modules.py

import math
import inspect
import numpy as np
from typing import Any, Dict, Optional, Tuple, Union, List, Callable

import torch
import torch.nn.functional as F
from einops import rearrange

from diffusers.models.attention import _chunked_feed_forward
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput
from diffusers.pipelines.flux.pipeline_flux import (
    retrieve_timesteps,
    replace_example_docstring,
    EXAMPLE_DOC_STRING,
    calculate_shift,
    XLA_AVAILABLE,
    FluxPipelineOutput
)
# from diffusers.models.transformers import FLUXTransformer2DModel
from diffusers.utils import (
    deprecate,
    BaseOutput,
    is_torch_version,
    logging,
    USE_PEFT_BACKEND,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
)


logger = logging.get_logger(__name__)


attn_maps = {}


@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def FluxPipeline_call(
    self,
    prompt: Union[str, List[str]] = None,
    prompt_2: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 28,
    timesteps: List[int] = None,
    guidance_scale: float = 3.5,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    latents: Optional[torch.FloatTensor] = None,
    prompt_embeds: Optional[torch.FloatTensor] = None,
    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
    callback_on_step_end_tensor_inputs: List[str] = ["latents"],
    max_sequence_length: int = 512,
):
    r"""
    Function invoked when calling the pipeline for generation.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
            instead.
        prompt_2 (`str` or `List[str]`, *optional*):
            The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
            will be used instead
        height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
            The height in pixels of the generated image. This is set to 1024 by default for the best results.
        width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
            The width in pixels of the generated image. This is set to 1024 by default for the best results.
        num_inference_steps (`int`, *optional*, defaults to 50):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        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.
        guidance_scale (`float`, *optional*, defaults to 7.0):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
            One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
            to make generation deterministic.
        latents (`torch.FloatTensor`, *optional*):
            Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
            generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
            tensor will ge generated by sampling using the supplied random `generator`.
        prompt_embeds (`torch.FloatTensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
            provided, text embeddings will be generated from `prompt` input argument.
        pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
            Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
            If not provided, pooled text embeddings will be generated from `prompt` input argument.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
        joint_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        callback_on_step_end (`Callable`, *optional*):
            A function that calls at the end of each denoising steps during the inference. The function is called
            with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
            callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
            `callback_on_step_end_tensor_inputs`.
        callback_on_step_end_tensor_inputs (`List`, *optional*):
            The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
            will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
            `._callback_tensor_inputs` attribute of your pipeline class.
        max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.

    Examples:

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

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

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

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

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

    device = self._execution_device

    lora_scale = (
        self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
    )
    (
        prompt_embeds,
        pooled_prompt_embeds,
        text_ids,
    ) = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        device=device,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )

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

    # 5. Prepare timesteps
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    image_seq_len = latents.shape[1]
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.base_image_seq_len,
        self.scheduler.config.max_image_seq_len,
        self.scheduler.config.base_shift,
        self.scheduler.config.max_shift,
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        device,
        timesteps,
        sigmas,
        mu=mu,
    )
    num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
    self._num_timesteps = len(timesteps)

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

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

            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            timestep = t.expand(latents.shape[0]).to(latents.dtype)

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

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

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

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

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

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

            if XLA_AVAILABLE:
                xm.mark_step()

    if output_type == "latent":
        image = latents

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

    # Offload all models
    self.maybe_free_model_hooks()

    if not return_dict:
        return (image,)

    return FluxPipelineOutput(images=image)


def UNet2DConditionModelForward(
    self,
    sample: torch.Tensor,
    timestep: Union[torch.Tensor, float, int],
    encoder_hidden_states: torch.Tensor,
    class_labels: Optional[torch.Tensor] = None,
    timestep_cond: Optional[torch.Tensor] = None,
    attention_mask: Optional[torch.Tensor] = None,
    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
    mid_block_additional_residual: Optional[torch.Tensor] = None,
    down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None,
    return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
    r"""
    The [`UNet2DConditionModel`] forward method.

    Args:
        sample (`torch.Tensor`):
            The noisy input tensor with the following shape `(batch, channel, height, width)`.
        timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
        encoder_hidden_states (`torch.Tensor`):
            The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
        class_labels (`torch.Tensor`, *optional*, defaults to `None`):
            Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
        timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
            Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
            through the `self.time_embedding` layer to obtain the timestep embeddings.
        attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
            is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
            negative values to the attention scores corresponding to "discard" tokens.
        cross_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).
        added_cond_kwargs: (`dict`, *optional*):
            A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
            are passed along to the UNet blocks.
        down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
            A tuple of tensors that if specified are added to the residuals of down unet blocks.
        mid_block_additional_residual: (`torch.Tensor`, *optional*):
            A tensor that if specified is added to the residual of the middle unet block.
        down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
            additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
        encoder_attention_mask (`torch.Tensor`):
            A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
            `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
            which adds large negative values to the attention scores corresponding to "discard" tokens.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
            tuple.

