Delete diffusion/pipelines/onediffusion.py with huggingface_hub

diffusion/pipelines/onediffusion.py

@@ -1,1080 +0,0 @@
-import einops
-import inspect
-import torch
-import numpy as np
-import PIL
-import os
-
-from dataclasses import dataclass
-from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.utils import (
-    CONFIG_NAME,
-    DEPRECATED_REVISION_ARGS,
-    BaseOutput,
-    PushToHubMixin,
-    deprecate,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_torch_npu_available,
-    is_torch_version,
-    logging,
-    numpy_to_pil,
-    replace_example_docstring,
-)
-from diffusers.models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, ModelMixin
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.utils import BaseOutput
-# from diffusers.image_processor import VaeImageProcessor
-from transformers import T5EncoderModel, T5Tokenizer
-from typing import Any, Callable, Dict, List, Optional, Union
-from PIL import Image
-
-from onediffusion.models.denoiser.nextdit import NextDiT
-from onediffusion.dataset.utils import *
-from onediffusion.dataset.multitask.multiview import calculate_rays
-from onediffusion.diffusion.pipelines.image_processor import VaeImageProcessorOneDiffuser
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-SUPPORTED_DEVICE_MAP = ["balanced"]
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from one_diffusion import OneDiffusionPipeline
-
-        >>> pipe = OneDiffusionPipeline.from_pretrained("path_to_one_diffuser_model")
-        >>> pipe = pipe.to("cuda")
-
-        >>> prompt = "A beautiful sunset over the ocean"
-        >>> image = pipe(prompt).images[0]
-        >>> image.save("beautiful_sunset.png")
-        ```
-"""
-
-def create_c2w_matrix(azimuth_deg, elevation_deg, distance=1.0, target=np.array([0, 0, 0])):
-    """
-    Create a Camera-to-World (C2W) matrix from azimuth and elevation angles.
-
-    Parameters:
-    - azimuth_deg: Azimuth angle in degrees.
-    - elevation_deg: Elevation angle in degrees.
-    - distance: Distance from the target point.
-    - target: The point the camera is looking at in world coordinates.
-
-    Returns:
-    - C2W: A 4x4 NumPy array representing the Camera-to-World transformation matrix.
-    """
-    # Convert angles from degrees to radians
-    azimuth = np.deg2rad(azimuth_deg)
-    elevation = np.deg2rad(elevation_deg)
-
-    # Spherical to Cartesian conversion for camera position
-    x = distance * np.cos(elevation) * np.cos(azimuth)
-    y = distance * np.cos(elevation) * np.sin(azimuth)
-    z = distance * np.sin(elevation)
-    camera_position = np.array([x, y, z])
-
-    # Define the forward vector (from camera to target)
-    target = 2*camera_position - target
-    forward = target - camera_position
-    forward /= np.linalg.norm(forward)
-
-    # Define the world up vector
-    world_up = np.array([0, 0, 1])
-
-    # Compute the right vector
-    right = np.cross(world_up, forward)
-    if np.linalg.norm(right) < 1e-6:
-        # Handle the singularity when forward is parallel to world_up
-        world_up = np.array([0, 1, 0])
-        right = np.cross(world_up, forward)
-    right /= np.linalg.norm(right)
-
-    # Recompute the orthogonal up vector
-    up = np.cross(forward, right)
-
-    # Construct the rotation matrix
-    rotation = np.vstack([right, up, forward]).T  # 3x3
-
-    # Construct the full C2W matrix
-    C2W = np.eye(4)
-    C2W[:3, :3] = rotation
-    C2W[:3, 3] = camera_position
-
-    return C2W
-
-@dataclass
-class OneDiffusionPipelineOutput(BaseOutput):
-    """
-    Output class for Stable Diffusion pipelines.
-
-    Args:
-        images (`List[PIL.Image.Image]` or `np.ndarray`)
-            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
-            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
-    """
-
-    images: Union[List[Image.Image], np.ndarray]
-    latents: Optional[torch.Tensor] = None
-
-
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-    
-    
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.16,
-    # max_clip: float = 1.5,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len) # 0.000169270833
-    b = base_shift - m * base_seq_len # 0.5-0.0433333332
-    mu = image_seq_len * m + b
-    # mu = min(mu, max_clip)
-    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,
-):
-    """
-    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 OneDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using OneDiffuser.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        transformer ([`NextDiT`]):
-            Conditional transformer (NextDiT) architecture to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            Frozen text-encoder. OneDiffuser uses the T5 model as text encoder.
