Add pipeline code for trust_remote_code

pipeline_fofpred.py

@@ -0,0 +1,894 @@
+"""
+FOFPred Diffusion Pipeline.
+
+Modified from OmniGen2 Diffusion Pipeline (By OmniGen2 Team and The HuggingFace Team).
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import inspect
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    BaseOutput,
+    is_torch_xla_available,
+    logging,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import Qwen2_5_VLForConditionalGeneration
+
+from fofpred.pipelines.image_processor import OmniGen2ImageProcessor
+from fofpred.utils.teacache_util import TeaCacheParams
+
+from ...models.transformers import OmniGen2Transformer3DModel
+from ...models.transformers.repo import OmniGen2RotaryPosEmbed
+from ..lora_pipeline import OmniGen2LoraLoaderMixin
+
+if is_torch_xla_available():
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+from ...cache_functions import cache_init
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class FMPipelineOutput(BaseOutput):
+    """
+    Output class for OmniGen2 pipeline.
+
+    Args:
+        images (Union[List[PIL.Image.Image], np.ndarray]):
+            List of denoised PIL images of length `batch_size` or numpy array of shape
+            `(batch_size, height, width, num_channels)`. Contains the generated images.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+# 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,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None:
+        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)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class FOFPredPipeline(DiffusionPipeline, OmniGen2LoraLoaderMixin):
+    """
+    Pipeline for text-to-image generation using OmniGen2.
+
+    This pipeline implements a text-to-image generation model that uses:
+    - Qwen2.5-VL for text encoding
+    - A custom transformer architecture for image generation
+    - VAE for image encoding/decoding
+    - FlowMatchEulerDiscreteScheduler for noise scheduling
+
+    Args:
+        transformer (OmniGen2Transformer3DModel): The transformer model for image generation.
+        vae (AutoencoderKL): The VAE model for image encoding/decoding.
+        scheduler (FlowMatchEulerDiscreteScheduler): The scheduler for noise scheduling.
+        text_encoder (Qwen2_5_VLModel): The text encoder model.
+        tokenizer (Union[Qwen2Tokenizer, Qwen2TokenizerFast]): The tokenizer for text processing.
+    """
+
+    model_cpu_offload_seq = "mllm->transformer->vae"
+
+    def __init__(
+        self,
+        transformer: OmniGen2Transformer3DModel,
+        vae: AutoencoderKL,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        mllm: Qwen2_5_VLForConditionalGeneration,
+        processor,
+    ) -> None:
+        """
+        Initialize the OmniGen2 pipeline.
+
+        Args:
+            transformer: The transformer model for image generation.
+            vae: The VAE model for image encoding/decoding.
+            scheduler: The scheduler for noise scheduling.
+            text_encoder: The text encoder model.
+            tokenizer: The tokenizer for text processing.
+        """
+        super().__init__()
+
+        self.register_modules(
+            transformer=transformer,
+            vae=vae,
+            scheduler=scheduler,
+            mllm=mllm,
+            processor=processor,
+        )
+        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 = OmniGen2ImageProcessor(
+            vae_scale_factor=self.vae_scale_factor * 2, do_resize=True
+        )
+        self.default_sample_size = 128
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        generator: Optional[torch.Generator],
+        latents: Optional[torch.FloatTensor] = None,
+        frame_count: int = 1,
+    ) -> torch.FloatTensor:
+        """
+        Prepare the initial latents for the diffusion process.
+
+        Args:
+            batch_size: The number of images to generate.
+            num_channels_latents: The number of channels in the latent space.
+            height: The height of the generated image.
+            width: The width of the generated image.
+            dtype: The data type of the latents.
+            device: The device to place the latents on.
+            generator: The random number generator to use.
+            latents: Optional pre-computed latents to use instead of random initialization.
+            frame_count: The number of frames to output.
+
+        Returns:
+            torch.FloatTensor: The prepared latents tensor.
+        """
+        height = int(height) // self.vae_scale_factor
+        width = int(width) // self.vae_scale_factor
+
+        if frame_count > 1:
+            shape = (batch_size, frame_count, num_channels_latents, height, width)
+        else:
+            shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is None:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+        else:
+            latents = latents.to(device)
+        return latents
+
+    def encode_vae(self, img: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        Encode an image into the VAE latent space.