    Returns:
        [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
            If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
            otherwise a `tuple` is returned where the first element is the sample tensor.
    """
    # By default samples have to be AT least a multiple of the overall upsampling factor.
    # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
    # However, the upsampling interpolation output size can be forced to fit any upsampling size
    # on the fly if necessary.
    default_overall_up_factor = 2**self.num_upsamplers

    # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
    forward_upsample_size = False
    upsample_size = None

    for dim in sample.shape[-2:]:
        if dim % default_overall_up_factor != 0:
            # Forward upsample size to force interpolation output size.
            forward_upsample_size = True
            break

    # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
    # expects mask of shape:
    #   [batch, key_tokens]
    # adds singleton query_tokens dimension:
    #   [batch,                    1, key_tokens]
    # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
    #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
    #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
    if attention_mask is not None:
        # assume that mask is expressed as:
        #   (1 = keep,      0 = discard)
        # convert mask into a bias that can be added to attention scores:
        #       (keep = +0,     discard = -10000.0)
        attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
        attention_mask = attention_mask.unsqueeze(1)

    # convert encoder_attention_mask to a bias the same way we do for attention_mask
    if encoder_attention_mask is not None:
        encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
        encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

    # 0. center input if necessary
    if self.config.center_input_sample:
        sample = 2 * sample - 1.0

    # 1. time
    t_emb = self.get_time_embed(sample=sample, timestep=timestep)
    emb = self.time_embedding(t_emb, timestep_cond)
    aug_emb = None

    class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
    if class_emb is not None:
        if self.config.class_embeddings_concat:
            emb = torch.cat([emb, class_emb], dim=-1)
        else:
            emb = emb + class_emb

    aug_emb = self.get_aug_embed(
        emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
    )
    if self.config.addition_embed_type == "image_hint":
        aug_emb, hint = aug_emb
        sample = torch.cat([sample, hint], dim=1)

    emb = emb + aug_emb if aug_emb is not None else emb

    if self.time_embed_act is not None:
        emb = self.time_embed_act(emb)

    encoder_hidden_states = self.process_encoder_hidden_states(
        encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
    )

    # 2. pre-process
    sample = self.conv_in(sample)

    # 2.5 GLIGEN position net
    if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
        cross_attention_kwargs = cross_attention_kwargs.copy()
        gligen_args = cross_attention_kwargs.pop("gligen")
        cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}

    # 3. down
    # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
    # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
    ################################################################################
    if cross_attention_kwargs is None:
        cross_attention_kwargs = {'timestep' : timestep}
    else:
        cross_attention_kwargs['timestep'] = timestep
    ################################################################################


    if cross_attention_kwargs is not None:
        cross_attention_kwargs = cross_attention_kwargs.copy()
        lora_scale = cross_attention_kwargs.pop("scale", 1.0)
    else:
        lora_scale = 1.0

    if USE_PEFT_BACKEND:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self, lora_scale)

    is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
    # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
    is_adapter = down_intrablock_additional_residuals is not None
    # maintain backward compatibility for legacy usage, where
    #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
    #       but can only use one or the other
    if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
        deprecate(
            "T2I should not use down_block_additional_residuals",
            "1.3.0",
            "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
                    and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
                    for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
            standard_warn=False,
        )
        down_intrablock_additional_residuals = down_block_additional_residuals
        is_adapter = True

    down_block_res_samples = (sample,)
    for downsample_block in self.down_blocks:
        if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
            # For t2i-adapter CrossAttnDownBlock2D
            additional_residuals = {}
            if is_adapter and len(down_intrablock_additional_residuals) > 0:
                additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

            sample, res_samples = downsample_block(
                hidden_states=sample,
                temb=emb,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=attention_mask,
                cross_attention_kwargs=cross_attention_kwargs,
                encoder_attention_mask=encoder_attention_mask,
                **additional_residuals,
            )
        else:
            sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
            if is_adapter and len(down_intrablock_additional_residuals) > 0:
                sample += down_intrablock_additional_residuals.pop(0)

        down_block_res_samples += res_samples

    if is_controlnet:
        new_down_block_res_samples = ()

        for down_block_res_sample, down_block_additional_residual in zip(
            down_block_res_samples, down_block_additional_residuals
        ):
            down_block_res_sample = down_block_res_sample + down_block_additional_residual
            new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

        down_block_res_samples = new_down_block_res_samples

    # 4. mid
    if self.mid_block is not None:
        if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
            sample = self.mid_block(
                sample,
                emb,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=attention_mask,
                cross_attention_kwargs=cross_attention_kwargs,
                encoder_attention_mask=encoder_attention_mask,
            )
        else:
            sample = self.mid_block(sample, emb)

        # To support T2I-Adapter-XL
        if (
            is_adapter
            and len(down_intrablock_additional_residuals) > 0
            and sample.shape == down_intrablock_additional_residuals[0].shape
        ):
            sample += down_intrablock_additional_residuals.pop(0)

    if is_controlnet:
        sample = sample + mid_block_additional_residual

    # 5. up
    for i, upsample_block in enumerate(self.up_blocks):
        is_final_block = i == len(self.up_blocks) - 1

        res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
        down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

