code/diffusion/pipeline.py

# Copyright (c) Ant Group. 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.

# All rights and credit for the original implementation remain with the original authors and contributors, and this project complies with the applicable open-source license terms of the referenced repository.

import inspect
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import AutoTokenizer, PreTrainedModel

from diffusers.image_processor import VaeImageProcessor
from diffusers.models.autoencoders import AutoencoderKL
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import logging, replace_example_docstring
from diffusers.utils.torch_utils import randn_tensor

from .transformer import DiffusionTransformer

from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils import BaseOutput


@dataclass
class ImageGenerationOutput(BaseOutput):
    """
    Output class for image generation pipelines.

    Args:
        images (`List[PIL.Image.Image]` or `np.ndarray`)
            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
    """

    images: Union[List[PIL.Image.Image], np.ndarray]


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        This pipeline is assembled by the Ming image checkpoint loader; the
        supported entry point is the repository CLI:

        ```bash
        python infer.py \
          --model /path/to/checkpoint-package \
          --task text-to-image \
          --prompt "A small red cube on a white table" \
          --output-dir outputs/t2i
        ```
"""


# 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


class ImageGenerationPipeline(DiffusionPipeline):
    model_cpu_offload_seq = "text_encoder->transformer->vae"
    _optional_components = []
    _callback_tensor_inputs = ["latents", "prompt_embeds"]

    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: PreTrainedModel,
        tokenizer: AutoTokenizer,
        transformer: DiffusionTransformer,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            scheduler=scheduler,
            transformer=transformer,
        )

        if hasattr(self, "vae") and self.vae is not None and 'temperal_downsample' in self.vae.config:
            self.vae_scale_factor = 2 ** len(self.vae.config.temperal_downsample)
        else:
            self.vae_scale_factor = (
                2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
            )

        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)

    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        device: Optional[torch.device] = None,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        max_sequence_length: int = 512,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        prompt_embeds = self._encode_prompt(
            prompt=prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            max_sequence_length=max_sequence_length,
        )

        if do_classifier_free_guidance:
            if negative_prompt is None:
                negative_prompt = ["" for _ in prompt]
            else:
                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            assert len(prompt) == len(negative_prompt)
            negative_prompt_embeds = self._encode_prompt(
                prompt=negative_prompt,
                device=device,
                prompt_embeds=negative_prompt_embeds,
                max_sequence_length=max_sequence_length,
            )
        else:
            negative_prompt_embeds = []
        return prompt_embeds, negative_prompt_embeds

    def _encode_prompt(
        self,
        prompt: Union[str, List[str]],
        device: Optional[torch.device] = None,
        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
        max_sequence_length: int = 512,
    ) -> List[torch.FloatTensor]:
        device = device or self._execution_device

        if prompt_embeds is not None:
            return prompt_embeds

        if isinstance(prompt, str):
            prompt = [prompt]

        for i, prompt_item in enumerate(prompt):
            messages = [
                {"role": "user", "content": prompt_item},
            ]
            prompt_item = self.tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True,
                enable_thinking=True,
            )
            prompt[i] = prompt_item

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids.to(device)
        prompt_masks = text_inputs.attention_mask.to(device).bool()

        prompt_embeds = self.text_encoder(
            input_ids=text_input_ids,
            attention_mask=prompt_masks,
            output_hidden_states=True,
        ).hidden_states[-2]

        embeddings_list = []

        for i in range(len(prompt_embeds)):
            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])

        return embeddings_list

    def prepare_latents(
        self,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        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)

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)
        return latents

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

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

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

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

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

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 5.0,
        cfg_normalization: bool = False,
        cfg_truncation: float = 1.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        num_frames_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
        negative_prompt_embeds: Optional[List[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,
        device: Optional[Union[str, torch.device]] = None,
        ref_hidden_states: Optional[torch.FloatTensor] = None,
        prompt_embeds_2: Optional[List[torch.FloatTensor]] = None,
        negative_prompt_embeds_2: Optional[List[torch.FloatTensor]] = None,
        sample_mode: Optional[str] = "sample",
    ):
        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.
            height (`int`, *optional*, defaults to 1024):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 1024):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            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 5.0):
                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.
            cfg_normalization (`bool`, *optional*, defaults to False):
                Whether to apply configuration normalization.
            cfg_truncation (`float`, *optional*, defaults to 1.0):
                The truncation value for configuration.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            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 be generated by sampling using the supplied random `generator`.
            prompt_embeds (`List[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.
            negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.ImageGenerationOutput`] 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`, *optional*, defaults to 512):
                Maximum sequence length to use with the `prompt`.

