VisualCloze

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning is an innovative in-context learning based universal image generation framework that offers key capabilities: 1. Support for various in-domain tasks 2. Generalization to unseen tasks through in-context learning 3. Unify multiple tasks into one step and generate both target image and intermediate results 4. Support reverse-engineering conditions from target images

Overview

The abstract from the paper is:

Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures. The codes, dataset, and models are available at https://visualcloze.github.io.

Inference

Model loading

VisualCloze is a two-stage cascade pipeline, containing VisualClozeGenerationPipeline and VisualClozeUpsamplingPipeline. - In VisualClozeGenerationPipeline, each image is downsampled before concatenating images into a grid layout, avoiding excessively high resolutions. VisualCloze releases two models suitable for diffusers, i.e., VisualClozePipeline-384 and VisualClozePipeline-512, which downsample images to resolutions of 384 and 512, respectively. - VisualClozeUpsamplingPipeline uses SDEdit to enable high-resolution image synthesis.

The VisualClozePipeline integrates both stages to support convenient end-to-end sampling, while also allowing users to utilize each pipeline independently as needed.

Input Specifications

Task and Content Prompts

• Task prompt: Required to describe the generation task intention
• Content prompt: Optional description or caption of the target image
• When content prompt is not needed, pass None
• For batch inference, pass List[str|None]

Image Input Format

• Format: List[List[Image|None]]
• Structure:
• All rows except the last represent in-context examples
• Last row represents the current query (target image set to None)
• For batch inference, pass List[List[List[Image|None]]]

Resolution Control

• Default behavior:
• Initial generation in the first stage: area of \({pipe.resolution}^2\)
• Upsampling in the second stage: 3x factor
• Custom resolution: Adjust using upsampling_height and upsampling_width parameters

Examples

For comprehensive examples covering a wide range of tasks, please refer to the Online Demo and GitHub Repository. Below are simple examples for three cases: mask-to-image conversion, edge detection, and subject-driven generation.

Example for mask2image

import mindspore as ms
from mindone.diffusers import VisualClozePipeline
from mindone.diffusers.utils import load_image
import numpy as np

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, mindspore_dtype=ms.bfloat16)

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = """Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape.
The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible.
Its plumage is a mix of dark brown and golden hues, with intricate feather details.
The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere.
The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field,
soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background,
tranquil, majestic, wildlife photography."""

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=1344,
    upsampling_height=768,
    upsampling_strength=0.4,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=np.random.Generator(np.random.PCG64(seed=0))
)[0][0][0]

# Save the resulting image
image_result.save("visualcloze.png")

Example for edge-detection

import mindspore as ms
from mindone.diffusers import VisualClozePipeline
from mindone.diffusers.utils import load_image
import numpy as np

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, mindspore_dtype=ms.bfloat16)

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_image.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_edge.jpg'),
    ],
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_image.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_edge.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_query_image.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = "Each row illustrates a pathway from [IMAGE1] a sharp and beautifully composed photograph to [IMAGE2] edge map with natural well-connected outlines using a clear logical task."
content_prompt = ""

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=864,
    upsampling_height=1152,
    upsampling_strength=0.4,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=np.random.Generator(np.random.PCG64(seed=0))
)[0][0][0]

# Save the resulting image
image_result.save("visualcloze.png")

Example for subject-driven generation

import mindspore as ms
from mindone.diffusers import VisualClozePipeline
from mindone.diffusers.utils import load_image
import numpy as np

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, mindspore_dtype=ms.bfloat16)

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_depth.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_image.jpg'),
    ],
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_depth.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_image.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_depth.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = """Each row describes a process that begins with [IMAGE1] an image containing the key object,
[IMAGE2] depth map revealing gray-toned spatial layers and results in
[IMAGE3] an image with artistic qualitya high-quality image with exceptional detail."""
content_prompt = """A vintage porcelain collector's item. Beneath a blossoming cherry tree in early spring,
this treasure is photographed up close, with soft pink petals drifting through the air and vibrant blossoms framing the scene."""

