pipeline_sd3.py

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

import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.autoencoders 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 CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel, T5Tokenizer
from vllm.model_executor.models.utils import AutoWeightsLoader

from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
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.sd3.sd3_transformer import (
    SD3Transformer2DModel,
)
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_sd3_image_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)

    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


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 StableDiffusion3Pipeline(nn.Module, CFGParallelMixin):
    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.tokenizer = CLIPTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
        self.tokenizer_2 = CLIPTokenizer.from_pretrained(
            model, subfolder="tokenizer_2", local_files_only=local_files_only
        )
        self.tokenizer_3 = T5Tokenizer.from_pretrained(
            model, subfolder="tokenizer_3", local_files_only=local_files_only
        )
        self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
            model, subfolder="text_encoder", local_files_only=local_files_only
        )
        self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
            model, subfolder="text_encoder_2", local_files_only=local_files_only
        )
        self.text_encoder_3 = T5EncoderModel.from_pretrained(
            model,
            subfolder="text_encoder_3",
            local_files_only=local_files_only,
        )
        self.transformer = SD3Transformer2DModel(od_config=od_config)

        self.vae = AutoencoderKL.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
            self.device
        )

        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

        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
        self.patch_size = 2
        self.output_type = self.od_config.output_type

    def check_inputs(
        self,
        prompt,
        prompt_2,
        prompt_3,
        height,
        width,
        negative_prompt=None,
        negative_prompt_2=None,
        negative_prompt_3=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        max_sequence_length=None,
    ):
        if (
            height % (self.vae_scale_factor * self.patch_size) != 0
            or width % (self.vae_scale_factor * self.patch_size) != 0
        ):
            raise ValueError(
                f"`height` and `width` have to be divisible by "
                f"{self.vae_scale_factor * self.patch_size} but are "
                f"{height} and {width}. You can use height "
                f"{height - height % (self.vae_scale_factor * self.patch_size)} "
                f"and width {width - width % (self.vae_scale_factor * self.patch_size)}."
            )

        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_3 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_3`: {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)}")
        elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):
            raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}")

        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."
            )
        elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} 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 negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

        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_clip_prompt_embeds(
        self,
        prompt: str | list[str] = "",
        num_images_per_prompt: int = 1,
        dtype: torch.dtype | None = None,
        clip_model_index: int = 0,
    ):
        dtype = dtype or self.text_encoder.dtype
        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
        clip_text_encoders = [self.text_encoder, self.text_encoder_2]

        tokenizer = clip_tokenizers[clip_model_index]
        text_encoder = clip_text_encoders[clip_model_index]

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

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

        text_input_ids = text_inputs.input_ids
        untruncated_ids = 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 = 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 = text_encoder(text_input_ids.to(self.device), output_hidden_states=True)
        pooled_prompt_embeds = prompt_embeds[0]

        prompt_embeds = prompt_embeds.hidden_states[-2]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=self.device)
        _, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt)
        pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)

        return prompt_embeds, pooled_prompt_embeds

    def _get_t5_prompt_embeds(
        self,
        prompt: str | list[str] = "",
        num_images_per_prompt: int = 1,
        max_sequence_length: int = 256,
        dtype: torch.dtype | None = None,
    ):
        dtype = dtype or self.text_encoder_3.dtype

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

        if self.text_encoder_3 is None:
            return torch.zeros(
                (
                    batch_size,
                    max_sequence_length,
                    self.transformer.joint_attention_dim,
                ),
                device=self.device,
                dtype=dtype,
            )

        text_inputs = self.tokenizer_3(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        ).to(self.device)
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer_3(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_3.batch_decode(untruncated_ids[:, self.tokenizer_max_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_3(text_input_ids.to(self.device))[0]

        dtype = self.text_encoder_3.dtype
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device)
        _, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        return prompt_embeds

    def encode_prompt(
        self,
        prompt: str | list[str],
        prompt_2: str | list[str],
        prompt_3: str | list[str],
        prompt_embeds: torch.Tensor | None = None,
        max_sequence_length: int = 256,
        num_images_per_prompt: int = 1,
    ):
        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
            prompt_3 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. 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.
        """

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

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

            prompt_3 = prompt_3 or prompt
            prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3

            prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
                prompt=prompt,
                num_images_per_prompt=num_images_per_prompt,
                clip_model_index=0,
            )
            prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
                prompt=prompt_2,
                num_images_per_prompt=num_images_per_prompt,
                clip_model_index=1,
            )
            clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)

            t5_prompt_embed = self._get_t5_prompt_embeds(
                prompt=prompt_3,
                num_images_per_prompt=num_images_per_prompt,
                max_sequence_length=max_sequence_length,
            )

            clip_prompt_embeds = torch.nn.functional.pad(
                clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
            )

            prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
            pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)

        return prompt_embeds, pooled_prompt_embeds

    def prepare_latents(
        self,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ) -> torch.Tensor:
        if latents is not None:
            return latents.to(device=device, dtype=dtype)

        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(width) // self.vae_scale_factor,
        )