        # if we have not reached the final block and need to forward the
        # upsample size, we do it here
        if not is_final_block and forward_upsample_size:
            upsample_size = down_block_res_samples[-1].shape[2:]

        if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
            sample = upsample_block(
                hidden_states=sample,
                temb=emb,
                res_hidden_states_tuple=res_samples,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                upsample_size=upsample_size,
                attention_mask=attention_mask,
                encoder_attention_mask=encoder_attention_mask,
            )
        else:
            sample = upsample_block(
                hidden_states=sample,
                temb=emb,
                res_hidden_states_tuple=res_samples,
                upsample_size=upsample_size,
            )

    # 6. post-process
    if self.conv_norm_out:
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
    sample = self.conv_out(sample)

    if USE_PEFT_BACKEND:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self, lora_scale)

    if not return_dict:
        return (sample,)

    return UNet2DConditionOutput(sample=sample)


def SD3Transformer2DModelForward(
    self,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor = None,
    pooled_projections: torch.FloatTensor = None,
    timestep: torch.LongTensor = None,
    block_controlnet_hidden_states: List = None,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
    """
    The [`SD3Transformer2DModel`] forward method.

    Args:
        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
            Input `hidden_states`.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
            from the embeddings of input conditions.
        timestep ( `torch.LongTensor`):
            Used to indicate denoising step.
        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
            A list of tensors that if specified are added to the residuals of transformer blocks.
        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).
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
            tuple.

    Returns:
        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
        `tuple` where the first element is the sample tensor.
    """
    if joint_attention_kwargs is not None:
        joint_attention_kwargs = joint_attention_kwargs.copy()
        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
    else:
        lora_scale = 1.0

    if USE_PEFT_BACKEND:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self, lora_scale)
    else:
        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
            logger.warning(
                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
            )

    height, width = hidden_states.shape[-2:]

    hidden_states = self.pos_embed(hidden_states)  # takes care of adding positional embeddings too.
    temb = self.time_text_embed(timestep, pooled_projections)
    encoder_hidden_states = self.context_embedder(encoder_hidden_states)

    for index_block, block in enumerate(self.transformer_blocks):
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module, return_dict=None):
                def custom_forward(*inputs):
                    if return_dict is not None:
                        return module(*inputs, return_dict=return_dict)
                    else:
                        return module(*inputs)

                return custom_forward

            ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
            encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                encoder_hidden_states,
                temb,
                **ckpt_kwargs,
            )

        else:
            encoder_hidden_states, hidden_states = block(
                hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb,
                ##########################################################################################
                timestep=timestep, height=height // self.config.patch_size,
                ##########################################################################################
            )

        # controlnet residual
        if block_controlnet_hidden_states is not None and block.context_pre_only is False:
            interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
            hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]

    hidden_states = self.norm_out(hidden_states, temb)
    hidden_states = self.proj_out(hidden_states)

    # unpatchify
    patch_size = self.config.patch_size
    height = height // patch_size
    width = width // patch_size

    hidden_states = hidden_states.reshape(
        shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
    )
    hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
    output = hidden_states.reshape(
        shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
    )

    if USE_PEFT_BACKEND:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self, lora_scale)

    if not return_dict:
        return (output,)

    return Transformer2DModelOutput(sample=output)


def FluxTransformer2DModelForward(
    self,
    hidden_states: torch.Tensor,
    encoder_hidden_states: torch.Tensor = None,
    pooled_projections: torch.Tensor = None,
    timestep: torch.LongTensor = None,
    img_ids: torch.Tensor = None,
    txt_ids: torch.Tensor = None,
    guidance: torch.Tensor = None,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    controlnet_block_samples=None,
    controlnet_single_block_samples=None,
    return_dict: bool = True,
    controlnet_blocks_repeat: bool = False,
    ##################################################
    height: int = None,
    ##################################################
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
    """
    The [`FluxTransformer2DModel`] forward method.

    Args:
        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
            Input `hidden_states`.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
            from the embeddings of input conditions.
        timestep ( `torch.LongTensor`):
            Used to indicate denoising step.
        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
            A list of tensors that if specified are added to the residuals of transformer blocks.
        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).
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
            tuple.

