pipeline_flux.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import inspect
import json
import logging
import os
from collections.abc import Iterable
from typing import Any

import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
    FlowMatchEulerDiscreteScheduler,
)
from diffusers.utils.torch_utils import randn_tensor
from torch import nn
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from vllm.model_executor.models.utils import AutoWeightsLoader

from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
from vllm_omni.diffusion.distributed.parallel_state import get_classifier_free_guidance_world_size
from vllm_omni.diffusion.distributed.utils import get_local_device
from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
from vllm_omni.diffusion.models.flux import FluxTransformer2DModel
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.model_executor.model_loader.weight_utils import download_weights_from_hf_specific

logger = logging.getLogger(__name__)


def get_flux_post_process_func(
    od_config: OmniDiffusionConfig,
):
    if od_config.output_type == "latent":
        return lambda x: x
    model_name = od_config.model
    if os.path.exists(model_name):
        model_path = model_name
    else:
        model_path = download_weights_from_hf_specific(model_name, None, ["*"])

    vae_config_path = os.path.join(model_path, "vae/config.json")
    with open(vae_config_path) as f:
        vae_config = json.load(f)
        vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8

    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)

    def post_process_func(images: torch.Tensor):
        return image_processor.postprocess(images)

    return post_process_func


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: int | None = None,
    device: str | torch.device | None = None,
    timesteps: list[int] | None = None,
    sigmas: list[float] | None = None,
    **kwargs,
) -> tuple[torch.Tensor, int]:
    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 FluxPipeline(
    nn.Module,
):
    def __init__(
        self,
        *,
        od_config: OmniDiffusionConfig,
        prefix: str = "",
    ):
        super().__init__()
        self.od_config = od_config
        self.weights_sources = [
            DiffusersPipelineLoader.ComponentSource(
                model_or_path=od_config.model,
                subfolder="transformer",
                revision=None,
                prefix="transformer.",
                fall_back_to_pt=True,
            )
        ]

        self.device = get_local_device()
        model = od_config.model
        # Check if model is a local path
        local_files_only = os.path.exists(model)

        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
            model, subfolder="scheduler", local_files_only=local_files_only
        )
        self.text_encoder = CLIPTextModel.from_pretrained(
            model, subfolder="text_encoder", local_files_only=local_files_only
        )
        self.text_encoder_2 = T5EncoderModel.from_pretrained(
            model, subfolder="text_encoder_2", local_files_only=local_files_only
        )
        self.vae = AutoencoderKL.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
            self.device
        )
        self.transformer = FluxTransformer2DModel(od_config=od_config)

        self.tokenizer = CLIPTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
        self.tokenizer_2 = T5TokenizerFast.from_pretrained(
            model, subfolder="tokenizer_2", local_files_only=local_files_only
        )

        self.stage = None

        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        # Flux latents are turned into 2x2 patches and packed.
        # This means the latent width and height has to be divisible
        # by the patch size. So the vae scale factor is multiplied by the patch size to account for this
        # self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
        self.tokenizer_max_length = (
            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
        )
        self.default_sample_size = 128

    def check_inputs(
        self,
        prompt,
        prompt_2,
        height,
        width,
        negative_prompt=None,
        negative_prompt_2=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        pooled_prompt_embeds=None,
        negative_pooled_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        max_sequence_length=None,
    ):
        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
            logger.warning(
                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} "
                f"but are {height} and {width}. Dimensions will be resized accordingly"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

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

        if max_sequence_length is not None and max_sequence_length > 512:
            raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")

    def _get_t5_prompt_embeds(
        self,
        prompt: str | list[str] = None,
        num_images_per_prompt: int = 1,
        max_sequence_length: int = 512,
        dtype: torch.dtype | None = None,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

        if isinstance(self, TextualInversionLoaderMixin):
            prompt = self.maybe_convert_prompt(prompt, self.tokenizer_2)

        text_inputs = self.tokenizer_2(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            return_length=False,
            return_overflowing_tokens=False,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because `max_sequence_length` is set to "
                f" {max_sequence_length} tokens: {removed_text}"
            )

        prompt_embeds = self.text_encoder_2(text_input_ids.to(self.device), output_hidden_states=False)[0]

        dtype = self.text_encoder_2.dtype
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device)