        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)

        return latents

    def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
        scheduler_kwargs = {}
        if self.scheduler.config.get("use_dynamic_shifting", None):
            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", 1.16),
            )
            scheduler_kwargs["mu"] = mu
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            sigmas=sigmas,
            **scheduler_kwargs,
        )
        return timesteps, num_inference_steps

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

    @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,
        latents: torch.Tensor,
        timesteps: torch.Tensor,
        prompt_embeds: torch.Tensor,
        pooled_prompt_embeds: torch.Tensor,
        negative_prompt_embeds: torch.Tensor,
        negative_pooled_prompt_embeds: torch.Tensor,
        do_true_cfg: bool,
        guidance_scale: float,
        cfg_normalize: bool = False,
    ) -> torch.Tensor:
        """
        Diffusion loop with optional classifier-free guidance.

        Args:
            latents: Noise latents to denoise
            timesteps: Diffusion timesteps
            prompt_embeds: Positive prompt embeddings
            pooled_prompt_embeds: Pooled positive prompt embeddings
            negative_prompt_embeds: Negative prompt embeddings
            negative_pooled_prompt_embeds: Pooled negative prompt embeddings
            do_true_cfg: Whether to apply CFG
            guidance_scale: CFG scale factor
            cfg_normalize: Whether to normalize CFG output (default: False)

        Returns:
            Denoised latents
        """
        self.scheduler.set_begin_index(0)

        for _, t in enumerate(timesteps):
            if self.interrupt:
                continue
            self._current_timestep = t

            # Broadcast timestep to match batch size
            timestep = t.expand(latents.shape[0]).to(device=latents.device, dtype=latents.dtype)

            positive_kwargs = {
                "hidden_states": latents,
                "timestep": timestep,
                "encoder_hidden_states": prompt_embeds,
                "pooled_projections": pooled_prompt_embeds,
                "return_dict": False,
            }
            if do_true_cfg:
                negative_kwargs = {
                    "hidden_states": latents,
                    "timestep": timestep,
                    "encoder_hidden_states": negative_prompt_embeds,
                    "pooled_projections": negative_pooled_prompt_embeds,
                    "return_dict": False,
                }
            else:
                negative_kwargs = None

            # Predict noise with automatic CFG parallel handling
            noise_pred = self.predict_noise_maybe_with_cfg(
                do_true_cfg,
                guidance_scale,
                positive_kwargs,
                negative_kwargs,
                cfg_normalize,
            )

            # Compute the previous noisy sample x_t -> x_t-1 with automatic CFG sync
            latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)

        return latents

    def forward(
        self,
        req: OmniDiffusionRequest,
        prompt: str | list[str] = "",
        prompt_2: str | list[str] = "",
        prompt_3: str | list[str] = "",
        negative_prompt: str | list[str] = "",
        negative_prompt_2: str | list[str] = "",
        negative_prompt_3: str | list[str] = "",
        height: int | None = None,
        width: int | None = None,
        num_inference_steps: int = 28,
        sigmas: list[float] | None = None,
        num_images_per_prompt: int = 1,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: torch.Tensor | None = None,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        pooled_prompt_embeds: torch.Tensor | None = None,
        negative_pooled_prompt_embeds: torch.Tensor | None = None,
        max_sequence_length: int = 256,
    ) -> DiffusionOutput:
        # 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
        negative_prompt = [
            "" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts
        ] or negative_prompt

        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
        sigmas = req.sampling_params.sigmas or sigmas
        max_sequence_length = req.sampling_params.max_sequence_length or max_sequence_length
        num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
        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
        # 2. encode prompts
        # 3. prepare latents and timesteps
        # 4. diffusion process
        # 5. decode latents
        # 6. post-process outputs
        self.check_inputs(
            prompt,
            prompt_2,
            prompt_3,
            height,
            width,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            negative_prompt_3=negative_prompt_3,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            max_sequence_length=max_sequence_length,
        )

        self._guidance_scale = req.sampling_params.guidance_scale
        self._current_timestep = None
        self._interrupt = False

        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]

        prompt_embeds, pooled_prompt_embeds = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            prompt_3=prompt_3,
            prompt_embeds=prompt_embeds,
            max_sequence_length=max_sequence_length,
        )

        do_cfg = self.guidance_scale > 1
        if do_cfg:
            negative_prompt_embeds, negative_pooled_prompt_embeds = self.encode_prompt(
                prompt=negative_prompt,
                prompt_2=negative_prompt_2,
                prompt_3=negative_prompt_3,
                prompt_embeds=negative_prompt_embeds,
                max_sequence_length=max_sequence_length,
            )

        num_channels_latents = self.transformer.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            self.device,
            generator,
            latents,
        )

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

        # Denoising loop using diffuse method
        latents = self.diffuse(
            latents=latents,
            timesteps=timesteps,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds if do_cfg else None,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds if do_cfg else None,
            do_true_cfg=do_cfg,
            guidance_scale=self.guidance_scale,
            cfg_normalize=False,
        )

        self._current_timestep = None
        if self.output_type == "latent":
            image = latents
        else:
            latents = latents.to(self.vae.dtype)
            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)