    Returns:
        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
        `tuple` where the first element is the sample tensor.
    """
    if joint_attention_kwargs is not None:
        joint_attention_kwargs = joint_attention_kwargs.copy()
        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
    else:
        lora_scale = 1.0

    if USE_PEFT_BACKEND:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self, lora_scale)
    else:
        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
            logger.warning(
                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
            )
    hidden_states = self.x_embedder(hidden_states)

    timestep = timestep.to(hidden_states.dtype) * 1000
    if guidance is not None:
        guidance = guidance.to(hidden_states.dtype) * 1000
    else:
        guidance = None
    temb = (
        self.time_text_embed(timestep, pooled_projections)
        if guidance is None
        else self.time_text_embed(timestep, guidance, pooled_projections)
    )
    encoder_hidden_states = self.context_embedder(encoder_hidden_states)

    if txt_ids.ndim == 3:
        logger.warning(
            "Passing `txt_ids` 3d torch.Tensor is deprecated."
            "Please remove the batch dimension and pass it as a 2d torch Tensor"
        )
        txt_ids = txt_ids[0]
    if img_ids.ndim == 3:
        logger.warning(
            "Passing `img_ids` 3d torch.Tensor is deprecated."
            "Please remove the batch dimension and pass it as a 2d torch Tensor"
        )
        img_ids = img_ids[0]

    ids = torch.cat((txt_ids, img_ids), dim=0)
    image_rotary_emb = self.pos_embed(ids)

    for index_block, block in enumerate(self.transformer_blocks):
        if torch.is_grad_enabled() and self.gradient_checkpointing:

            def create_custom_forward(module, return_dict=None):
                def custom_forward(*inputs):
                    if return_dict is not None:
                        return module(*inputs, return_dict=return_dict)
                    else:
                        return module(*inputs)

                return custom_forward

            ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
            encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                encoder_hidden_states,
                temb,
                image_rotary_emb,
                **ckpt_kwargs,
            )

        else:
            encoder_hidden_states, hidden_states = block(
                hidden_states=hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                temb=temb,
                image_rotary_emb=image_rotary_emb,
                joint_attention_kwargs=joint_attention_kwargs,
                ##########################################################################################
                timestep=timestep, height=height // self.config.patch_size,
                ##########################################################################################
            )

        # controlnet residual
        if controlnet_block_samples is not None:
            interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
            interval_control = int(np.ceil(interval_control))
            # For Xlabs ControlNet.
            if controlnet_blocks_repeat:
                hidden_states = (
                    hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
                )
            else:
                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]

    hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

    for index_block, block in enumerate(self.single_transformer_blocks):
        if torch.is_grad_enabled() and self.gradient_checkpointing:

            def create_custom_forward(module, return_dict=None):
                def custom_forward(*inputs):
                    if return_dict is not None:
                        return module(*inputs, return_dict=return_dict)
                    else:
                        return module(*inputs)

                return custom_forward

            ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
            hidden_states = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                temb,
                image_rotary_emb,
                **ckpt_kwargs,
            )

        else:
            hidden_states = block(
                hidden_states=hidden_states,
                temb=temb,
                image_rotary_emb=image_rotary_emb,
                joint_attention_kwargs=joint_attention_kwargs,
            )

        # controlnet residual
        if controlnet_single_block_samples is not None:
            interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
            interval_control = int(np.ceil(interval_control))
            hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
                hidden_states[:, encoder_hidden_states.shape[1] :, ...]
                + controlnet_single_block_samples[index_block // interval_control]
            )

    hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]

    hidden_states = self.norm_out(hidden_states, temb)
    output = self.proj_out(hidden_states)

    if USE_PEFT_BACKEND:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self, lora_scale)

    if not return_dict:
        return (output,)

    return Transformer2DModelOutput(sample=output)


def Transformer2DModelForward(
    self,
    hidden_states: torch.Tensor,
    encoder_hidden_states: Optional[torch.Tensor] = None,
    timestep: Optional[torch.LongTensor] = None,
    added_cond_kwargs: Dict[str, torch.Tensor] = None,
    class_labels: Optional[torch.LongTensor] = None,
    cross_attention_kwargs: Dict[str, Any] = None,
    attention_mask: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None,
    return_dict: bool = True,
):
    """
    The [`Transformer2DModel`] forward method.

    Args:
        hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):
            Input `hidden_states`.
        encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
            Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
            self-attention.
        timestep ( `torch.LongTensor`, *optional*):
            Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
        class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
            Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
            `AdaLayerZeroNorm`.
        cross_attention_kwargs ( `Dict[str, Any]`, *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).
        attention_mask ( `torch.Tensor`, *optional*):
            An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
            is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
            negative values to the attention scores corresponding to "discard" tokens.
        encoder_attention_mask ( `torch.Tensor`, *optional*):
            Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                * Mask `(batch, sequence_length)` True = keep, False = discard.
                * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

            If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
            above. This bias will be added to the cross-attention scores.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
            tuple.

