Upload extensions_built_in/diffusion_models/hidream/src/pipelines/hidream_image/pipeline_hidream_image.py with huggingface_hub

extensions_built_in/diffusion_models/hidream/src/pipelines/hidream_image/pipeline_hidream_image.py

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+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+import math
+import einops
+import torch
+from transformers import (
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5Tokenizer,
+    LlamaForCausalLM,
+    PreTrainedTokenizerFast
+)
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_xla_available,
+    logging,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from .pipeline_output import HiDreamImagePipelineOutput
+from ...models.transformers.transformer_hidream_image import HiDreamImageTransformer2DModel
+from ...schedulers.fm_solvers_unipc import FlowUniPCMultistepScheduler
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+class HiDreamImagePipeline(DiffusionPipeline, FromSingleFileMixin):
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->text_encoder_4->image_encoder->transformer->vae"
+    _optional_components = ["image_encoder", "feature_extractor"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer_2: CLIPTokenizer,
+        text_encoder_3: T5EncoderModel,
+        tokenizer_3: T5Tokenizer,
+        text_encoder_4: LlamaForCausalLM,
+        tokenizer_4: PreTrainedTokenizerFast,
+        transformer: HiDreamImageTransformer2DModel,
+        aggressive_unloading: bool = False,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            text_encoder_3=text_encoder_3,
+            text_encoder_4=text_encoder_4,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            tokenizer_3=tokenizer_3,
+            tokenizer_4=tokenizer_4,
+            scheduler=scheduler,
+            transformer=transformer,
+        )
+        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
+        )
+        # HiDreamImage 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.default_sample_size = 128
+        self.tokenizer_4.pad_token = self.tokenizer_4.eos_token
+        self.aggressive_unloading = aggressive_unloading
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        max_sequence_length: int = 128,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder_3.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer_3(
+            prompt,
+            padding="max_length",
+            max_length=min(max_sequence_length, self.tokenizer_3.model_max_length),
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        attention_mask = text_inputs.attention_mask
+        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[:, min(max_sequence_length, self.tokenizer_3.model_max_length) - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {min(max_sequence_length, self.tokenizer_3.model_max_length)} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder_3(text_input_ids.to(device), attention_mask=attention_mask.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=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,
+        tokenizer,
+        text_encoder,
+        prompt: Union[str, List[str]],
+        num_images_per_prompt: int = 1,
+        max_sequence_length: int = 128,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=min(max_sequence_length, 218),
+            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[:, 218 - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {218} tokens: {removed_text}"
+            )
+        prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+        # Use pooled output of CLIPTextModel
+        prompt_embeds = prompt_embeds[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=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 _get_llama3_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        max_sequence_length: int = 128,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder_4.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer_4(
+            prompt,
+            padding="max_length",
+            max_length=min(max_sequence_length, self.tokenizer_4.model_max_length),
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        attention_mask = text_inputs.attention_mask
+        untruncated_ids = self.tokenizer_4(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_4.batch_decode(untruncated_ids[:, min(max_sequence_length, self.tokenizer_4.model_max_length) - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {min(max_sequence_length, self.tokenizer_4.model_max_length)} tokens: {removed_text}"
+            )
+
+        outputs = self.text_encoder_4(
+            text_input_ids.to(device), 
+            attention_mask=attention_mask.to(device), 
+            output_hidden_states=True,
+            output_attentions=True
+        )
+
+        prompt_embeds = outputs.hidden_states[1:]
+        prompt_embeds = torch.stack(prompt_embeds, dim=0)
+        _, _, seq_len, dim = prompt_embeds.shape
+
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, 1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(-1, batch_size * num_images_per_prompt, seq_len, dim)
+        return prompt_embeds
+    
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]],
+        prompt_3: Union[str, List[str]],
+        prompt_4: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt_3: Optional[Union[str, List[str]]] = None,
+        negative_prompt_4: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+        lora_scale: Optional[float] = None,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds[0].shape[0]
+
+        prompt_embeds, pooled_prompt_embeds = self._encode_prompt(
+            prompt = prompt,
+            prompt_2 = prompt_2,
+            prompt_3 = prompt_3,
+            prompt_4 = prompt_4,
+            device = device,
+            dtype = dtype,
+            num_images_per_prompt = num_images_per_prompt,
+            prompt_embeds = prompt_embeds,
+            pooled_prompt_embeds = pooled_prompt_embeds,
+            max_sequence_length = max_sequence_length,
+        )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+            negative_prompt_3 = negative_prompt_3 or negative_prompt
+            negative_prompt_4 = negative_prompt_4 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+            negative_prompt_3 = (
+                batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
+            )
+            negative_prompt_4 = (
+                batch_size * [negative_prompt_4] if isinstance(negative_prompt_4, str) else negative_prompt_4
+            )
+
+            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 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_pooled_prompt_embeds = self._encode_prompt(
+                prompt = negative_prompt,
+                prompt_2 = negative_prompt_2,
+                prompt_3 = negative_prompt_3,
+                prompt_4 = negative_prompt_4,
+                device = device,
+                dtype = dtype,
+                num_images_per_prompt = num_images_per_prompt,
+                prompt_embeds = negative_prompt_embeds,