+
+        Args:
+            img: The input image tensor to encode.
+
+        Returns:
+            torch.FloatTensor: The encoded latent representation.
+        """
+        z0 = self.vae.encode(img.to(dtype=self.vae.dtype)).latent_dist.sample()
+        if self.vae.config.shift_factor is not None:
+            z0 = z0 - self.vae.config.shift_factor
+        if self.vae.config.scaling_factor is not None:
+            z0 = z0 * self.vae.config.scaling_factor
+        z0 = z0.to(dtype=self.vae.dtype)
+        return z0
+
+    def prepare_image(
+        self,
+        images: Union[List[PIL.Image.Image], PIL.Image.Image],
+        batch_size: int,
+        num_images_per_prompt: int,
+        max_pixels: int,
+        max_side_length: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> List[Optional[torch.FloatTensor]]:
+        """
+        Prepare input images for processing by encoding them into the VAE latent space.
+
+        Args:
+            images: Single image or list of images to process.
+            batch_size: The number of images to generate per prompt.
+            num_images_per_prompt: The number of images to generate for each prompt.
+            device: The device to place the encoded latents on.
+            dtype: The data type of the encoded latents.
+
+        Returns:
+            List[Optional[torch.FloatTensor]]: List of encoded latent representations for each image.
+        """
+        if batch_size == 1:
+            images = [images]
+        latents = []
+        for i, img in enumerate(images):
+            if img is not None and len(img) > 0:
+                ref_latents = []
+                for j, img_j in enumerate(img):
+                    img_j = self.image_processor.preprocess(
+                        img_j, max_pixels=max_pixels, max_side_length=max_side_length
+                    )
+                    ref_latents.append(
+                        self.encode_vae(img_j.to(device=device)).squeeze(0)
+                    )
+            else:
+                ref_latents = None
+            for _ in range(num_images_per_prompt):
+                latents.append(ref_latents)
+
+        return latents
+
+    def _get_qwen2_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 256,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Get prompt embeddings from the Qwen2 text encoder.
+
+        Args:
+            prompt: The prompt or list of prompts to encode.
+            device: The device to place the embeddings on. If None, uses the pipeline's device.
+            max_sequence_length: Maximum sequence length for tokenization.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
+                - The prompt embeddings tensor
+                - The attention mask tensor
+
+        Raises:
+            Warning: If the input text is truncated due to sequence length limitations.
+        """
+        device = device or self._execution_device
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        # text_inputs = self.processor.tokenizer(
+        #     prompt,
+        #     padding="max_length",
+        #     max_length=max_sequence_length,
+        #     truncation=True,
+        #     return_tensors="pt",
+        # )
+        text_inputs = self.processor.tokenizer(
+            prompt,
+            padding="longest",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        untruncated_ids = self.processor.tokenizer(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids.to(device)
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+            text_input_ids, untruncated_ids
+        ):
+            removed_text = self.processor.tokenizer.batch_decode(
+                untruncated_ids[:, max_sequence_length - 1 : -1]
+            )
+            logger.warning(
+                "The following part of your input was truncated because Gemma can only handle sequences up to"
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_attention_mask = text_inputs.attention_mask.to(device)
+        prompt_embeds = self.mllm(
+            text_input_ids,
+            attention_mask=prompt_attention_mask,
+            output_hidden_states=True,
+        ).hidden_states[-1]
+
+        if self.mllm is not None:
+            dtype = self.mllm.dtype
+        elif self.transformer is not None:
+            dtype = self.transformer.dtype
+        else:
+            dtype = None
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        return prompt_embeds, prompt_attention_mask
+
+    def _apply_chat_template(self, prompt: str):
+        prompt = [
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that generates high-quality images based on user instructions.",
+            },
+            {"role": "user", "content": prompt},
+        ]
+        prompt = self.processor.tokenizer.apply_chat_template(
+            prompt, tokenize=False, add_generation_prompt=False
+        )
+        return prompt
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 256,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt 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`). For
+                Lumina-T2I, this should be "".
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                whether to use classifier free guidance or not
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                number of images that should be generated per prompt
+            device: (`torch.device`, *optional*):
+                torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" string.
+            max_sequence_length (`int`, defaults to `256`):
+                Maximum sequence length to use for the prompt.