    Returns:
        If `return_dict` is True, an [`~models.transformers.transformer_2d.Transformer2DModelOutput`] is returned,
        otherwise a `tuple` where the first element is the sample tensor.
    """
    if cross_attention_kwargs is not None:
        if cross_attention_kwargs.get("scale", None) is not None:
            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
    # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
    #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
    #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
    # expects mask of shape:
    #   [batch, key_tokens]
    # adds singleton query_tokens dimension:
    #   [batch,                    1, key_tokens]
    # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
    #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
    #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
    if attention_mask is not None and attention_mask.ndim == 2:
        # assume that mask is expressed as:
        #   (1 = keep,      0 = discard)
        # convert mask into a bias that can be added to attention scores:
        #       (keep = +0,     discard = -10000.0)
        attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
        attention_mask = attention_mask.unsqueeze(1)

    # convert encoder_attention_mask to a bias the same way we do for attention_mask
    if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
        encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
        encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

    # 1. Input
    if self.is_input_continuous:
        batch_size, _, height, width = hidden_states.shape
        residual = hidden_states
        hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states)
    elif self.is_input_vectorized:
        hidden_states = self.latent_image_embedding(hidden_states)
    elif self.is_input_patches:
        height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
        hidden_states, encoder_hidden_states, timestep, embedded_timestep = self._operate_on_patched_inputs(
            hidden_states, encoder_hidden_states, timestep, added_cond_kwargs
        )

    ####################################################################################################
    cross_attention_kwargs['height'] = height
    cross_attention_kwargs['width'] = width
    ####################################################################################################

    # 2. Blocks
    for block in self.transformer_blocks:
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module, return_dict=None):
                def custom_forward(*inputs):
                    if return_dict is not None:
                        return module(*inputs, return_dict=return_dict)
                    else:
                        return module(*inputs)

                return custom_forward

            ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
            hidden_states = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                timestep,
                cross_attention_kwargs,
                class_labels,
                **ckpt_kwargs,
            )
        else:
            hidden_states = block(
                hidden_states,
                attention_mask=attention_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                timestep=timestep,
                cross_attention_kwargs=cross_attention_kwargs,
                class_labels=class_labels,
            )

    # 3. Output
    if self.is_input_continuous:
        output = self._get_output_for_continuous_inputs(
            hidden_states=hidden_states,
            residual=residual,
            batch_size=batch_size,
            height=height,
            width=width,
            inner_dim=inner_dim,
        )
    elif self.is_input_vectorized:
        output = self._get_output_for_vectorized_inputs(hidden_states)
    elif self.is_input_patches:
        output = self._get_output_for_patched_inputs(
            hidden_states=hidden_states,
            timestep=timestep,
            class_labels=class_labels,
            embedded_timestep=embedded_timestep,
            height=height,
            width=width,
        )

    if not return_dict:
        return (output,)

    return Transformer2DModelOutput(sample=output)


def BasicTransformerBlockForward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None,
    timestep: Optional[torch.LongTensor] = None,
    cross_attention_kwargs: Dict[str, Any] = None,
    class_labels: Optional[torch.LongTensor] = None,
    added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
    if cross_attention_kwargs is not None:
        if cross_attention_kwargs.get("scale", None) is not None:
            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")

    # Notice that normalization is always applied before the real computation in the following blocks.
    # 0. Self-Attention
    batch_size = hidden_states.shape[0]

    if self.norm_type == "ada_norm":
        norm_hidden_states = self.norm1(hidden_states, timestep)
    elif self.norm_type == "ada_norm_zero":
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
            hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
        )
    elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
        norm_hidden_states = self.norm1(hidden_states)
    elif self.norm_type == "ada_norm_continuous":
        norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
    elif self.norm_type == "ada_norm_single":
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
        ).chunk(6, dim=1)
        norm_hidden_states = self.norm1(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
        norm_hidden_states = norm_hidden_states.squeeze(1)
    else:
        raise ValueError("Incorrect norm used")

    if self.pos_embed is not None:
        norm_hidden_states = self.pos_embed(norm_hidden_states)

    # 1. Prepare GLIGEN inputs
    cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
    gligen_kwargs = cross_attention_kwargs.pop("gligen", None)

    ################################################################################
    attn_parameters = set(inspect.signature(self.attn1.processor.__call__).parameters.keys())
    ################################################################################

    attn_output = self.attn1(
        norm_hidden_states,
        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
        attention_mask=attention_mask,
        ################################################################################
        **{k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters},
        ################################################################################
    )
    if self.norm_type == "ada_norm_zero":
        attn_output = gate_msa.unsqueeze(1) * attn_output
    elif self.norm_type == "ada_norm_single":
        attn_output = gate_msa * attn_output

    hidden_states = attn_output + hidden_states
    if hidden_states.ndim == 4:
        hidden_states = hidden_states.squeeze(1)

    # 1.2 GLIGEN Control
    if gligen_kwargs is not None:
        hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])

    # 3. Cross-Attention
    if self.attn2 is not None:
        if self.norm_type == "ada_norm":
            norm_hidden_states = self.norm2(hidden_states, timestep)
        elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
            norm_hidden_states = self.norm2(hidden_states)
        elif self.norm_type == "ada_norm_single":
            # For PixArt norm2 isn't applied here:
            # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
            norm_hidden_states = hidden_states
        elif self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
        else:
            raise ValueError("Incorrect norm")

        if self.pos_embed is not None and self.norm_type != "ada_norm_single":
            norm_hidden_states = self.pos_embed(norm_hidden_states)

        attn_output = self.attn2(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=encoder_attention_mask,
            **cross_attention_kwargs,
        )
        hidden_states = attn_output + hidden_states

