vllm-omni/vllm_omni/diffusion/models/longcat_image/pipeline_longcat_image.py

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

from __future__ import annotations

import inspect
import json
import os
import re
from collections.abc import Iterable
from functools import partial
from typing import Any

import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.pipelines.longcat_image.system_messages import SYSTEM_PROMPT_EN, SYSTEM_PROMPT_ZH
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, SchedulerMixin
from diffusers.utils.torch_utils import randn_tensor
from torch import nn
from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration, Qwen2VLProcessor
from vllm.logger import init_logger
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.longcat_image.longcat_image_transformer import LongCatImageTransformer2DModel
from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.model_executor.model_loader.weight_utils import (
    download_weights_from_hf_specific,
)

logger = init_logger(__name__)


def get_longcat_image_post_process_func(
    od_config: OmniDiffusionConfig,
):
    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


def split_quotation(prompt, quote_pairs=None):
    """
    Implement a regex-based string splitting algorithm that identifies delimiters
    defined by single or double quote pairs.

    Examples::
        >>> prompt_en = "Please write 'Hello' on the blackboard for me."
        >>> print(split_quotation(prompt_en))
        >>> # output: [('Please write ', False), ("'Hello'", True), (' on the blackboard for me.', False)]
    """
    word_internal_quote_pattern = re.compile(r"[a-zA-Z]+'[a-zA-Z]+")
    matches_word_internal_quote_pattern = word_internal_quote_pattern.findall(prompt)
    mapping_word_internal_quote = []

    for i, word_src in enumerate(set(matches_word_internal_quote_pattern)):
        word_tgt = "longcat_$##$_longcat" * (i + 1)
        prompt = prompt.replace(word_src, word_tgt)
        mapping_word_internal_quote.append([word_src, word_tgt])

    if quote_pairs is None:
        quote_pairs = [("'", "'"), ('"', '"'), ("‘", "’"), ("“", "”")]
    pattern = "|".join([re.escape(q1) + r"[^" + re.escape(q1 + q2) + r"]*?" + re.escape(q2) for q1, q2 in quote_pairs])
    parts = re.split(f"({pattern})", prompt)

    result = []
    for part in parts:
        for word_src, word_tgt in mapping_word_internal_quote:
            part = part.replace(word_tgt, word_src)
        if re.match(pattern, part):
            if len(part):
                result.append((part, True))
        else:
            if len(part):
                result.append((part, False))
    return result


def prepare_pos_ids(modality_id=0, type="text", start=(0, 0), num_token=None, height=None, width=None) -> torch.Tensor:
    if type == "text":
        assert num_token
        if height or width:
            logger.warning('Warning: The parameters of height and width will be ignored in "text" type.')
        pos_ids = torch.zeros(num_token, 3)
        pos_ids[..., 0] = modality_id
        pos_ids[..., 1] = torch.arange(num_token) + start[0]
        pos_ids[..., 2] = torch.arange(num_token) + start[1]
    elif type == "image":
        assert height and width
        if num_token:
            logger.warning('Warning: The parameter of num_token will be ignored in "image" type.')
        pos_ids = torch.zeros(height, width, 3)
        pos_ids[..., 0] = modality_id
        pos_ids[..., 1] = pos_ids[..., 1] + torch.arange(height)[:, None] + start[0]
        pos_ids[..., 2] = pos_ids[..., 2] + torch.arange(width)[None, :] + start[1]
        pos_ids = pos_ids.reshape(height * width, 3)
    else:
        raise KeyError(f'Unknown type {type}, only support "text" or "image".')
    # pos_ids = pos_ids[None, :].repeat(batch_size, 1, 1)
    return pos_ids


def retrieve_timesteps(
    scheduler: SchedulerMixin,
    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


def get_prompt_language(prompt):
    pattern = re.compile(r"[\u4e00-\u9fff]")
    if bool(pattern.search(prompt)):
        return "zh"
    return "en"


class LongCatImagePipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
    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
        local_files_only = os.path.exists(model)