        Examples:

        Returns:
            [`ImageGenerationOutput`] or `tuple`: [`ImageGenerationOutput`] 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 1024
        width = width or 1024
        if type(num_frames_per_prompt) is not int or num_frames_per_prompt < 1:
            raise ValueError("num_frames_per_prompt must be an integer >= 1")
        supports_multi_frame = bool(
            getattr(self.transformer.config, "multi_frame_output", False)
        )
        if num_frames_per_prompt > 1 and not supports_multi_frame:
            raise ValueError(
                "the loaded checkpoint profile does not support multi-frame output"
            )

        vae_scale = self.vae_scale_factor * 2
        if height % vae_scale != 0:
            raise ValueError(
                f"Height must be divisible by {vae_scale} (got {height}). "
                f"Please adjust the height to a multiple of {vae_scale}."
            )
        if width % vae_scale != 0:
            raise ValueError(
                f"Width must be divisible by {vae_scale} (got {width}). "
                f"Please adjust the width to a multiple of {vae_scale}."
            )

        device = device or self._execution_device

        self._guidance_scale = guidance_scale
        self._joint_attention_kwargs = joint_attention_kwargs
        self._interrupt = False
        self._cfg_normalization = cfg_normalization
        self._cfg_truncation = cfg_truncation
        # 2. Define call parameters

        self.vae.config.shift_factor = self.vae.config.shift_factor.to(device=device, dtype=self.vae.dtype) if isinstance(self.vae.config.shift_factor, torch.Tensor) else self.vae.config.shift_factor
        self.vae.config.scaling_factor = self.vae.config.scaling_factor.to(device=device, dtype=self.vae.dtype) if isinstance(self.vae.config.scaling_factor, torch.Tensor) else self.vae.config.scaling_factor

        assert prompt is None
        assert prompt_embeds is not None or prompt_embeds_2 is not None
        if prompt_embeds is not None:
            batch_size = len(prompt_embeds)
            if prompt_embeds_2 is not None:
                assert len(prompt_embeds_2) == len(prompt_embeds)

            assert negative_prompt_embeds is not None
            assert len(negative_prompt_embeds) == len(prompt_embeds)
        else:
            assert prompt_embeds_2 is not None
            batch_size = len(prompt_embeds_2)
            assert negative_prompt_embeds_2 is not None
            assert len(negative_prompt_embeds_2) == len(prompt_embeds_2)
            


        # 4. Prepare latent variables
        num_channels_latents = self.transformer.in_channels

        latents = self.prepare_latents(
            batch_size * num_images_per_prompt * num_frames_per_prompt,
            num_channels_latents,
            height,
            width,
            torch.float32,
            device,
            generator,
            latents,
        )

        if ref_hidden_states is not None:
            assert ref_hidden_states.ndim == 4  # and ref_hidden_states.shape[0] == 1
            ref_hidden_states = ref_hidden_states.to(self.vae.dtype).to(device)
            C = ref_hidden_states.shape[1]
            input_channels = self.vae.input_channels
            if C % input_channels != 0:
               raise ValueError(f"ref_hidden_states.shape[1] must be a multiple of {input_channels}, got C={C}")
            num_images = C // input_channels
            ref_latents = []
            for i in range(num_images):
                x = ref_hidden_states[:, i * input_channels:(i + 1) * input_channels, :, :]  # RGB block of image i
                if 'temperal_downsample' in self.vae.config:
                    x = x.unsqueeze(2)
                if sample_mode == "argmax":
                    z = self.vae.encode(x).latent_dist.mode()
                elif sample_mode == "sample":
                    z = self.vae.encode(x).latent_dist.sample()
                z = (z - self.vae.config.shift_factor) * self.vae.config.scaling_factor
                ref_latents.append(z)
            
            ref_hidden_states = torch.cat(ref_latents, dim=1)
            
            if ref_hidden_states.dim() == 5:
                ref_hidden_states = ref_hidden_states.squeeze(2)



        # Repeat prompt_embeds for num_images_per_prompt
        if num_images_per_prompt > 1:
            if prompt_embeds is not None:
                prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
                if self.do_classifier_free_guidance and negative_prompt_embeds:
                    negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]

            if prompt_embeds_2 is not None:
                prompt_embeds_2 = [pe for pe in prompt_embeds_2 for _ in range(num_images_per_prompt)]
                if self.do_classifier_free_guidance and negative_prompt_embeds_2:
                    negative_prompt_embeds_2 = [npe for npe in negative_prompt_embeds_2 for _ in range(num_images_per_prompt)]

        actual_batch_size = batch_size * num_images_per_prompt
        image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)

        # 5. Prepare timesteps
        if image_seq_len >= 4096:
            self.scheduler.config["max_image_seq_len"] = image_seq_len
            self.scheduler.config["max_shift"] = 1.35
        else:
            self.scheduler.config["max_image_seq_len"] = 4096
            self.scheduler.config["max_shift"] = 1.15
        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),
        )
        self.scheduler.sigma_min = 0.0
        scheduler_kwargs = {"mu": mu}
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            device,
            sigmas=sigmas,
            **scheduler_kwargs,
        )
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        self._num_timesteps = len(timesteps)

        if num_frames_per_prompt > 1:
            latents = torch.stack(
                latents.chunk(num_frames_per_prompt, dim=0), dim=2
            )

        # 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])
                timestep = (1000 - timestep) / 1000
                # Normalized time for time-aware config (0 at start, 1 at end)
                t_norm = timestep[0].item()