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [self._apply_chat_template(_prompt) for _prompt in prompt]
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+        if prompt_embeds is None:
+            prompt_embeds, prompt_attention_mask = self._get_qwen2_prompt_embeds(
+                prompt=prompt, device=device, max_sequence_length=max_sequence_length
+            )
+
+        batch_size, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            batch_size * num_images_per_prompt, seq_len, -1
+        )
+        prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+        prompt_attention_mask = prompt_attention_mask.view(
+            batch_size * num_images_per_prompt, -1
+        )
+
+        # Get negative embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt if negative_prompt is not None else ""
+
+            # Normalize str to list
+            negative_prompt = (
+                batch_size * [negative_prompt]
+                if isinstance(negative_prompt, str)
+                else negative_prompt
+            )
+            negative_prompt = [
+                self._apply_chat_template(_negative_prompt)
+                for _negative_prompt in negative_prompt
+            ]
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                negative_prompt = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            negative_prompt_embeds, negative_prompt_attention_mask = (
+                self._get_qwen2_prompt_embeds(
+                    prompt=negative_prompt,
+                    device=device,
+                    max_sequence_length=max_sequence_length,
+                )
+            )
+
+            batch_size, seq_len, _ = negative_prompt_embeds.shape
+            # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+            negative_prompt_embeds = negative_prompt_embeds.repeat(
+                1, num_images_per_prompt, 1
+            )
+            negative_prompt_embeds = negative_prompt_embeds.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(
+                num_images_per_prompt, 1
+            )
+            negative_prompt_attention_mask = negative_prompt_attention_mask.view(
+                batch_size * num_images_per_prompt, -1
+            )
+
+        return (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        )
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def text_guidance_scale(self):
+        return self._text_guidance_scale
+
+    @property
+    def image_guidance_scale(self):
+        return self._image_guidance_scale
+
+    @property
+    def cfg_range(self):
+        return self._cfg_range
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        prompt_attention_mask: Optional[torch.LongTensor] = None,
+        negative_prompt_attention_mask: Optional[torch.LongTensor] = None,
+        max_sequence_length: Optional[int] = None,
+        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
+        input_images: Optional[List[PIL.Image.Image]] = None,
+        num_images_per_prompt: int = 1,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        max_pixels: int = 1024 * 1024,
+        max_input_image_side_length: int = 1024,
+        align_res: bool = True,
+        num_inference_steps: int = 28,
+        text_guidance_scale: float = 4.0,
+        image_guidance_scale: float = 1.0,
+        cfg_range: Tuple[float, float] = (0.0, 1.0),
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        timesteps: List[int] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        frame_count: int = 1,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        verbose: bool = False,
+        step_func=None,
+        get_latents_text_embeds=False,
+    ):
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        self._text_guidance_scale = text_guidance_scale
+        self._image_guidance_scale = image_guidance_scale
+        self._cfg_range = cfg_range
+        self._attention_kwargs = attention_kwargs
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt,
+            self.text_guidance_scale > 1.0,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            max_sequence_length=max_sequence_length,
+        )
+
+        dtype = self.vae.dtype
+        # 3. Prepare control image
+        ref_latents = self.prepare_image(
+            images=input_images,
+            batch_size=batch_size,
+            num_images_per_prompt=num_images_per_prompt,
+            max_pixels=max_pixels,
+            max_side_length=max_input_image_side_length,
+            device=device,
+            dtype=dtype,
+        )
+
+        if input_images is None:
+            input_images = []
+
+        if len(input_images) == 1 and align_res:
+            width, height = (
+                ref_latents[0][0].shape[-1] * self.vae_scale_factor,
+                ref_latents[0][0].shape[-2] * self.vae_scale_factor,
+            )
+            ori_width, ori_height = width, height
+        else:
+            ori_width, ori_height = width, height
+
+            cur_pixels = height * width
+            ratio = (max_pixels / cur_pixels) ** 0.5
+            ratio = min(ratio, 1.0)
+
+            height, width = (
+                int(height * ratio) // 16 * 16,
+                int(width * ratio) // 16 * 16,
+            )
+
+        if len(input_images) == 0:
+            self._image_guidance_scale = 1
+
+        # 4. Prepare latents.