    # 4. Feed-forward
    # i2vgen doesn't have this norm 🤷‍♂️
    if self.norm_type == "ada_norm_continuous":
        norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
    elif not self.norm_type == "ada_norm_single":
        norm_hidden_states = self.norm3(hidden_states)

    if self.norm_type == "ada_norm_zero":
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

    if self.norm_type == "ada_norm_single":
        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp

    if self._chunk_size is not None:
        # "feed_forward_chunk_size" can be used to save memory
        ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
    else:
        ff_output = self.ff(norm_hidden_states)

    if self.norm_type == "ada_norm_zero":
        ff_output = gate_mlp.unsqueeze(1) * ff_output
    elif self.norm_type == "ada_norm_single":
        ff_output = gate_mlp * ff_output

    hidden_states = ff_output + hidden_states
    if hidden_states.ndim == 4:
        hidden_states = hidden_states.squeeze(1)

    return hidden_states


def JointTransformerBlockForward(
    self,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor,
    temb: torch.FloatTensor,
    ############################################################
    height: int = None,
    timestep: Optional[torch.Tensor] = None,
    ############################################################
):
    if self.use_dual_attention:
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(
            hidden_states, emb=temb
        )
    else:
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

    if self.context_pre_only:
        norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
    else:
        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
            encoder_hidden_states, emb=temb
        )

    # Attention.
    attn_output, context_attn_output = self.attn(
        hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states,
        ############################################################
        timestep=timestep, height=height,
        ############################################################
    )

    # Process attention outputs for the `hidden_states`.
    attn_output = gate_msa.unsqueeze(1) * attn_output
    hidden_states = hidden_states + attn_output

    if self.use_dual_attention:
        attn_output2 = self.attn2(hidden_states=norm_hidden_states2)
        attn_output2 = gate_msa2.unsqueeze(1) * attn_output2
        hidden_states = hidden_states + attn_output2

    norm_hidden_states = self.norm2(hidden_states)
    norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
    if self._chunk_size is not None:
        # "feed_forward_chunk_size" can be used to save memory
        ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
    else:
        ff_output = self.ff(norm_hidden_states)
    ff_output = gate_mlp.unsqueeze(1) * ff_output

    hidden_states = hidden_states + ff_output

    # Process attention outputs for the `encoder_hidden_states`.
    if self.context_pre_only:
        encoder_hidden_states = None
    else:
        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
        encoder_hidden_states = encoder_hidden_states + context_attn_output

        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            context_ff_output = _chunked_feed_forward(
                self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size
            )
        else:
            context_ff_output = self.ff_context(norm_encoder_hidden_states)
        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output

    return encoder_hidden_states, hidden_states


def FluxTransformerBlockForward(
    self,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor,
    temb: torch.FloatTensor,
    image_rotary_emb=None,
    joint_attention_kwargs=None,
    ############################################################
    height: int = None,
    timestep: Optional[torch.Tensor] = None,
    ############################################################
):
    norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

    norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
        encoder_hidden_states, emb=temb
    )
    joint_attention_kwargs = joint_attention_kwargs or {}
    # Attention.
    attn_output, context_attn_output = self.attn(
        hidden_states=norm_hidden_states,
        encoder_hidden_states=norm_encoder_hidden_states,
        image_rotary_emb=image_rotary_emb,
        ############################################################
        timestep=timestep, height=height,
        ############################################################
        **joint_attention_kwargs,
    )

    # Process attention outputs for the `hidden_states`.
    attn_output = gate_msa.unsqueeze(1) * attn_output
    hidden_states = hidden_states + attn_output

    norm_hidden_states = self.norm2(hidden_states)
    norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

    ff_output = self.ff(norm_hidden_states)
    ff_output = gate_mlp.unsqueeze(1) * ff_output

    hidden_states = hidden_states + ff_output

    # Process attention outputs for the `encoder_hidden_states`.

    context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
    encoder_hidden_states = encoder_hidden_states + context_attn_output

    norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
    norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]

    context_ff_output = self.ff_context(norm_encoder_hidden_states)
    encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
    if encoder_hidden_states.dtype == torch.float16:
        encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)

    return encoder_hidden_states, hidden_states


def attn_call(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        temb: Optional[torch.Tensor] = None,
        height: int = None,
        width: int = None,
        timestep: Optional[torch.Tensor] = None,
        *args,
        **kwargs,
    ) -> torch.Tensor:
        if len(args) > 0 or kwargs.get("scale", None) is not None:
            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
            deprecate("scale", "1.0.0", deprecation_message)