-        tokenizer (`T5Tokenizer`):
-            Tokenizer of class T5Tokenizer.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-    """
-
-    def __init__(
-        self,
-        transformer: NextDiT,
-        vae: AutoencoderKL,
-        text_encoder: T5EncoderModel,
-        tokenizer: T5Tokenizer,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-    ):
-        super().__init__()
-        self.register_modules(
-            transformer=transformer,
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessorOneDiffuser(vae_scale_factor=self.vae_scale_factor)
-
-    def enable_vae_slicing(self):
-        self.vae.enable_slicing()
-
-    def disable_vae_slicing(self):
-        self.vae.disable_slicing()
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.transformer, self.text_encoder, self.vae]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    def _execution_device(self):
-        if self.device != torch.device("meta") or not hasattr(self.transformer, "_hf_hook"):
-            return self.device
-        for module in self.transformer.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        max_length=300,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=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(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[:, max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {max_length} tokens: {removed_text}"
-            )
-
-        text_encoder_output = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask.to(device))
-        prompt_embeds = text_encoder_output[0].to(torch.float32)
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # duplicate attention mask for each generation per prompt
-        attention_mask = attention_mask.repeat(1, num_images_per_prompt)
-        attention_mask = attention_mask.view(bs_embed * num_images_per_prompt, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            uncond_encoder_output = self.text_encoder(uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device))
-            negative_prompt_embeds = uncond_encoder_output[0].to(torch.float32)
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # duplicate unconditional attention mask for each generation per prompt
-            uncond_attention_mask = uncond_input.attention_mask.repeat(1, num_images_per_prompt)
-            uncond_attention_mask = uncond_attention_mask.view(batch_size * num_images_per_prompt, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            attention_mask = torch.cat([uncond_attention_mask, attention_mask])
-
-        return prompt_embeds.to(device), attention_mask.to(device)
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        forward_kwargs: Optional[Dict[str, Any]] = {},
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-            height (`int`, *optional*, defaults to self.transformer.config.sample_size):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.transformer.config.sample_size):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        height = height or self.transformer.config.input_size[-2] * 8 # TODO: Hardcoded downscale factor of vae
-        width = width or self.transformer.config.input_size[-1] * 8
-
-        # check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        encoder_hidden_states, encoder_attention_mask = self.encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-        )
-
-        # set timesteps
-        # # self.scheduler.set_timesteps(num_inference_steps, device=device)
-        # timesteps = self.scheduler.timesteps
-        timesteps = None
-
-        # 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,
-            self.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # prepare extra step kwargs
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 5. Prepare timesteps
-        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-        image_seq_len = latents.shape[-1] * latents.shape[-2] / self.transformer.config.patch_size[-1] / self.transformer.config.patch_size[-2]
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
-        )
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps,
-            sigmas,
-            mu=mu,
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-        
-        # denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                # latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.transformer(
-                    samples=latent_model_input.to(self.dtype),
-                    timesteps=torch.tensor([t] * latent_model_input.shape[0], device=device),
-                    encoder_hidden_states=encoder_hidden_states.to(self.dtype),
-                    encoder_attention_mask=encoder_attention_mask,
-                    **forward_kwargs
-                )
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # 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 callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        if latents.ndim == 5:
-            latents = latents.squeeze(1)
-        image = self.vae.decode(latents.to(self.vae.dtype)).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, None)
-
-        return OneDiffusionPipelineOutput(images=image)
-
