 * 28
            width, height = imgs.size
            h_bar = max(28, round(height / 28) * 28)
            w_bar = max(28, round(width / 28) * 28)
            if h_bar * w_bar > max_pixels:
                beta = math.sqrt((height * width) / max_pixels)
                h_bar = math.floor(height / beta / 28) * 28
                w_bar = math.floor(width / beta / 28) * 28
            elif h_bar * w_bar < min_pixels:
                beta = math.sqrt(min_pixels / (height * width))
                h_bar = math.ceil(height * beta / 28) * 28
                w_bar = math.ceil(width * beta / 28) * 28
            image_inputs = [imgs.resize((w_bar, h_bar))]

            inputs = self.processor(
                text=[text],
                images=image_inputs,
                padding=True,
                return_tensors="pt",
            )

            old_inputs_ids = inputs.input_ids
            text_split_list = self._split_string(text)

            token_list = []
            for text_each in text_split_list:
                txt_inputs = self.processor(
                    text=text_each,
                    images=None,
                    videos=None,
                    padding=True,
                    return_tensors="pt",
                )
                token_each=txt_inputs.input_ids
                if token_each[0][0] == 2073 and token_each[0][-1] == 854:
                    token_each = token_each[:,1:-1]
                    token_list.append(token_each)
                else:
                    token_list.append(token_each)

            new_txt_ids = torch.cat(token_list, dim=1).to(device)

            new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
            idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0]
            idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0]
            inputs.input_ids = torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0).unsqueeze(0).to(device)
            inputs.attention_mask = (inputs.input_ids > 0).long().to(device)
            outputs = self.text_encoder(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, pixel_values=inputs.pixel_values.to(device), image_grid_thw=inputs.image_grid_thw.to(device), output_hidden_states=True)

            emb = outputs['hidden_states'][-1]
            embs[idx,:min(self.max_token_length,emb.shape[1]-217)] = emb[0,217:][:self.max_token_length]
            masks[idx, :min(self.max_token_length, emb.shape[1] - 217)] = torch.ones((min(self.max_token_length, emb.shape[1] - 217)), dtype=torch.long, device=device)

        return embs, masks

    def encode_prompt(
        self,
        ref_image: Optional[torch.Tensor],
        prompt: Union[str, List[str]],
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        prompt_embeds: Optional[torch.Tensor] = None,
        prompt_embeds_mask: Optional[torch.Tensor] = None,
    ):
        r"""

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            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.
        """
        device = device or self._execution_device

        ref_image = [ref_image] if isinstance(prompt, str) else ref_image # change
        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]

        if prompt_embeds is None:
            prompt_embeds, prompt_embeds_mask = self._get_qwenvl_embeds(prompt, ref_image, device)

        _, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device)

        return prompt_embeds, prompt_embeds_mask, text_ids

    def encode_image(
        self,
        image: Optional[torch.Tensor],
        width: Optional[int] = None,
        height: Optional[int] = None,
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
    ):

        if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
            img_info = image.size
            width, height = img_info
            aspect_ratio = width / height

            if width > height:
                width_new = math.ceil(math.sqrt(1024 * 1024 * aspect_ratio))
                height_new = math.ceil(width_new / aspect_ratio)
            else:
                height_new = math.ceil(math.sqrt(1024 * 1024 / aspect_ratio))
                width_new = math.ceil(height_new * aspect_ratio)

            multiple_of = self.vae_scale_factor * 2
            height_new = height_new // multiple_of * multiple_of
            width_new = width_new // multiple_of * multiple_of

            if height != height_new or width != width_new:
                logger.warning(
                    f"Generation `height` and `width` have been adjusted to {height_new} and {width_new} to fit the model requirements."
                )
            height, width = height_new, width_new
            ref_image = self.image_processor.resize(image, height, width)
            image = self.image_processor.preprocess(ref_image, height, width).contiguous()
        else:
            width = width if width is not None else 1024
            height = height if height is not None else 1024
            img_info = (width, height)
            # Keep t2i reference image size aligned with requested output size.
            ref_image = torch.zeros(3, height, width).unsqueeze(0).to(device)
            ref_image = self.image_processor.pt_to_numpy(ref_image)
            ref_image = self.image_processor.numpy_to_pil(ref_image)[0]
            image = None

        return image, ref_image, img_info, width, height

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        height,
        width,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        prompt_embeds_mask=None,
        negative_prompt_embeds_mask=None,
        callback_on_step_end_tensor_inputs=None,
    ):
        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
            logger.warning(
                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
            )

        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 not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and prompt_embeds_mask is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
            )
        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
            raise ValueError(
                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
            )