        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        ####################################################################################################
        if hasattr(self, "store_attn_map"):
            self.attn_map = rearrange(attention_probs, 'b (h w) d -> b d h w', h=height)
            self.timestep = int(timestep.item())
        ####################################################################################################
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
    # Efficient implementation equivalent to the following:
    L, S = query.size(-2), key.size(-2)
    scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
    attn_bias = torch.zeros(L, S, dtype=query.dtype)
    if is_causal:
        assert attn_mask is None
        temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)

    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_mask.masked_fill_(attn_mask.logical_not(), float("-inf"))
        else:
            attn_bias += attn_mask
    attn_weight = query @ key.transpose(-2, -1) * scale_factor
    attn_weight += attn_bias.to(attn_weight.device)
    attn_weight = torch.softmax(attn_weight, dim=-1)

    return torch.dropout(attn_weight, dropout_p, train=True) @ value, attn_weight


def attn_call2_0(
    self,
    attn: Attention,
    hidden_states: torch.Tensor,
    encoder_hidden_states: Optional[torch.Tensor] = None,
    attention_mask: Optional[torch.Tensor] = None,
    temb: Optional[torch.Tensor] = None,
    height: int = None,
    width: int = None,
    timestep: Optional[torch.Tensor] = None,
    *args,
    **kwargs,
) -> torch.Tensor:
    if len(args) > 0 or kwargs.get("scale", None) is not None:
        deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
        deprecate("scale", "1.0.0", deprecation_message)

    residual = hidden_states
    if attn.spatial_norm is not None:
        hidden_states = attn.spatial_norm(hidden_states, temb)

    input_ndim = hidden_states.ndim

    if input_ndim == 4:
        batch_size, channel, height, width = hidden_states.shape
        hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

    batch_size, sequence_length, _ = (
        hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
    )

    if attention_mask is not None:
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        # scaled_dot_product_attention expects attention_mask shape to be
        # (batch, heads, source_length, target_length)
        attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

    if attn.group_norm is not None:
        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

    query = attn.to_q(hidden_states)

    if encoder_hidden_states is None:
        encoder_hidden_states = hidden_states
    elif attn.norm_cross:
        encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

    key = attn.to_k(encoder_hidden_states)
    value = attn.to_v(encoder_hidden_states)

    inner_dim = key.shape[-1]
    head_dim = inner_dim // attn.heads

    query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

    key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
    value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

    # the output of sdp = (batch, num_heads, seq_len, head_dim)
    # TODO: add support for attn.scale when we move to Torch 2.1
    ####################################################################################################
    if hasattr(self, "store_attn_map"):
        hidden_states, attention_probs = scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        self.attn_map = rearrange(attention_probs, 'batch attn_head (h w) attn_dim -> batch attn_head h w attn_dim ', h=height) # detach height*width
        self.timestep = int(timestep.item())
    else:
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
    ####################################################################################################

    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) # (b,attn_head,h*w,attn_dim) -> (b,h*w,attn_head*attn_dim)
    hidden_states = hidden_states.to(query.dtype)

    # linear proj
    hidden_states = attn.to_out[0](hidden_states)
    # dropout
    hidden_states = attn.to_out[1](hidden_states)

    if input_ndim == 4:
        hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

    if attn.residual_connection:
        hidden_states = hidden_states + residual

    hidden_states = hidden_states / attn.rescale_output_factor

    return hidden_states


def lora_attn_call(self, attn: Attention, hidden_states, height, width, *args, **kwargs):
    self_cls_name = self.__class__.__name__
    deprecate(
        self_cls_name,
        "0.26.0",
        (
            f"Make sure use {self_cls_name[4:]} instead by setting"
            "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
            " `LoraLoaderMixin.load_lora_weights`"
        ),
    )
    attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
    attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
    attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
    attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

    attn._modules.pop("processor")
    attn.processor = AttnProcessor()
    ####################################################################################################
    attn.processor.__call__ = attn_call.__get__(attn.processor, AttnProcessor)
    ####################################################################################################

    if hasattr(self, "store_attn_map"):
        attn.processor.store_attn_map = True

    return attn.processor(attn, hidden_states, height, width, *args, **kwargs)


def lora_attn_call2_0(self, attn: Attention, hidden_states, height, width, *args, **kwargs):
    self_cls_name = self.__class__.__name__
    deprecate(
        self_cls_name,
        "0.26.0",
        (
            f"Make sure use {self_cls_name[4:]} instead by setting"
            "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
            " `LoraLoaderMixin.load_lora_weights`"
        ),
    )
    attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
    attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
    attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
    attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

    attn._modules.pop("processor")
    attn.processor = AttnProcessor2_0()
    ####################################################################################################
    attn.processor.__call__ = attn_call.__get__(attn.processor, AttnProcessor2_0)
    ####################################################################################################

    if hasattr(self, "store_attn_map"):
        attn.processor.store_attn_map = True

    return attn.processor(attn, hidden_states, height, width, *args, **kwargs)


def joint_attn_call2_0(
    self,
    attn: Attention,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor = None,
    attention_mask: Optional[torch.FloatTensor] = None,
    ############################################################
    height: int = None,
    timestep: Optional[torch.Tensor] = None,
    ############################################################
    *args,
    **kwargs,
) -> torch.FloatTensor:
    residual = hidden_states

    batch_size = hidden_states.shape[0]