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=1024,
    upsampling_height=1024,
    upsampling_strength=0.2,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=np.random.Generator(np.random.PCG64(seed=0))
)[0][0][0]

# Save the resulting image
image_result.save("visualcloze.png")

Utilize each pipeline independently

import mindspore as ms
from mindone.diffusers import VisualClozeGenerationPipeline, FluxFillPipeline as VisualClozeUpsamplingPipeline
from mindone.diffusers.utils import load_image
from PIL import Image
import numpy as np

pipe = VisualClozeGenerationPipeline.from_pretrained(
    "VisualCloze/VisualClozePipeline-384", resolution=384, mindspore_dtype=ms.bfloat16
)

image_paths = [
    # in-context examples
    [
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg"
        ),
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg"
        ),
    ],
    # query with the target image
    [
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg"
        ),
        None,  # No image needed for the target image
    ],
]
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography."

# Stage 1: Generate initial image
image = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=np.random.Generator(np.random.PCG64(seed=0)),
)[0][0][0]

# Stage 2 (optional): Upsample the generated image
pipe_upsample = VisualClozeUpsamplingPipeline.from_pipe(pipe)

mask_image = Image.new("RGB", image.size, (255, 255, 255))

image = pipe_upsample(
    image=image,
    mask_image=mask_image,
    prompt=content_prompt,
    width=1344,
    height=768,
    strength=0.4,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=np.random.Generator(np.random.PCG64(seed=0)),
)[0][0]

image.save("visualcloze.png")

mindone.diffusers.VisualClozePipeline

Bases: DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin

The VisualCloze pipeline for image generation with visual context. Reference: https://github.com/lzyhha/VisualCloze/tree/main. This pipeline is designed to generate images based on visual in-context examples.

PARAMETER	DESCRIPTION
transformer	Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. TYPE: [`FluxTransformer2DModel`]
scheduler	A scheduler to be used in combination with transformer to denoise the encoded image latents. TYPE: [`FlowMatchEulerDiscreteScheduler`]
vae	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	CLIP, specifically the clip-vit-large-patch14 variant. TYPE: [`CLIPTextModel`]
text_encoder_2	T5, specifically the google/t5-v1_1-xxl variant. TYPE: [`T5EncoderModel`]
tokenizer	Tokenizer of class CLIPTokenizer. TYPE: `CLIPTokenizer`
tokenizer_2	Second Tokenizer of class T5TokenizerFast. TYPE: `T5TokenizerFast`
resolution	The resolution of each image when concatenating images from the query and in-context examples. TYPE: `int`, *optional*, defaults to 384 DEFAULT: 384

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_combined.py

class VisualClozePipeline(
    DiffusionPipeline,
    FluxLoraLoaderMixin,
    FromSingleFileMixin,
    TextualInversionLoaderMixin,
):
    r"""
    The VisualCloze pipeline for image generation with visual context. Reference:
    https://github.com/lzyhha/VisualCloze/tree/main. This pipeline is designed to generate images based on visual
    in-context examples.

    Args:
        transformer ([`FluxTransformer2DModel`]):
            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
            A scheduler to be used in combination with `transformer` 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 ([`CLIPTextModel`]):
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        text_encoder_2 ([`T5EncoderModel`]):
            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
        tokenizer_2 (`T5TokenizerFast`):
            Second Tokenizer of class
            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
        resolution (`int`, *optional*, defaults to 384):
            The resolution of each image when concatenating images from the query and in-context examples.
    """

    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
    _optional_components = []
    _callback_tensor_inputs = ["latents", "prompt_embeds"]