    @staticmethod
    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
        latent_image_ids = torch.zeros(height, width, 3)
        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]

        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape

        latent_image_ids = latent_image_ids.reshape(
            latent_image_id_height * latent_image_id_width, latent_image_id_channels
        )

        return latent_image_ids.to(device=device, dtype=dtype)

    @staticmethod
    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents
    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
        latents = latents.permute(0, 2, 4, 1, 3, 5)
        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)

        return latents

    @staticmethod
    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._unpack_latents
    def _unpack_latents(latents, height, width, vae_scale_factor):
        batch_size, _num_patches, channels = latents.shape

        # VAE applies 8x compression on images but we must also account for packing which requires
        # latent height and width to be divisible by 2.
        height = 2 * (int(height) // (vae_scale_factor * 2))
        width = 2 * (int(width) // (vae_scale_factor * 2))

        latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
        latents = latents.permute(0, 3, 1, 4, 2, 5)

        latents = latents.reshape(batch_size, channels // (2 * 2), height, width)

        return latents

    @staticmethod
    def _output_process_image(image, image_size):
        def _to_pil(img):
            arr = np.asarray(img)

            # Handle CHW tensors by converting to HWC for PIL.
            if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
                arr = np.transpose(arr, (1, 2, 0))

            if np.issubdtype(arr.dtype, np.floating):
                arr = np.nan_to_num(arr)
                if arr.size > 0:
                    arr_min = float(arr.min())
                    arr_max = float(arr.max())
                    if arr_min >= -1.0 and arr_max <= 1.0:
                        if arr_min < 0.0:
                            arr = (arr + 1.0) * 0.5
                        arr = arr * 255.0
                arr = np.clip(arr, 0.0, 255.0).astype(np.uint8)
            elif arr.dtype != np.uint8:
                arr = np.clip(arr, 0, 255).astype(np.uint8)

            return Image.fromarray(arr)

        if isinstance(image, np.ndarray):
            images = image if image.ndim == 4 else [image]
            resized = [np.array(_to_pil(img).resize(image_size)) for img in images]
            output = np.stack(resized, axis=0)
            return output if image.ndim == 4 else output[0]
        if len(image) > 0 and isinstance(image[0], np.ndarray):
            resized = [np.array(_to_pil(img).resize(image_size)) for img in image]
            return np.stack(resized, axis=0)
        return [img.resize(image_size) for img in image]

    @staticmethod
    def process_diff_norm(diff_norm, k):
        pow_result = torch.pow(diff_norm, k)

        result = torch.where(
            diff_norm > 1.0,
            pow_result,
            torch.where(diff_norm < 1.0, torch.ones_like(diff_norm), diff_norm),
        )
        return result

    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
        image_latents = None
        if isinstance(generator, list):
            image_latents = [
                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="sample")
                for i in range(image.shape[0])
            ]
            image_latents = torch.cat(image_latents, dim=0)
        else:
            image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="sample")

        image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor

        return image_latents

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.enable_vae_slicing
    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_slicing
    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.enable_vae_tiling
    def enable_vae_tiling(self):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger images.
        """
        self.vae.enable_tiling()

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
    def disable_vae_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_tiling()

    def prepare_latents(
        self,
        image: Optional[torch.Tensor],
        batch_size: int,
        num_channels_latents: int,
        height: int,
        width: int,
        dtype: torch.dtype,
        device: torch.device,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
    ):
        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."
            )

        # VAE applies 8x compression on images but we must also account for packing which requires
        # latent height and width to be divisible by 2.
        height = 2 * (int(height) // (self.vae_scale_factor * 2))
        width = 2 * (int(width) // (self.vae_scale_factor * 2))
        shape = (batch_size, num_channels_latents, height, width)

        image_latents = image_ids = None
        if image is not None:
            image = image.to(device=device, dtype=dtype)

            if image.shape[1] != self.latent_channels:
                image_latents = self._encode_vae_image(image=image, generator=generator)
            else:
                image_latents = image
            if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
                # expand init_latents for batch_size
                additional_image_per_prompt = batch_size // image_latents.shape[0]
                image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
            elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
                raise ValueError(
                    f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
                )
            else:
                image_latents = torch.cat([image_latents], dim=0)

            image_latent_height, image_latent_width = image_latents.shape[2:]
            image_latents = self._pack_latents(
                image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
            )
            image_ids = self._prepare_latent_image_ids(
                batch_size, image_latent_height // 2, image_latent_width // 2, device, torch.float32  # change
                # batch_size, image_latent_height // 2, image_latent_width // 2, device, dtype
            )
            # image ids are the same as latent ids with the first dimension set to 1 instead of 0
            image_ids[..., 0] = 1
            image_ids[..., 1] = image_ids[..., 1] + 1
            image_ids[..., 2] = image_ids[..., 2] + 1
        latent_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)   # change
            latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
        else:
            latents = latents.to(device=device, dtype=dtype)

        return latents, image_latents, latent_ids, image_ids

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def joint_attention_kwargs(self):
        return self._joint_attention_kwargs