                # Handle cfg truncation
                current_guidance_scale = self.guidance_scale
                if (
                    self.do_classifier_free_guidance
                    and self._cfg_truncation is not None
                    and float(self._cfg_truncation) <= 1
                ):
                    if t_norm > self._cfg_truncation:
                        current_guidance_scale = 0.0

                # Run CFG only if configured AND scale is non-zero
                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0

                if apply_cfg:
                    latents_typed = latents.to(self.transformer.dtype)
                    repeat_dims = (2, 1, 1, 1, 1) if latents_typed.ndim == 5 else (2, 1, 1, 1)
                    latent_model_input = latents_typed.repeat(*repeat_dims)
                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds if prompt_embeds is not None else None
                    prompt_embeds_model_input_2 = prompt_embeds_2 + negative_prompt_embeds_2 if prompt_embeds_2 is not None else None
                    timestep_model_input = timestep.repeat(2)
                    ref_hidden_states_input = ref_hidden_states.repeat(2, 1, 1, 1) if ref_hidden_states is not None else None
                    if ref_hidden_states_input is not None:
                        ref_hidden_states_input = ref_hidden_states_input.to(latent_model_input.dtype)
                else:
                    latent_model_input = latents.to(self.transformer.dtype)
                    prompt_embeds_model_input = prompt_embeds
                    prompt_embeds_model_input_2 = prompt_embeds_2
                    timestep_model_input = timestep
                    ref_hidden_states_input = ref_hidden_states*1.0 if ref_hidden_states is not None else None
                    if ref_hidden_states_input is not None:
                        ref_hidden_states_input = ref_hidden_states_input.to(latent_model_input.dtype)

                if latent_model_input.ndim == 4:
                    latent_model_input = latent_model_input.unsqueeze(2)
                latent_model_input_list = list(latent_model_input.unbind(dim=0))
                
                if ref_hidden_states_input is not None:
                    C = self.vae.config.latent_channels if 'latent_channels' in self.vae.config else self.vae.config.z_dim

                    # single-image input: [B, C, H, W] -> list of B elems, each [C, 1, H, W]
                    if ref_hidden_states_input.shape[1] == C:
                        ref_hidden_states_input = ref_hidden_states_input.unsqueeze(2)              # [B, C, 1, H, W]
                        ref_hidden_states_input = list(ref_hidden_states_input.unbind(dim=0))       # B * [C, 1, H, W]

                    # multi-image input: [B, N*C, H, W] -> list of B elems, each [C, N, H, W]
                    else:
                        B, total_C, H, W = ref_hidden_states_input.shape
                        if total_C % C != 0:
                            raise ValueError(
                                f"ref_hidden_states_input channel ({total_C}) must be divisible by latent_channels ({C})."
                            )
                        N = total_C // C

                        # order-preserving: input assumes [img0(C), img1(C), ..., imgN-1(C)] stacked along channels
                        ref_hidden_states_input = (
                            ref_hidden_states_input.reshape(B, N, C, H, W)         # [B, N, C, H, W], N in stacking order
                                .permute(0, 2, 1, 3, 4)         # [B, C, N, H, W]
                                .contiguous()
                        )
                        ref_hidden_states_input = list(ref_hidden_states_input.unbind(dim=0))        # B * [C, N, H, W]

                model_out_list = self.transformer(
                    latent_model_input_list,
                    timestep_model_input,
                    prompt_embeds_model_input,
                    ref_hidden_states=ref_hidden_states_input,
                    return_dict=False,
                    encoder_hidden_states_2=prompt_embeds_model_input_2,
                )[0]
                model_out_list = [
                    output[:, :num_frames_per_prompt, :, :].float()
                    for output in model_out_list
                ]

                if apply_cfg:
                    # Perform CFG
                    pos_out = model_out_list[:actual_batch_size]
                    neg_out = model_out_list[actual_batch_size:]

                    noise_pred = []
                    for j in range(actual_batch_size):
                        pos = pos_out[j].float()
                        neg = neg_out[j].float()

                        pred = pos + current_guidance_scale * (pos - neg)

                        # Renormalization
                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
                            ori_pos_norm = torch.linalg.vector_norm(pos)
                            new_pos_norm = torch.linalg.vector_norm(pred)
                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
                            if new_pos_norm > max_new_norm:
                                pred = pred * (max_new_norm / new_pos_norm)

                        noise_pred.append(pred)

                    noise_pred = torch.stack(noise_pred, dim=0)
                else:
                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)

                if num_frames_per_prompt == 1:
                    noise_pred = noise_pred.squeeze(2)
                noise_pred = -noise_pred

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
                assert latents.dtype == torch.float32

                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)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_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 num_frames_per_prompt > 1:
            latents = torch.cat(
                latents.chunk(num_frames_per_prompt, dim=2), dim=0
            ).squeeze(2)

        if output_type == "latent":
            image = latents

        else:
            latents = latents.to(self.vae.dtype)
            if 'temperal_downsample' in self.vae.config:
                latents = latents.unsqueeze(2)

            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor

            image = self.vae.decode(latents, return_dict=False)[0]
            if image.dim() == 5:
                image = image.squeeze(2)
                
            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 ImageGenerationOutput(images=image)