-    @torch.no_grad()
-    def img2img(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[PIL.Image.Image, List[PIL.Image.Image]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        denoise_mask: Optional[List[int]] = [1, 0],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        do_crop: bool = True,
-        is_multiview: bool = False,
-        multiview_azimuths: Optional[List[int]] = [0, 30, 60, 90],
-        multiview_elevations: Optional[List[int]] = [0, 0, 0, 0],
-        multiview_distances: float = 1.7,
-        multiview_c2ws: Optional[List[torch.Tensor]] = None,
-        multiview_intrinsics: Optional[torch.Tensor] = None,
-        multiview_focal_length: float = 1.3887,
-        forward_kwargs: Optional[Dict[str, Any]] = {},
-        noise_scale: float = 1.0,
-        **kwargs,
-):
-        # Convert single image to list for consistent handling
-        if isinstance(image, PIL.Image.Image):
-            image = [image]
-            
-        if height is None or width is None:
-            closest_ar = get_closest_ratio(height=image[0].size[1], width=image[0].size[0], ratios=ASPECT_RATIO_512)
-            height, width = int(closest_ar[0][0]), int(closest_ar[0][1])
-        
-        if not isinstance(multiview_distances, list) and not isinstance(multiview_distances, tuple):
-            multiview_distances = [multiview_distances] * len(multiview_azimuths)
-            
-        # height = height or self.transformer.config.input_size[-2] * 8  # TODO: Hardcoded downscale factor of vae
-        # width = width or self.transformer.config.input_size[-1] * 8
-
-        # 1. check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # Additional input validation for image list
-        if not all(isinstance(img, PIL.Image.Image) for img in image):
-            raise ValueError("All elements in image list must be PIL.Image objects")
-
-        # 2. define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        encoder_hidden_states, encoder_attention_mask = self.encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-        )
-
-        # 4. Preprocess all images
-        if image is not None and len(image) > 0:
-            processed_image = self.image_processor.preprocess(image, height=height, width=width, do_crop=do_crop)
-        else:
-            processed_image = None
-            
-        # # Stack processed images along the sequence dimension
-        # if len(processed_images) > 1:
-        #     processed_image = torch.cat(processed_images, dim=0)
-        # else:
-        #     processed_image = processed_images[0]
-
-        timesteps = None
-
-        # 6. prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels
-        if processed_image is not None:
-            cond_latents = self.prepare_latents(
-                batch_size * num_images_per_prompt,
-                num_channels_latents,
-                height,
-                width,
-                self.dtype,
-                device,
-                generator,
-                latents,
-                image=processed_image,
-            )
-        else:
-            cond_latents = None
-            
-        # 7. prepare extra step kwargs
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-        denoise_mask = torch.tensor(denoise_mask, device=device)
-        denoise_indices = torch.where(denoise_mask == 1)[0]
-        cond_indices = torch.where(denoise_mask == 0)[0]
-        seq_length = denoise_mask.shape[0]
-
-        latents = self.prepare_init_latents(
-            batch_size * num_images_per_prompt,
-            seq_length,
-            num_channels_latents,
-            height,
-            width,
-            self.dtype,
-            device,
-            generator,
-        )
-
-        # 5. Prepare timesteps
-        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-        # image_seq_len = latents.shape[1] * latents.shape[-1] * latents.shape[-2] / self.transformer.config.patch_size[-1] / self.transformer.config.patch_size[-2]
-        image_seq_len = noise_scale * sum(denoise_mask) * latents.shape[-1] * latents.shape[-2] / self.transformer.config.patch_size[-1] / self.transformer.config.patch_size[-2]
-        # image_seq_len = 256
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
-        )
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps,
-            sigmas,
-            mu=mu,
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-        
-        if is_multiview:
-            cond_indices_images = [index // 2 for index in cond_indices if index % 2 == 0]
-            cond_indices_rays = [index // 2 for index in cond_indices if index % 2 == 1]
-                        
-            multiview_elevations = [element for element in multiview_elevations if element is not None]
-            multiview_azimuths = [element for element in multiview_azimuths if element is not None]
-            multiview_distances = [element for element in multiview_distances if element is not None]
-            
-            if multiview_c2ws is None:
-                multiview_c2ws = [
-                    torch.tensor(create_c2w_matrix(azimuth, elevation, distance)) for azimuth, elevation, distance in zip(multiview_azimuths, multiview_elevations, multiview_distances)