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @property
    def current_timestep(self):
        return self._current_timestep

    @property
    def interrupt(self):
        return self._interrupt

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        image: Optional[PipelineImageInput] = None,
        prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        true_cfg_scale: float = 6.0,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 28,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 6.0,
        num_images_per_prompt: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        prompt_embeds_mask: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds_mask: Optional[torch.Tensor] = 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,
        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"],
        timesteps_truncate: float = 0.93,
        process_norm_power: float = 0.4
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            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.
            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.
            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`).
            true_cfg_scale (`float`, *optional*, defaults to 6.0):
                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 28):
                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 3.5):
                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.Tensor`, *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 be generated by sampling using the supplied random `generator`.
            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.
            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.step1x_edit.Step1XEditPipelineOutput`] 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.

        Examples:

        Returns:
            [`~pipelines.step1x_edit.Step1XEditPipelineOutput`] or `tuple`:
            [`~pipelines.step1x_edit.Step1XEditPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is a list with the generated images.
        """

        device = self._execution_device

        # 1. Preprocess image
        image, ref_image, img_info, width, height = self.encode_image(
            image=image[0] if isinstance(image, list) and len(image) > 0 else None,
            width=width,
            height=height,
            device=device,
            num_images_per_prompt=num_images_per_prompt
        )

        # 2. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            prompt_embeds_mask=prompt_embeds_mask,
            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        )

        self._guidance_scale = guidance_scale
        self._joint_attention_kwargs = joint_attention_kwargs
        self._current_timestep = None
        self._interrupt = False

        # 3. 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]

        lora_scale = (
            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
        )
        has_neg_prompt = negative_prompt is not None or (
            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
        )
        if not has_neg_prompt:
            negative_prompt = "" if image is not None else "worst quality, wrong limbs, unreasonable limbs, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting"
        do_true_cfg = true_cfg_scale > 1
        negative_text_ids = None
        if (
            image is None
            and do_true_cfg
            and prompt_embeds is None
            and negative_prompt_embeds is None
            and isinstance(prompt, str)
            and isinstance(negative_prompt, str)
        ):
            combined_prompts = [prompt, negative_prompt]
            combined_ref_images = [ref_image, ref_image]
            (
                combined_prompt_embeds,
                combined_prompt_embeds_mask,
                text_ids,
            ) = self.encode_prompt(
                ref_image=combined_ref_images,
                prompt=combined_prompts,
                prompt_embeds=None,
                prompt_embeds_mask=None,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
            )
            prompt_embeds, negative_prompt_embeds = combined_prompt_embeds.chunk(2, dim=0)
            prompt_embeds_mask, negative_prompt_embeds_mask = combined_prompt_embeds_mask.chunk(2, dim=0)
            negative_text_ids = text_ids
        else:
            (
                prompt_embeds,
                prompt_embeds_mask,
                text_ids
            ) = self.encode_prompt(
                ref_image=ref_image,
                prompt=prompt,
                prompt_embeds=prompt_embeds,
                prompt_embeds_mask=prompt_embeds_mask,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
            )
            negative_text_ids = text_ids
            if do_true_cfg:
                (
                    negative_prompt_embeds,
                    negative_prompt_embeds_mask,
                    negative_text_ids,
                ) = self.encode_prompt(
                    ref_image=ref_image,
                    prompt=negative_prompt,
                    prompt_embeds=negative_prompt_embeds,
                    prompt_embeds_mask=negative_prompt_embeds_mask,
                    device=device,
                    num_images_per_prompt=num_images_per_prompt,
                )

        if do_true_cfg and negative_text_ids is None:
            negative_text_ids = text_ids

        # 4. Prepare latent variables
        num_channels_latents = self.transformer.config.in_channels // 4
        latents, image_latents, latent_ids, image_ids = self.prepare_latents(
            image,
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        if image_ids is not None:
            latent_ids = torch.cat([latent_ids, image_ids], dim=0)  # dim 0 is sequence dimension

        # 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)

        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)
            negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters

        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)
            ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters

        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
        # We set the index here to remove DtoH sync, helpful especially during compilation.
        # Check out more details here: https://github.com/huggingface/diffusers/pull/11696
        is_t2i = image_latents is None
        self.scheduler.set_begin_index(0)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                self._current_timestep = t
                if image_embeds is not None:
                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds

                latent_model_input = latents
                if image_latents is not None:
                    latent_model_input = torch.cat([latents, image_latents], dim=1)
                timestep = t.expand(latents.shape[0]).to(latents.dtype)

                if is_t2i and do_true_cfg:
                    # Reference implementation uses a dedicated t2i denoise path
                    # where cond/uncond are evaluated in one forward pass.
                    cfg_hidden_states = torch.cat([latent_model_input, latent_model_input], dim=0)
                    cfg_timestep = torch.cat([timestep, timestep], dim=0)
                    cfg_prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds], dim=0)
                    cfg_prompt_embeds_mask = torch.cat([prompt_embeds_mask, negative_prompt_embeds_mask], dim=0)

                    cfg_guidance = guidance
                    if cfg_guidance is not None:
                        cfg_guidance = torch.cat([guidance, guidance], dim=0)

                    if image_embeds is not None and negative_image_embeds is not None:
                        self._joint_attention_kwargs["ip_adapter_image_embeds"] = [
                            torch.cat([img_embed, neg_img_embed], dim=0)
                            for img_embed, neg_img_embed in zip(image_embeds, negative_image_embeds)
                        ]

                    cfg_noise_pred = self.transformer(
                        hidden_states=cfg_hidden_states,
                        timestep=cfg_timestep / 1000,
                        guidance=cfg_guidance,
                        encoder_hidden_states=cfg_prompt_embeds,
                        prompt_embeds_mask=cfg_prompt_embeds_mask,
                        txt_ids=text_ids,
                        img_ids=latent_ids,
                        joint_attention_kwargs=self.joint_attention_kwargs,
                        return_dict=False,
                    )[0]
                    cfg_noise_pred = cfg_noise_pred[:, : latents.size(1)]
                    noise_pred, neg_noise_pred = cfg_noise_pred.chunk(2, dim=0)

                    if t.item() > timesteps_truncate:
                        diff = noise_pred - neg_noise_pred
                        diff_norm = torch.norm(diff, dim=(2), keepdim=True)
                        noise_pred = neg_noise_pred + true_cfg_scale * (
                            noise_pred - neg_noise_pred
                        ) / self.process_diff_norm(diff_norm, k=process_norm_power)
                    else:
                        noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
                else:
                    noise_pred = self.transformer(
                        hidden_states=latent_model_input,
                        timestep=timestep / 1000,
                        guidance=guidance,
                        encoder_hidden_states=prompt_embeds,
                        prompt_embeds_mask=prompt_embeds_mask,
                        txt_ids=text_ids,
                        img_ids=latent_ids,
                        joint_attention_kwargs=self.joint_attention_kwargs,
                        return_dict=False,
                    )[0]
                    noise_pred = noise_pred[:, : latents.size(1)]

                    if do_true_cfg:
                        if negative_image_embeds is not None:
                            self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
                        neg_noise_pred = self.transformer(
                            hidden_states=latent_model_input,
                            timestep=timestep / 1000,
                            guidance=guidance,
                            encoder_hidden_states=negative_prompt_embeds,
                            prompt_embeds_mask=negative_prompt_embeds_mask,
                            txt_ids=negative_text_ids,
                            img_ids=latent_ids,
                            joint_attention_kwargs=self.joint_attention_kwargs,
                            return_dict=False,
                        )[0]
                        neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
                        if t.item() > timesteps_truncate:
                            diff = noise_pred - neg_noise_pred
                            diff_norm = torch.norm(diff, dim=(2), keepdim=True)
                            noise_pred = neg_noise_pred + true_cfg_scale * (
                                noise_pred - neg_noise_pred
                            ) / self.process_diff_norm(diff_norm, k=process_norm_power)
                        else:
                            noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)

                # compute the previous noisy sample x_t -> x_t-1
                latents_dtype = latents.dtype
                if is_t2i:
                    t_prev = timesteps[i + 1] if i + 1 < len(timesteps) else latents.new_tensor(0.0)
                    # Timesteps are fed to the model in /1000 scale, so use the same
                    # normalized delta for manual t2i update.
                    dt = ((t_prev - t) / 1000).to(latents.dtype)
                    latents = latents + dt * noise_pred
                else:
                    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()

        self._current_timestep = None

        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)
            image = self._output_process_image(image, img_info)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return Step1XEditPipelineOutput(images=image)