    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        text_encoder_2: T5EncoderModel,
        tokenizer_2: T5TokenizerFast,
        transformer: FluxTransformer2DModel,
        resolution: int = 384,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            transformer=transformer,
            scheduler=scheduler,
        )

        self.generation_pipe = VisualClozeGenerationPipeline(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            transformer=transformer,
            scheduler=scheduler,
            resolution=resolution,
        )
        self.upsampling_pipe = VisualClozeUpsamplingPipeline(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            transformer=transformer,
            scheduler=scheduler,
        )

    def check_inputs(
        self,
        image,
        task_prompt,
        content_prompt,
        upsampling_height,
        upsampling_width,
        strength,
        prompt_embeds=None,
        pooled_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        max_sequence_length=None,
    ):
        if strength < 0 or strength > 1:
            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

        if upsampling_height is not None and upsampling_height % (self.vae_scale_factor * 2) != 0:
            logger.warning(
                f"`upsampling_height`has to be divisible by {self.vae_scale_factor * 2} but are {upsampling_height}. Dimensions will be resized accordingly"
            )
        if upsampling_width is not None and upsampling_width % (self.vae_scale_factor * 2) != 0:
            logger.warning(
                f"`upsampling_width` have to be divisible by {self.vae_scale_factor * 2} but are {upsampling_width}. Dimensions will be resized accordingly"
            )

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"  # noqa: E501
            )

        # Validate prompt inputs
        if (task_prompt is not None or content_prompt is not None) and prompt_embeds is not None:
            raise ValueError("Cannot provide both text `task_prompt` + `content_prompt` and `prompt_embeds`. ")

        if task_prompt is None and content_prompt is None and prompt_embeds is None:
            raise ValueError("Must provide either `task_prompt` + `content_prompt` or pre-computed `prompt_embeds`. ")

        # Validate prompt types and consistency
        if task_prompt is None:
            raise ValueError("`task_prompt` is missing.")

        if task_prompt is not None and not isinstance(task_prompt, (str, list)):
            raise ValueError(f"`task_prompt` must be str or list, got {type(task_prompt)}")

        if content_prompt is not None and not isinstance(content_prompt, (str, list)):
            raise ValueError(f"`content_prompt` must be str or list, got {type(content_prompt)}")

        if isinstance(task_prompt, list) or isinstance(content_prompt, list):
            if not isinstance(task_prompt, list) or not isinstance(content_prompt, list):
                raise ValueError(
                    f"`task_prompt` and `content_prompt` must both be lists, or both be of type str or None, "
                    f"got {type(task_prompt)} and {type(content_prompt)}"
                )
            if len(content_prompt) != len(task_prompt):
                raise ValueError("`task_prompt` and `content_prompt` must have the same length whe they are lists.")

            for sample in image:
                if not isinstance(sample, list) or not isinstance(sample[0], list):
                    raise ValueError("Each sample in the batch must have a 2D list of images.")
                if len({len(row) for row in sample}) != 1:
                    raise ValueError("Each in-context example and query should contain the same number of images.")
                if not any(img is None for img in sample[-1]):
                    raise ValueError("There are no targets in the query, which should be represented as None.")
                for row in sample[:-1]:
                    if any(img is None for img in row):
                        raise ValueError("Images are missing in in-context examples.")

        # Validate embeddings
        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."  # noqa: E501
            )

        # Validate sequence length
        if max_sequence_length is not None and max_sequence_length > 512:
            raise ValueError(f"max_sequence_length cannot exceed 512, got {max_sequence_length}")

    def __call__(
        self,
        task_prompt: Union[str, List[str]] = None,
        content_prompt: Union[str, List[str]] = None,
        image: Optional[ms.Tensor] = None,
        upsampling_height: Optional[int] = None,
        upsampling_width: Optional[int] = None,
        num_inference_steps: int = 50,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 30.0,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        pooled_prompt_embeds: Optional[ms.Tensor] = 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 = 512,
        upsampling_strength: float = 1.0,
    ):
        r"""
        Function invoked when calling the VisualCloze pipeline for generation.