-                ]
-                c2ws = torch.stack(multiview_c2ws).float()
-            else:
-                c2ws = torch.Tensor(multiview_c2ws).float()    
-                
-            c2ws[:, 0:3, 1:3] *= -1
-            c2ws = c2ws[:, [1, 0, 2, 3], :]
-            c2ws[:, 2, :] *= -1
-            
-            w2cs = torch.inverse(c2ws)
-            if multiview_intrinsics is None:
-                multiview_intrinsics = torch.Tensor([[[multiview_focal_length, 0, 0.5], [0, multiview_focal_length, 0.5], [0, 0, 1]]]).repeat(c2ws.shape[0], 1, 1)
-            K = multiview_intrinsics
-            Rs = w2cs[:, :3, :3]
-            Ts = w2cs[:, :3, 3]
-            sizes = torch.Tensor([[1, 1]]).repeat(c2ws.shape[0], 1)
-
-            assert height == width
-            cond_rays = calculate_rays(K, sizes, Rs, Ts, height // 8)
-            cond_rays = cond_rays.reshape(-1, height // 8, width // 8, 6)
-            # padding = (0, 10)
-            # cond_rays = torch.nn.functional.pad(cond_rays, padding, "constant", 0)
-            cond_rays = torch.cat([cond_rays, cond_rays, cond_rays[..., :4]], dim=-1) * 1.658
-            cond_rays = cond_rays[None].repeat(batch_size * num_images_per_prompt, 1, 1, 1, 1)
-            cond_rays = cond_rays.permute(0, 1, 4, 2, 3)
-            cond_rays = cond_rays.to(device, dtype=self.dtype)
-
-            latents = einops.rearrange(latents, "b (f n) c h w -> b f n c h w", n=2)
-            if cond_latents is not None:
-                latents[:, cond_indices_images, 0] = cond_latents
-            latents[:, cond_indices_rays, 1] = cond_rays
-            latents = einops.rearrange(latents, "b f n c h w -> b (f n) c h w")
-        else:
-            if cond_latents is not None:
-                latents[:, cond_indices] = cond_latents
-        
-        # denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                input_t = torch.broadcast_to(einops.repeat(torch.Tensor([t]).to(device), "1 -> 1 f 1 1 1", f=latent_model_input.shape[1]), latent_model_input.shape).clone()
-                
-                if is_multiview:
-                    input_t = einops.rearrange(input_t, "b (f n) c h w -> b f n c h w", n=2)
-                    input_t[:, cond_indices_images, 0] = self.scheduler.timesteps[-1]
-                    input_t[:, cond_indices_rays, 1] = self.scheduler.timesteps[-1]
-                    input_t = einops.rearrange(input_t, "b f n c h w -> b (f n) c h w")
-                else:
-                    input_t[:, cond_indices] = self.scheduler.timesteps[-1]
-
-                # predict the noise residual
-                noise_pred = self.transformer(
-                    samples=latent_model_input.to(self.dtype),
-                    timesteps=input_t,
-                    encoder_hidden_states=encoder_hidden_states.to(self.dtype),
-                    encoder_attention_mask=encoder_attention_mask,
-                    **forward_kwargs
-                )
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                bs, n_frame = noise_pred.shape[:2]
-                noise_pred = einops.rearrange(noise_pred, "b f c h w -> (b f) c h w")
-                latents = einops.rearrange(latents, "b f c h w -> (b f) c h w")
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-                latents = einops.rearrange(latents, "(b f) c h w -> b f c h w", b=bs, f=n_frame)
-                if is_multiview:
-                    latents = einops.rearrange(latents, "b (f n) c h w -> b f n c h w", n=2)
-                    if cond_latents is not None:
-                        latents[:, cond_indices_images, 0] = cond_latents
-                    latents[:, cond_indices_rays, 1] = cond_rays
-                    latents = einops.rearrange(latents, "b f n c h w -> b (f n) c h w")
-                else:
-                    if cond_latents is not None:
-                        latents[:, cond_indices] = cond_latents
-
-                # 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 callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        decoded_latents = latents / 1.658
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        if latents.ndim == 5:
-            latents = latents[:, denoise_indices]
-            latents = einops.rearrange(latents, "b f c h w -> (b f) c h w")
-        image = self.vae.decode(latents.to(self.vae.dtype)).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, None)
-
-        return OneDiffusionPipelineOutput(images=image, latents=decoded_latents)
-    
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, height, width, callback_steps):
-        if 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)}")
-
-        if height % 16 != 0 or width % 16 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        if image is None:
-            # scale the initial noise by the standard deviation required by the scheduler
-            # latents = latents * self.scheduler.init_noise_sigma
-            return latents
-        
-        image = image.to(device=device, dtype=dtype)
-        
-        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."