        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
            model, subfolder="scheduler", local_files_only=local_files_only
        )

        self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
            model, subfolder="text_encoder", local_files_only=local_files_only
        )
        self.text_processor = Qwen2VLProcessor.from_pretrained(
            model, subfolder="tokenizer", 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 = LongCatImageTransformer2DModel(od_config=od_config)
        self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)

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

        self.prompt_template_encode_prefix = (
            "<|im_start|>system\n"
            "As an image captioning expert, generate a descriptive text prompt based on an image content,"
            " suitable for input to a text-to-image model.<|im_end|>\n"
            "<|im_start|>user\n"
        )
        self.prompt_template_encode_suffix = "<|im_end|>\n<|im_start|>assistant\n"

        self.default_sample_size = 128
        self.tokenizer_max_length = 512
        self.setup_diffusion_pipeline_profiler(
            enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
        )

    def rewire_prompt(self, prompt, device):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        all_text = []
        for each_prompt in prompt:
            language = get_prompt_language(each_prompt)
            if language == "zh":
                question = SYSTEM_PROMPT_ZH + f"\n用户输入为：{each_prompt}\n改写后的prompt为："
            else:
                question = SYSTEM_PROMPT_EN + f"\nUser Input: {each_prompt}\nRewritten prompt:"
            message = [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": question},
                    ],
                }
            ]
            text = self.text_processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
            all_text.append(text)

        inputs = self.text_processor(text=all_text, padding=True, return_tensors="pt").to(device)

        self.text_encoder.to(device)
        generated_ids = self.text_encoder.generate(**inputs, max_new_tokens=self.tokenizer_max_length)
        generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
        output_text = self.text_processor.batch_decode(
            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
        )
        return output_text

    def _encode_prompt(self, prompt: list[str]) -> torch.Tensor:
        batch_all_tokens = []

        for each_prompt in prompt:
            all_tokens = []
            for clean_prompt_sub, matched in split_quotation(each_prompt):
                if matched:
                    for sub_word in clean_prompt_sub:
                        tokens = self.tokenizer(sub_word, add_special_tokens=False)["input_ids"]
                        all_tokens.extend(tokens)
                else:
                    tokens = self.tokenizer(clean_prompt_sub, add_special_tokens=False)["input_ids"]
                    all_tokens.extend(tokens)

            if len(all_tokens) > self.tokenizer_max_length:
                logger.warning(
                    "Your input was truncated because `max_sequence_length` is set to "
                    f"{self.tokenizer_max_length} input token nums : {len(all_tokens)}"
                )
                all_tokens = all_tokens[: self.tokenizer_max_length]
            batch_all_tokens.append(all_tokens)

        text_tokens_and_mask = self.tokenizer.pad(
            {"input_ids": batch_all_tokens},
            max_length=self.tokenizer_max_length,
            padding="max_length",
            return_attention_mask=True,
            return_tensors="pt",
        )

        prefix_tokens = self.tokenizer(self.prompt_template_encode_prefix, add_special_tokens=False)["input_ids"]
        suffix_tokens = self.tokenizer(self.prompt_template_encode_suffix, add_special_tokens=False)["input_ids"]
        prefix_len = len(prefix_tokens)
        suffix_len = len(suffix_tokens)

        prefix_tokens_mask = torch.tensor([1] * len(prefix_tokens), dtype=text_tokens_and_mask.attention_mask[0].dtype)
        suffix_tokens_mask = torch.tensor([1] * len(suffix_tokens), dtype=text_tokens_and_mask.attention_mask[0].dtype)

        prefix_tokens = torch.tensor(prefix_tokens, dtype=text_tokens_and_mask.input_ids.dtype)
        suffix_tokens = torch.tensor(suffix_tokens, dtype=text_tokens_and_mask.input_ids.dtype)