        Args:
            task_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the task intention.
            content_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the content or caption of the target image to be generated.
            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
            upsampling_height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
                default, the image is upsampled by a factor of three, and the base resolution is determined by the
                resolution parameter of the pipeline. When only one of `upsampling_height` or `upsampling_width` is
                specified, the other will be automatically set based on the aspect ratio.
            upsampling_width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
                default, the image is upsampled by a factor of three, and the base resolution is determined by the
                resolution parameter of the pipeline. When only one of `upsampling_height` or `upsampling_width` is
                specified, the other will be automatically set based on the aspect ratio.
            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.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            guidance_scale (`float`, *optional*, defaults to 30.0):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            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.flux.FluxPipelineOutput`] instead of a plain tuple.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
            upsampling_strength (`float`, *optional*, defaults to 1.0):
                Indicates extent to transform the reference `image` when upsampling the results. Must be between 0 and
                1. The generated image is used as a starting point and more noise is added the higher the
                `upsampling_strength`. The number of denoising steps depends on the amount of noise initially added.
                When `upsampling_strength` is 1, added noise is maximum and the denoising process runs for the full
                number of iterations specified in `num_inference_steps`. A value of 0 skips the upsampling step and
                output the results at the resolution of `self.resolution`.

        Examples:

        Returns:
            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
            images.
        """

        generation_output = self.generation_pipe(
            task_prompt=task_prompt,
            content_prompt=content_prompt,
            image=image,
            num_inference_steps=num_inference_steps,
            sigmas=sigmas,
            guidance_scale=guidance_scale,
            num_images_per_prompt=num_images_per_prompt,
            generator=generator,
            latents=latents,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            joint_attention_kwargs=joint_attention_kwargs,
            callback_on_step_end=callback_on_step_end,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
            max_sequence_length=max_sequence_length,
            output_type=output_type if upsampling_strength == 0 else "pil",
        )
        if upsampling_strength == 0:
            if not return_dict:
                return (generation_output,)

            return FluxPipelineOutput(images=generation_output)

        # Upsampling the generated images
        # 1. Prepare the input images and prompts
        if not isinstance(content_prompt, (list)):
            content_prompt = [content_prompt]
        n_target_per_sample = []
        upsampling_image = []
        upsampling_mask = []
        upsampling_prompt = []
        upsampling_generator = generator if isinstance(generator, (np.random.Generator,)) else []
        for i in range(len(generation_output[0])):
            n_target_per_sample.append(len(generation_output[0][i]))
            for image in generation_output[0][i]:
                upsampling_image.append(image)
                upsampling_mask.append(Image.new("RGB", image.size, (255, 255, 255)))
                upsampling_prompt.append(
                    content_prompt[i % len(content_prompt)] if content_prompt[i % len(content_prompt)] else ""
                )
                if not isinstance(generator, (np.random.Generator,)):
                    upsampling_generator.append(generator[i % len(content_prompt)])

        # 2. Apply the denosing loop
        upsampling_output = self.upsampling_pipe(
            prompt=upsampling_prompt,
            image=upsampling_image,
            mask_image=upsampling_mask,
            height=upsampling_height,
            width=upsampling_width,
            strength=upsampling_strength,
            num_inference_steps=num_inference_steps,
            sigmas=sigmas,
            guidance_scale=guidance_scale,
            generator=upsampling_generator,
            output_type=output_type,
            joint_attention_kwargs=joint_attention_kwargs,
            callback_on_step_end=callback_on_step_end,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
            max_sequence_length=max_sequence_length,
        )
        image = upsampling_output[0]

        output = []
        if output_type == "pil":
            # Each sample in the batch may have multiple output images. When returning as PIL images,
            # these images cannot be concatenated. Therefore, for each sample,
            # a list is used to represent all the output images.
            output = []
            start = 0
            for n in n_target_per_sample:
                output.append(image[start : start + n])
                start += n
        else:
            output = image

        if not return_dict:
            return (output,)

        return FluxPipelineOutput(images=output)

mindone.diffusers.VisualClozePipeline.__call__(task_prompt=None, content_prompt=None, image=None, upsampling_height=None, upsampling_width=None, num_inference_steps=50, sigmas=None, guidance_scale=30.0, num_images_per_prompt=1, generator=None, latents=None, prompt_embeds=None, pooled_prompt_embeds=None, output_type='pil', return_dict=True, joint_attention_kwargs=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], max_sequence_length=512, upsampling_strength=1.0)

Function invoked when calling the VisualCloze pipeline for generation.