-            )
-        elif isinstance(generator, list):
-            if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
-                image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
-            elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
-                raise ValueError(
-                    f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
-                )
-            init_latents = [
-                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
-                for i in range(batch_size)
-            ]
-            init_latents = torch.cat(init_latents, dim=0)
-        else:
-            init_latents = retrieve_latents(self.vae.encode(image.to(self.vae.dtype)), generator=generator)
-            
-        init_latents = self.vae.config.scaling_factor * init_latents
-        init_latents = init_latents.to(device=device, dtype=dtype)
-
-        init_latents = einops.rearrange(init_latents, "(bs views) c h w -> bs views c h w", bs=batch_size, views=init_latents.shape[0]//batch_size)
-        # latents = einops.rearrange(latents, "b c h w -> b 1 c h w")
-        # latents = torch.concat([latents, init_latents], dim=1)
-        return init_latents
-    
-    def prepare_init_latents(self, batch_size, seq_length, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, seq_length, num_channels_latents, height // self.vae_scale_factor, 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."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        return latents
-
-    @torch.no_grad()
-    def generate(
-        self,
-        prompt: Union[str, List[str]],
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        """
-        Function for image generation using the OneDiffusionPipeline.
-        """
-        return self(
-            prompt=prompt,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            height=height,
-            width=width,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-        )
-
-    @staticmethod
-    def numpy_to_pil(images):
-        """
-        Convert a numpy image or a batch of images to a PIL image.
-        """
-        if images.ndim == 3:
-            images = images[None, ...]
-        images = (images * 255).round().astype("uint8")
-        if images.shape[-1] == 1:
-            # special case for grayscale (single channel) images
-            pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
-        else:
-            pil_images = [Image.fromarray(image) for image in images]
-
-        return pil_images
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        model_path = pretrained_model_name_or_path
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", None)
-        token = kwargs.pop("token", None)
-        revision = kwargs.pop("revision", None)
-        from_flax = kwargs.pop("from_flax", False)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        custom_pipeline = kwargs.pop("custom_pipeline", None)
-        custom_revision = kwargs.pop("custom_revision", None)
-        provider = kwargs.pop("provider", None)
-        sess_options = kwargs.pop("sess_options", None)
-        device_map = kwargs.pop("device_map", None)
-        max_memory = kwargs.pop("max_memory", None)
-        offload_folder = kwargs.pop("offload_folder", None)
-        offload_state_dict = kwargs.pop("offload_state_dict", False)
-        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
-        variant = kwargs.pop("variant", None)
-        use_safetensors = kwargs.pop("use_safetensors", None)
-        use_onnx = kwargs.pop("use_onnx", None)
-        load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
-        
-        if low_cpu_mem_usage and not is_accelerate_available():
-            low_cpu_mem_usage = False
-            logger.warning(
-                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
-                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
-                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
-                " install accelerate\n```\n."
-            )
-
-        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `low_cpu_mem_usage=False`."
-            )
-
-        if device_map is not None and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `device_map=None`."
-            )
-
-        if device_map is not None and not is_accelerate_available():
-            raise NotImplementedError(
-                "Using `device_map` requires the `accelerate` library. Please install it using: `pip install accelerate`."
-            )
-
-        if device_map is not None and not isinstance(device_map, str):
-            raise ValueError("`device_map` must be a string.")
-
-        if device_map is not None and device_map not in SUPPORTED_DEVICE_MAP:
-            raise NotImplementedError(
-                f"{device_map} not supported. Supported strategies are: {', '.join(SUPPORTED_DEVICE_MAP)}"
-            )
-
-        if device_map is not None and device_map in SUPPORTED_DEVICE_MAP:
-            if is_accelerate_version("<", "0.28.0"):
-                raise NotImplementedError("Device placement requires `accelerate` version `0.28.0` or later.")
-
-        if low_cpu_mem_usage is False and device_map is not None:
-            raise ValueError(
-                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
-                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
-            )
-
-        transformer = NextDiT.from_pretrained(f"{model_path}", subfolder="transformer", torch_dtype=torch.float32, cache_dir=cache_dir)
-        vae = AutoencoderKL.from_pretrained(f"{model_path}", subfolder="vae", cache_dir=cache_dir)
-        text_encoder = T5EncoderModel.from_pretrained(f"{model_path}", subfolder="text_encoder", torch_dtype=torch.float16, cache_dir=cache_dir)
-        tokenizer = T5Tokenizer.from_pretrained(model_path, subfolder="tokenizer", cache_dir=cache_dir)
-        scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler", cache_dir=cache_dir)
-
-        pipeline = cls(
-            transformer=transformer,
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            **kwargs
-        )
-
-        return pipeline