+        latent_channels = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            latent_channels,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+            frame_count,
+        )
+
+        freqs_cis = OmniGen2RotaryPosEmbed.get_freqs_cis(
+            self.transformer.config.axes_dim_rope,
+            self.transformer.config.axes_lens,
+            theta=10000,
+        )
+
+        image = self.processing(
+            latents=latents,
+            ref_latents=ref_latents,
+            prompt_embeds=prompt_embeds,
+            freqs_cis=freqs_cis,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            num_inference_steps=num_inference_steps,
+            timesteps=timesteps,
+            device=device,
+            dtype=dtype,
+            verbose=verbose,
+            step_func=step_func,
+            get_latents_text_embeds=get_latents_text_embeds,
+        )
+
+        if get_latents_text_embeds:
+            return image, prompt_embeds
+
+        if len(image.shape) == 4:
+            image = F.interpolate(image, size=(ori_height, ori_width), mode="bilinear")
+            image = self.image_processor.postprocess(image, output_type=output_type)
+        else:
+            image = [
+                F.interpolate(
+                    image[:, i], size=(ori_height, ori_width), mode="bilinear"
+                )
+                for i in range(image.shape[1])
+            ]
+            image = [
+                self.image_processor.postprocess(x, output_type=output_type)
+                for x in image
+            ]
+            image = torch.stack(image, dim=1)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return image
+        else:
+            return FMPipelineOutput(images=image)
+
+    def processing(
+        self,
+        latents,
+        ref_latents,
+        prompt_embeds,
+        freqs_cis,
+        negative_prompt_embeds,
+        prompt_attention_mask,
+        negative_prompt_attention_mask,
+        num_inference_steps,
+        timesteps,
+        device,
+        dtype,
+        verbose,
+        step_func=None,
+        get_latents_text_embeds=False,
+    ):
+        batch_size = latents.shape[0]
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            num_tokens=latents.shape[-2] * latents.shape[-1],
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        enable_taylorseer = getattr(self, "enable_taylorseer", False)
+        if enable_taylorseer:
+            model_pred_cache_dic, model_pred_current = cache_init(
+                self, num_inference_steps
+            )
+            model_pred_ref_cache_dic, model_pred_ref_current = cache_init(
+                self, num_inference_steps
+            )
+            model_pred_uncond_cache_dic, model_pred_uncond_current = cache_init(
+                self, num_inference_steps
+            )
+            self.transformer.enable_taylorseer = True
+        elif self.transformer.enable_teacache:
+            # Use different TeaCacheParams for different conditions
+            teacache_params = TeaCacheParams()
+            teacache_params_uncond = TeaCacheParams()
+            teacache_params_ref = TeaCacheParams()
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if enable_taylorseer:
+                    self.transformer.cache_dic = model_pred_cache_dic
+                    self.transformer.current = model_pred_current
+                elif self.transformer.enable_teacache:
+                    teacache_params.is_first_or_last_step = (
+                        i == 0 or i == len(timesteps) - 1
+                    )
+                    self.transformer.teacache_params = teacache_params
+
+                model_pred = self.predict(
+                    t=t,
+                    latents=latents,
+                    prompt_embeds=prompt_embeds,
+                    freqs_cis=freqs_cis,
+                    prompt_attention_mask=prompt_attention_mask,
+                    ref_image_hidden_states=ref_latents,
+                )
+                text_guidance_scale = (
+                    self.text_guidance_scale
+                    if self.cfg_range[0] <= i / len(timesteps) <= self.cfg_range[1]
+                    else 1.0
+                )
+                image_guidance_scale = (
+                    self.image_guidance_scale
+                    if self.cfg_range[0] <= i / len(timesteps) <= self.cfg_range[1]
+                    else 1.0
+                )
+
+                if text_guidance_scale > 1.0 and image_guidance_scale > 1.0:
+                    if enable_taylorseer:
+                        self.transformer.cache_dic = model_pred_ref_cache_dic
+                        self.transformer.current = model_pred_ref_current
+                    elif self.transformer.enable_teacache:
+                        teacache_params_ref.is_first_or_last_step = (
+                            i == 0 or i == len(timesteps) - 1
+                        )
+                        self.transformer.teacache_params = teacache_params_ref
+
+                    model_pred_ref = self.predict(