PARAMETER	DESCRIPTION
task_prompt	The prompt or prompts to define the task intention. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
content_prompt	The prompt or prompts to define the content or caption of the target image to be generated. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
image	Image, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between [0, 1] If it's a tensor or a list or tensors, the expected shape should be (B, C, H, W) or (C, H, W). If it is a numpy array or a list of arrays, the expected shape should be (B, H, W, C) or (H, W, C). TYPE: `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]` DEFAULT: None
upsampling_height	The height in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By default, the image is upsampled by a factor of three, and the base resolution is determined by the resolution parameter of the pipeline. When only one of upsampling_height or upsampling_width is specified, the other will be automatically set based on the aspect ratio. TYPE: `int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor DEFAULT: None
upsampling_width	The width in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By default, the image is upsampled by a factor of three, and the base resolution is determined by the resolution parameter of the pipeline. When only one of upsampling_height or upsampling_width is specified, the other will be automatically set based on the aspect ratio. TYPE: `int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor DEFAULT: None
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to 50 DEFAULT: 50
sigmas	Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. TYPE: `List[float]`, *optional* DEFAULT: None
guidance_scale	Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. 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. TYPE: `float`, *optional*, defaults to 30.0 DEFAULT: 30.0
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
generator	One or a list of torch generator(s) to make generation deterministic. TYPE: `torch.Generator` or `List[torch.Generator]`, *optional* DEFAULT: None
latents	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. TYPE: `torch.FloatTensor`, *optional* DEFAULT: None
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument. TYPE: `torch.FloatTensor`, *optional* DEFAULT: None
pooled_prompt_embeds	Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument. TYPE: `torch.FloatTensor`, *optional* DEFAULT: None
output_type	The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.flux.FluxPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
joint_attention_kwargs	A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. TYPE: `dict`, *optional* DEFAULT: None
callback_on_step_end	A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs. TYPE: `Callable`, *optional* DEFAULT: None
callback_on_step_end_tensor_inputs	The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class. TYPE: `List`, *optional* DEFAULT: ['latents']
max_sequence_length	Maximum sequence length to use with the prompt. TYPE: `int` defaults to 512 DEFAULT: 512
upsampling_strength	Indicates extent to transform the reference image when upsampling the results. Must be between 0 and 1. The generated image is used as a starting point and more noise is added the higher the upsampling_strength. The number of denoising steps depends on the amount of noise initially added. When upsampling_strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 0 skips the upsampling step and output the results at the resolution of self.resolution. TYPE: `float`, *optional*, defaults to 1.0 DEFAULT: 1.0

RETURNS	DESCRIPTION
	[~pipelines.flux.FluxPipelineOutput] or tuple: [~pipelines.flux.FluxPipelineOutput] if return_dict
	is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated
	images.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_combined.py

def __call__(
    self,
    task_prompt: Union[str, List[str]] = None,
    content_prompt: Union[str, List[str]] = None,
    image: Optional[ms.Tensor] = None,
    upsampling_height: Optional[int] = None,
    upsampling_width: Optional[int] = None,
    num_inference_steps: int = 50,
    sigmas: Optional[List[float]] = None,
    guidance_scale: float = 30.0,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    pooled_prompt_embeds: Optional[ms.Tensor] = 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 = 512,
    upsampling_strength: float = 1.0,
):
    r"""
    Function invoked when calling the VisualCloze pipeline for generation.