        _, 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)

        return prompt_embeds

    def _get_clip_prompt_embeds(
        self,
        prompt: str | list[str],
        num_images_per_prompt: int = 1,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

        if isinstance(self, TextualInversionLoaderMixin):
            prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer_max_length,
            truncation=True,
            return_overflowing_tokens=False,
            return_length=False,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {self.tokenizer_max_length} tokens: {removed_text}"
            )
        prompt_embeds = self.text_encoder(text_input_ids.to(self.device), output_hidden_states=False)

        # Use pooled output of CLIPTextModel
        prompt_embeds = prompt_embeds.pooler_output
        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=self.device)

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)

        return prompt_embeds

    def encode_prompt(
        self,
        prompt: str | list[str],
        prompt_2: str | list[str],
        num_images_per_prompt: int = 1,
        prompt_embeds: torch.FloatTensor | None = None,
        pooled_prompt_embeds: torch.FloatTensor | None = None,
        max_sequence_length: int = 512,
    ):
        r"""

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in all text-encoders
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            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.
        """

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

        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

            # We only use the pooled prompt output from the CLIPTextModel
            pooled_prompt_embeds = self._get_clip_prompt_embeds(
                prompt=prompt,
                num_images_per_prompt=num_images_per_prompt,
            )
            prompt_embeds = self._get_t5_prompt_embeds(
                prompt=prompt_2,
                num_images_per_prompt=num_images_per_prompt,
                max_sequence_length=max_sequence_length,
            )

        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=self.device, dtype=dtype)

        return prompt_embeds, pooled_prompt_embeds, text_ids

    @staticmethod
    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
        latent_image_ids = torch.zeros(height, width, 3)
        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]

        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape

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

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

    @staticmethod
    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
        latents = latents.permute(0, 2, 4, 1, 3, 5)
        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)

        return latents

    @staticmethod
    def _unpack_latents(latents, height, width, vae_scale_factor):
        batch_size, num_patches, channels = latents.shape

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

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

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

        return latents

    def prepare_latents(
        self,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        # VAE applies 8x compression on images but we must also account for packing which requires
        # latent height and width to be divisible by 2.
        height = 2 * (int(height) // (self.vae_scale_factor * 2))
        width = 2 * (int(width) // (self.vae_scale_factor * 2))

        shape = (batch_size, num_channels_latents, height, width)

        if latents is not None:
            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
            return latents.to(device=device, dtype=dtype), latent_image_ids

        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)

        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)

        return latents, latent_image_ids

    def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.get("base_image_seq_len", 256),
            self.scheduler.config.get("max_image_seq_len", 4096),
            self.scheduler.config.get("base_shift", 0.5),
            self.scheduler.config.get("max_shift", 1.15),
        )
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            sigmas=sigmas,
            mu=mu,
        )
        return timesteps, num_inference_steps

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

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

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

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

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

    def diffuse(
        self,
        prompt_embeds,
        pooled_prompt_embeds,
        negative_prompt_embeds,
        negative_pooled_prompt_embeds,
        latents,
        latent_image_ids,
        text_ids,
        negative_text_ids,
        timesteps,
        do_true_cfg,
        guidance,
        true_cfg_scale,
    ):
        """Diffusion loop with optional image conditioning."""
        self.scheduler.set_begin_index(0)
        for i, t in enumerate(timesteps):
            if self.interrupt:
                continue

            self._current_timestep = t
            # broadcast to batch dimension and place on same device/dtype as latents
            timestep = t.expand(latents.shape[0]).to(device=latents.device, dtype=latents.dtype)

            self.transformer.do_true_cfg = do_true_cfg  # used in teacache hook
            # Forward pass for positive prompt (or unconditional if no CFG)
            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,
            )[0]