+                        t=t,
+                        latents=latents,
+                        prompt_embeds=negative_prompt_embeds,
+                        freqs_cis=freqs_cis,
+                        prompt_attention_mask=negative_prompt_attention_mask,
+                        ref_image_hidden_states=ref_latents,
+                    )
+
+                    if enable_taylorseer:
+                        self.transformer.cache_dic = model_pred_uncond_cache_dic
+                        self.transformer.current = model_pred_uncond_current
+                    elif self.transformer.enable_teacache:
+                        teacache_params_uncond.is_first_or_last_step = (
+                            i == 0 or i == len(timesteps) - 1
+                        )
+                        self.transformer.teacache_params = teacache_params_uncond
+
+                    model_pred_uncond = self.predict(
+                        t=t,
+                        latents=latents,
+                        prompt_embeds=negative_prompt_embeds,
+                        freqs_cis=freqs_cis,
+                        prompt_attention_mask=negative_prompt_attention_mask,
+                        ref_image_hidden_states=None,
+                    )
+
+                    model_pred = (
+                        model_pred_uncond
+                        + image_guidance_scale * (model_pred_ref - model_pred_uncond)
+                        + text_guidance_scale * (model_pred - model_pred_ref)
+                    )
+                elif text_guidance_scale > 1.0:
+                    if enable_taylorseer:
+                        self.transformer.cache_dic = model_pred_uncond_cache_dic
+                        self.transformer.current = model_pred_uncond_current
+                    elif self.transformer.enable_teacache:
+                        teacache_params_uncond.is_first_or_last_step = (
+                            i == 0 or i == len(timesteps) - 1
+                        )
+                        self.transformer.teacache_params = teacache_params_uncond
+
+                    model_pred_uncond = self.predict(
+                        t=t,
+                        latents=latents,
+                        prompt_embeds=negative_prompt_embeds,
+                        freqs_cis=freqs_cis,
+                        prompt_attention_mask=negative_prompt_attention_mask,
+                        ref_image_hidden_states=None,
+                    )
+                    model_pred = model_pred_uncond + text_guidance_scale * (
+                        model_pred - model_pred_uncond
+                    )
+
+                latents = self.scheduler.step(
+                    model_pred, t, latents, return_dict=False
+                )[0]
+
+                latents = latents.to(dtype=dtype)
+
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+                if step_func is not None:
+                    step_func(i, self._num_timesteps)
+
+        if enable_taylorseer:
+            del (
+                model_pred_cache_dic,
+                model_pred_ref_cache_dic,
+                model_pred_uncond_cache_dic,
+            )
+            del model_pred_current, model_pred_ref_current, model_pred_uncond_current
+
+        latents = latents.to(dtype=dtype)
+        if get_latents_text_embeds:
+            return latents
+
+        if self.vae.config.scaling_factor is not None:
+            latents = latents / self.vae.config.scaling_factor
+        if self.vae.config.shift_factor is not None:
+            latents = latents + self.vae.config.shift_factor
+        if len(latents.shape) == 4:
+            image = self.vae.decode(latents, return_dict=False)[0]
+        else:
+            image = [
+                self.vae.decode(latents[:, i], return_dict=False)[0]
+                for i in range(latents.shape[1])
+            ]
+            image = torch.stack(image, dim=1)
+
+        return image
+
+    def predict(
+        self,
+        t,
+        latents,
+        prompt_embeds,
+        freqs_cis,
+        prompt_attention_mask,
+        ref_image_hidden_states,
+    ):
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+        if len(latents.shape) == 4:
+            batch_size, num_channels_latents, height, width = latents.shape
+            is_temporal = False
+        else:
+            batch_size, num_frames, num_channels_latents, height, width = latents.shape
+            latents = [_latents for _latents in latents]
+            is_temporal = True
+
+        optional_kwargs = {}
+        if "ref_image_hidden_states" in set(
+            inspect.signature(self.transformer.forward).parameters.keys()
+        ):
+            optional_kwargs["ref_image_hidden_states"] = ref_image_hidden_states
+
+        model_pred = self.transformer(
+            latents,
+            timestep,
+            prompt_embeds,
+            freqs_cis,
+            prompt_attention_mask,
+            **optional_kwargs,
+        )
+
+        if is_temporal:
+            model_pred = torch.stack(model_pred)
+        return model_pred