        batch_size = text_tokens_and_mask.input_ids.size(0)
        prefix_tokens_batch = prefix_tokens.unsqueeze(0).expand(batch_size, -1)
        suffix_tokens_batch = suffix_tokens.unsqueeze(0).expand(batch_size, -1)
        prefix_mask_batch = prefix_tokens_mask.unsqueeze(0).expand(batch_size, -1)
        suffix_mask_batch = suffix_tokens_mask.unsqueeze(0).expand(batch_size, -1)

        input_ids = torch.cat((prefix_tokens_batch, text_tokens_and_mask.input_ids, suffix_tokens_batch), dim=-1)
        attention_mask = torch.cat((prefix_mask_batch, text_tokens_and_mask.attention_mask, suffix_mask_batch), dim=-1)

        input_ids = input_ids.to(self.device)
        attention_mask = attention_mask.to(self.device)

        text_output = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True)
        prompt_embeds = text_output.hidden_states[-1].detach()
        prompt_embeds = prompt_embeds[:, prefix_len:-suffix_len, :]
        return prompt_embeds

    def encode_prompt(
        self,
        prompt: str | list[str] | None = None,
        num_images_per_prompt: int | None = 1,
        prompt_embeds: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if prompt_embeds is None and prompt is None:
            raise ValueError("Provide either `prompt` or `prompt_embeds`.")

        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt_embeds is None:
            prompt_embeds = self._encode_prompt(prompt)
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        _, 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)

        text_ids = prepare_pos_ids(modality_id=0, type="text", start=(0, 0), num_token=prompt_embeds.shape[1]).to(
            self.device
        )
        return prompt_embeds.to(self.device), text_ids

    @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

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

    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)
        latent_image_ids = prepare_pos_ids(
            modality_id=1,
            type="image",
            start=(self.tokenizer_max_length, self.tokenizer_max_length),
            height=height // 2,
            width=width // 2,
        ).to(device)

        if latents is not None:
            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)
        latents = latents.to(dtype=dtype)
        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)

        return latents, latent_image_ids

    def check_inputs(
        self, prompt, height, width, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
    ):
        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
            logger.warning(
                "`height` and `width` have to be divisible by "
                f"{self.vae_scale_factor * 2} 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 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, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        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."
            )

    def cfg_normalize_function(self, noise_pred, comb_pred, cfg_renorm_min=0.0):
        """
        Normalize the combined noise prediction.
        """
        cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
        noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
        scale = (cond_norm / (noise_norm + 1e-8)).clamp(min=cfg_renorm_min, max=1.0)
        noise_pred = comb_pred * scale
        return noise_pred

    def forward(
        self,
        req: OmniDiffusionRequest,
        prompt: str | list[str] | None = None,
        negative_prompt: str | list[str] | None = None,
        height: int | None = None,
        width: int | None = None,
        num_inference_steps: int = 50,
        sigmas: list[float] | None = None,
        guidance_scale: float = 4.5,
        num_images_per_prompt: int = 1,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: torch.FloatTensor | None = None,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        output_type: str | None = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: dict[str, Any] | None = None,
        enable_cfg_renorm: bool | None = True,
        cfg_renorm_min: float | None = 0.0,
        enable_prompt_rewrite: bool | None = True,
    ) -> 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
        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 height or self.default_sample_size * self.vae_scale_factor
        width = req.sampling_params.width or 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
        generator = req.sampling_params.generator or generator
        guidance_scale = (
            req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
        )
        num_images_per_prompt = (
            req.sampling_params.num_outputs_per_prompt
            if req.sampling_params.num_outputs_per_prompt is not None
            else num_images_per_prompt
        )
        enable_prompt_rewrite = req.sampling_params.extra_args.get("enable_prompt_rewrite", enable_prompt_rewrite)
        enable_cfg_renorm = req.sampling_params.extra_args.get("enable_cfg_renorm", enable_cfg_renorm)
        cfg_renorm_min = req.sampling_params.extra_args.get("cfg_renorm_min", cfg_renorm_min)