            # Forward pass for negative prompt (CFG)
            if do_true_cfg:
                neg_noise_pred = self.transformer(
                    hidden_states=latents,
                    timestep=timestep / 1000,
                    guidance=guidance,
                    pooled_projections=negative_pooled_prompt_embeds,
                    encoder_hidden_states=negative_prompt_embeds,
                    txt_ids=negative_text_ids,
                    img_ids=latent_image_ids,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    return_dict=False,
                )[0]
                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
        return latents

    def check_cfg_parallel_validity(self, true_cfg_scale: float, has_neg_prompt: bool):
        if get_classifier_free_guidance_world_size() == 1:
            return True

        if true_cfg_scale <= 1:
            logger.warning("CFG parallel is NOT working correctly when true_cfg_scale <= 1.")
            return False

        if not has_neg_prompt:
            logger.warning(
                "CFG parallel is NOT working correctly when there is no negative prompt or negative prompt embeddings."
            )
            return False
        return True

    def forward(
        self,
        req: OmniDiffusionRequest,
        prompt: str | list[str] | None = None,
        prompt_2: str | list[str] | None = None,
        negative_prompt: str | list[str] | None = None,
        negative_prompt_2: str | list[str] | None = None,
        true_cfg_scale: float = 1.0,
        height: int | None = None,
        width: int | None = None,
        num_inference_steps: int = 28,
        sigmas: list[float] | None = None,
        guidance_scale: float = 3.5,
        num_images_per_prompt: int = 1,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: torch.FloatTensor | None = None,
        prompt_embeds: torch.FloatTensor | None = None,
        pooled_prompt_embeds: torch.FloatTensor | None = None,
        negative_prompt_embeds: torch.FloatTensor | None = None,
        negative_pooled_prompt_embeds: torch.FloatTensor | None = None,
        output_type: str | None = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: dict[str, Any] | None = None,
        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
        max_sequence_length: int = 512,
    ):
        """Forward pass for flux."""
        # TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "")
        # TODO: May be some data formatting operations on the API side. Hack for now.
        prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
        if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
            negative_prompt = None
        elif req.prompts:
            negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]

        height = req.sampling_params.height or self.default_sample_size * self.vae_scale_factor
        width = req.sampling_params.width or self.default_sample_size * self.vae_scale_factor
        num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
        sigmas = req.sampling_params.sigmas or sigmas
        guidance_scale = (
            req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
        )
        generator = req.sampling_params.generator or generator
        num_images_per_prompt = (
            req.sampling_params.num_outputs_per_prompt
            if req.sampling_params.num_outputs_per_prompt > 0
            else num_images_per_prompt
        )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_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._current_timestep = None
        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]

        has_neg_prompt = negative_prompt is not None or (
            negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
        )
        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt

        self.check_cfg_parallel_validity(true_cfg_scale, has_neg_prompt)

        (
            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,
            num_images_per_prompt=num_images_per_prompt,
            max_sequence_length=max_sequence_length,
        )

        negative_text_ids = None
        if do_true_cfg:
            (
                negative_prompt_embeds,
                negative_pooled_prompt_embeds,
                negative_text_ids,
            ) = self.encode_prompt(
                prompt=negative_prompt,
                prompt_2=negative_prompt_2,
                prompt_embeds=negative_prompt_embeds,
                pooled_prompt_embeds=negative_pooled_prompt_embeds,
                num_images_per_prompt=num_images_per_prompt,
                max_sequence_length=max_sequence_length,
            )

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

        # 5. Prepare timesteps
        timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1])
        self._num_timesteps = len(timesteps)

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

        if self.joint_attention_kwargs is None:
            self._joint_attention_kwargs = {}

        latents = self.diffuse(
            prompt_embeds,
            pooled_prompt_embeds,
            negative_prompt_embeds,
            negative_pooled_prompt_embeds,
            latents,
            latent_image_ids,
            text_ids,
            negative_text_ids,
            timesteps,
            do_true_cfg,
            guidance,
            true_cfg_scale,
        )

        self._current_timestep = None

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

        return DiffusionOutput(output=image)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)