+                pooled_prompt_embeds = negative_pooled_prompt_embeds,
+                max_sequence_length = max_sequence_length,
+            )
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    def _encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]],
+        prompt_3: Union[str, List[str]],
+        prompt_4: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+    ):
+        device = device or self._execution_device
+        
+        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_4 = prompt_4 or prompt
+            prompt_4 = [prompt_4] if isinstance(prompt_4, str) else prompt_4
+
+            pooled_prompt_embeds_1 = self._get_clip_prompt_embeds(
+                self.tokenizer,
+                self.text_encoder,
+                prompt = prompt,
+                num_images_per_prompt = num_images_per_prompt,
+                max_sequence_length = max_sequence_length,
+                device = device,
+                dtype = dtype,
+            )
+
+            pooled_prompt_embeds_2 = self._get_clip_prompt_embeds(
+                self.tokenizer_2,
+                self.text_encoder_2,
+                prompt = prompt_2,
+                num_images_per_prompt = num_images_per_prompt,
+                max_sequence_length = max_sequence_length,
+                device = device,
+                dtype = dtype,
+            )
+
+            pooled_prompt_embeds = torch.cat([pooled_prompt_embeds_1, pooled_prompt_embeds_2], dim=-1)
+
+            t5_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt = prompt_3,
+                num_images_per_prompt = num_images_per_prompt,
+                max_sequence_length = max_sequence_length,
+                device = device,
+                dtype = dtype
+            )
+            llama3_prompt_embeds = self._get_llama3_prompt_embeds(
+                prompt = prompt_4,
+                num_images_per_prompt = num_images_per_prompt,
+                max_sequence_length = max_sequence_length,
+                device = device,
+                dtype = dtype
+            )
+            prompt_embeds = [t5_prompt_embeds, llama3_prompt_embeds]
+
+        return prompt_embeds, pooled_prompt_embeds
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    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 None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+        return latents
+    
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+    
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+    
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+    
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        prompt_3: Optional[Union[str, List[str]]] = None,
+        prompt_4: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt_3: Optional[Union[str, List[str]]] = None,
+        negative_prompt_4: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 128,
+    ):
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        division = self.vae_scale_factor * 2
+        S_max = (self.default_sample_size * self.vae_scale_factor) ** 2
+        scale = S_max / (width * height)
+        scale = math.sqrt(scale)
+        width, height = int(width * scale // division * division), int(height * scale // division * division)
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        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[0].shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_3=prompt_3,
+            prompt_4=prompt_4,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            negative_prompt_3=negative_prompt_3,
+            negative_prompt_4=negative_prompt_4,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds_arr = []
+            for n, p in zip(negative_prompt_embeds, prompt_embeds):
+                if len(n.shape) == 3:
+                    prompt_embeds_arr.append(torch.cat([n, p], dim=0))
+                else:
+                    prompt_embeds_arr.append(torch.cat([n, p], dim=1))
+            prompt_embeds = prompt_embeds_arr
+            pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            pooled_prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        if latents.shape[-2] != latents.shape[-1]:
+            B, C, H, W = latents.shape
+            pH, pW = H // self.transformer.config.patch_size, W // self.transformer.config.patch_size
+
+            img_sizes = torch.tensor([pH, pW], dtype=torch.int64).reshape(-1)
+            img_ids = torch.zeros(pH, pW, 3)
+            img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH)[:, None]
+            img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW)[None, :]
+            img_ids = img_ids.reshape(pH * pW, -1)
+            img_ids_pad = torch.zeros(self.transformer.max_seq, 3)
+            img_ids_pad[:pH*pW, :] = img_ids
+
+            img_sizes = img_sizes.unsqueeze(0).to(latents.device) 
+            img_ids = img_ids_pad.unsqueeze(0).to(latents.device) 
+            if self.do_classifier_free_guidance:
+                img_sizes = img_sizes.repeat(2 * B, 1)
+                img_ids = img_ids.repeat(2 * B, 1, 1)
+        else:
+            img_sizes = img_ids = None
+
+        # 5. Prepare timesteps
+        mu = calculate_shift(self.transformer.max_seq)
+        scheduler_kwargs = {"mu": mu}
+        if isinstance(self.scheduler, FlowUniPCMultistepScheduler):
+            self.scheduler.set_timesteps(num_inference_steps, device=device, shift=math.exp(mu))
+            timesteps = self.scheduler.timesteps
+        else:
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler,
+                num_inference_steps,
+                device,
+                sigmas=sigmas,
+                **scheduler_kwargs,
+            )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                if latent_model_input.shape[-2] != latent_model_input.shape[-1]:
+                    B, C, H, W = latent_model_input.shape
+                    patch_size = self.transformer.config.patch_size
+                    pH, pW = H // patch_size, W // patch_size
+                    out = torch.zeros(
+                        (B, C, self.transformer.max_seq, patch_size * patch_size), 
+                        dtype=latent_model_input.dtype, 
+                        device=latent_model_input.device
+                    )
+                    latent_model_input = einops.rearrange(latent_model_input, 'B C (H p1) (W p2) -> B C (H W) (p1 p2)', p1=patch_size, p2=patch_size)
+                    out[:, :, 0:pH*pW] = latent_model_input
+                    latent_model_input = out
+
+                noise_pred = self.transformer(
+                    hidden_states = latent_model_input,
+                    timesteps = timestep,
+                    encoder_hidden_states = prompt_embeds,
+                    pooled_embeds = pooled_prompt_embeds,
+                    img_sizes = img_sizes,
+                    img_ids = img_ids,
+                    return_dict = False,
+                )[0]
+                noise_pred = -noise_pred
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return HiDreamImagePipelineOutput(images=image)