        req_prompt_embeds = [p.get("prompt_embeds") if not isinstance(p, str) else None for p in req.prompts]
        if any(p is not None for p in req_prompt_embeds):
            # If at list one prompt is provided as an embedding,
            # Then assume that the user wants to provide embeddings for all prompts, and enter this if block
            # If the user in fact provides mixed input format, req_prompt_embeds will have some None's
            # And `torch.stack` automatically raises an exception for us
            prompt_embeds = torch.stack(req_prompt_embeds)  # type: ignore # intentionally expect TypeError

        req_negative_prompt_embeds = [
            p.get("negative_prompt_embeds") if not isinstance(p, str) else None for p in req.prompts
        ]
        if any(p is not None for p in req_negative_prompt_embeds):
            negative_prompt_embeds = torch.stack(req_negative_prompt_embeds)  # type: ignore # intentionally expect TypeError

        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        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]

        device = self.device
        if enable_prompt_rewrite and prompt is not None:
            prompt = self.rewire_prompt(prompt if isinstance(prompt, list) else [prompt], device)

        negative_prompt = "" if negative_prompt is None else negative_prompt

        (prompt_embeds, text_ids) = self.encode_prompt(
            prompt=prompt,
            prompt_embeds=prompt_embeds,
            num_images_per_prompt=num_images_per_prompt,
        )
        if self.do_classifier_free_guidance:
            (negative_prompt_embeds, negative_text_ids) = self.encode_prompt(
                prompt=negative_prompt,
                prompt_embeds=negative_prompt_embeds,
                num_images_per_prompt=num_images_per_prompt,
            )

        # 4. Prepare latent variables
        num_channels_latents = 16
        latents, latent_image_ids = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 5. Prepare timesteps
        sigmas = np.linspace(1.0, 1.0 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
        image_seq_len = latents.shape[1]
        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,
            device,
            sigmas=sigmas,
            mu=mu,
        )

        self._num_timesteps = len(timesteps)

        # handle guidance
        guidance = None

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

        prompt_embeds = prompt_embeds.to(device)
        if self.do_classifier_free_guidance:
            negative_prompt_embeds = negative_prompt_embeds.to(device)

        # custom partial function with cfg_renorm_min
        self.cfg_normalize_function = partial(self.cfg_normalize_function, cfg_renorm_min=cfg_renorm_min)

        # 6. Denoising loop
        for i, t in enumerate(timesteps):
            if self._interrupt:
                continue

            self._current_timestep = t
            timestep = t.expand(latents.shape[0]).to(latents.dtype)

            positive_kwargs = {
                "hidden_states": latents,
                "timestep": timestep / 1000,
                "guidance": guidance,
                "encoder_hidden_states": prompt_embeds,
                "txt_ids": text_ids,
                "img_ids": latent_image_ids,
                "return_dict": False,
            }
            if self.do_classifier_free_guidance:
                negative_kwargs = {
                    "hidden_states": latents,
                    "timestep": timestep / 1000,
                    "encoder_hidden_states": negative_prompt_embeds,
                    "txt_ids": negative_text_ids,
                    "img_ids": latent_image_ids,
                    "return_dict": False,
                }
            else:
                negative_kwargs = None

            noise_pred = self.predict_noise_maybe_with_cfg(
                do_true_cfg=self.do_classifier_free_guidance,
                true_cfg_scale=guidance_scale,
                positive_kwargs=positive_kwargs,
                negative_kwargs=negative_kwargs,
                cfg_normalize=enable_cfg_renorm,
            )

            # 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, self.do_classifier_free_guidance)

        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

            if latents.dtype != self.vae.dtype:
                latents = latents.to(dtype=self.vae.dtype)

            image = self.vae.decode(latents, return_dict=False)[0]

        return DiffusionOutput(
            output=image, stage_durations=self.stage_durations if hasattr(self, "stage_durations") else None
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        """Load weights using AutoWeightsLoader for vLLM integration."""
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)