    # `sample` projections.
    query = attn.to_q(hidden_states)
    key = attn.to_k(hidden_states)
    value = attn.to_v(hidden_states)

    inner_dim = key.shape[-1]
    head_dim = inner_dim // attn.heads

    query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
    key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
    value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

    if attn.norm_q is not None:
        query = attn.norm_q(query)
    if attn.norm_k is not None:
        key = attn.norm_k(key)

    # `context` projections.
    if encoder_hidden_states is not None:
        encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

        encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)
        encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)
        encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)

        if attn.norm_added_q is not None:
            encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
        if attn.norm_added_k is not None:
            encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

        query = torch.cat([query, encoder_hidden_states_query_proj], dim=2)
        key = torch.cat([key, encoder_hidden_states_key_proj], dim=2)
        value = torch.cat([value, encoder_hidden_states_value_proj], dim=2)

    ####################################################################################################
    if hasattr(self, "store_attn_map"):
        hidden_states, attention_probs = scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        image_length = query.shape[2] - encoder_hidden_states_query_proj.shape[2]

        # (4,24,4429,4429) -> (4,24,4096,333)
        attention_probs = attention_probs[:,:,:image_length,image_length:].cpu()
        
        self.attn_map = rearrange(
            attention_probs,
            'batch attn_head (height width) attn_dim -> batch attn_head height width attn_dim',
            height = height
        ) # (4, 24, 4096, 333) -> (4, 24, height, width, 333)
        self.timestep = timestep[0].cpu().item() # TODO: int -> list
    else:
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
    ####################################################################################################

    # hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
    hidden_states = hidden_states.to(query.dtype)

    if encoder_hidden_states is not None:
        # Split the attention outputs.
        hidden_states, encoder_hidden_states = (
            hidden_states[:, : residual.shape[1]],
            hidden_states[:, residual.shape[1] :],
        )
        if not attn.context_pre_only:
            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

    # linear proj
    hidden_states = attn.to_out[0](hidden_states)
    # dropout
    hidden_states = attn.to_out[1](hidden_states)

    if encoder_hidden_states is not None:
        return hidden_states, encoder_hidden_states
    else:
        return hidden_states


# FluxAttnProcessor2_0
def flux_attn_call2_0(
    self,
    attn: Attention,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor = None,
    attention_mask: Optional[torch.FloatTensor] = None,
    image_rotary_emb: Optional[torch.Tensor] = None,
    ############################################################
    height: int = None,
    timestep: Optional[torch.Tensor] = None,
    ############################################################
) -> torch.FloatTensor:
    batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

    # `sample` projections.
    query = attn.to_q(hidden_states)
    key = attn.to_k(hidden_states)
    value = attn.to_v(hidden_states)

    inner_dim = key.shape[-1]
    head_dim = inner_dim // attn.heads

    query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
    key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
    value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

    if attn.norm_q is not None:
        query = attn.norm_q(query)
    if attn.norm_k is not None:
        key = attn.norm_k(key)

    # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
    if encoder_hidden_states is not None:
        # `context` projections.
        encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

        encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)
        encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)
        encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
            batch_size, -1, attn.heads, head_dim
        ).transpose(1, 2)

        if attn.norm_added_q is not None:
            encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
        if attn.norm_added_k is not None:
            encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

        # attention
        query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
        key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
        value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)

    if image_rotary_emb is not None:
        from diffusers.models.embeddings import apply_rotary_emb
        

        query = apply_rotary_emb(query, image_rotary_emb)
        key = apply_rotary_emb(key, image_rotary_emb)

    ####################################################################################################
    if hasattr(self, "store_attn_map"):
        hidden_states, attention_probs = scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        image_length = query.shape[2] - encoder_hidden_states_query_proj.shape[2]

        # (4,24,4429,4429) -> (4,24,4096,333)
        attention_probs = attention_probs[:,:,:image_length,image_length:].cpu()
        
        self.attn_map = rearrange(
            attention_probs,
            'batch attn_head (height width) attn_dim -> batch attn_head height width attn_dim',
            height = height
        ) # (4, 24, 4096, 333) -> (4, 24, height, width, 333)
        self.timestep = timestep[0].cpu().item() # TODO: int -> list
    else:
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
    ####################################################################################################
    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
    hidden_states = hidden_states.to(query.dtype)

    if encoder_hidden_states is not None:
        encoder_hidden_states, hidden_states = (
            hidden_states[:, : encoder_hidden_states.shape[1]],
            hidden_states[:, encoder_hidden_states.shape[1] :],
        )

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

        return hidden_states, encoder_hidden_states
    else:
        return hidden_states