    Args:
        task_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the task intention.
        content_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the content or caption of the target image to be generated.
        image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
            `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
            numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
            or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
            list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
        upsampling_height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
            The height in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
            default, the image is upsampled by a factor of three, and the base resolution is determined by the
            resolution parameter of the pipeline. When only one of `upsampling_height` or `upsampling_width` is
            specified, the other will be automatically set based on the aspect ratio.
        upsampling_width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
            The width in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
            default, the image is upsampled by a factor of three, and the base resolution is determined by the
            resolution parameter of the pipeline. When only one of `upsampling_height` or `upsampling_width` is
            specified, the other will be automatically set based on the aspect ratio.
        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.
        sigmas (`List[float]`, *optional*):
            Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
            their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
            will be used.
        guidance_scale (`float`, *optional*, defaults to 30.0):
            Guidance scale as defined in [Classifier-Free Diffusion
            Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
            of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        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`.
        prompt_embeds (`torch.FloatTensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
            provided, text embeddings will be generated from `prompt` input argument.
        pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
            Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
            If not provided, pooled text embeddings will be generated from `prompt` input argument.
        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.flux.FluxPipelineOutput`] instead of a plain tuple.
        joint_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        callback_on_step_end (`Callable`, *optional*):
            A function that calls at the end of each denoising steps during the inference. The function is called
            with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
            callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
            `callback_on_step_end_tensor_inputs`.
        callback_on_step_end_tensor_inputs (`List`, *optional*):
            The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
            will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
            `._callback_tensor_inputs` attribute of your pipeline class.
        max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
        upsampling_strength (`float`, *optional*, defaults to 1.0):
            Indicates extent to transform the reference `image` when upsampling the results. Must be between 0 and
            1. The generated image is used as a starting point and more noise is added the higher the
            `upsampling_strength`. The number of denoising steps depends on the amount of noise initially added.
            When `upsampling_strength` is 1, added noise is maximum and the denoising process runs for the full
            number of iterations specified in `num_inference_steps`. A value of 0 skips the upsampling step and
            output the results at the resolution of `self.resolution`.

    Examples:

    Returns:
        [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
        is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
        images.
    """

    generation_output = self.generation_pipe(
        task_prompt=task_prompt,
        content_prompt=content_prompt,
        image=image,
        num_inference_steps=num_inference_steps,
        sigmas=sigmas,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        generator=generator,
        latents=latents,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        joint_attention_kwargs=joint_attention_kwargs,
        callback_on_step_end=callback_on_step_end,
        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        max_sequence_length=max_sequence_length,
        output_type=output_type if upsampling_strength == 0 else "pil",
    )
    if upsampling_strength == 0:
        if not return_dict:
            return (generation_output,)

        return FluxPipelineOutput(images=generation_output)

    # Upsampling the generated images
    # 1. Prepare the input images and prompts
    if not isinstance(content_prompt, (list)):
        content_prompt = [content_prompt]
    n_target_per_sample = []
    upsampling_image = []
    upsampling_mask = []
    upsampling_prompt = []
    upsampling_generator = generator if isinstance(generator, (np.random.Generator,)) else []
    for i in range(len(generation_output[0])):
        n_target_per_sample.append(len(generation_output[0][i]))
        for image in generation_output[0][i]:
            upsampling_image.append(image)
            upsampling_mask.append(Image.new("RGB", image.size, (255, 255, 255)))
            upsampling_prompt.append(
                content_prompt[i % len(content_prompt)] if content_prompt[i % len(content_prompt)] else ""
            )
            if not isinstance(generator, (np.random.Generator,)):
                upsampling_generator.append(generator[i % len(content_prompt)])

    # 2. Apply the denosing loop
    upsampling_output = self.upsampling_pipe(
        prompt=upsampling_prompt,
        image=upsampling_image,
        mask_image=upsampling_mask,
        height=upsampling_height,
        width=upsampling_width,
        strength=upsampling_strength,
        num_inference_steps=num_inference_steps,
        sigmas=sigmas,
        guidance_scale=guidance_scale,
        generator=upsampling_generator,
        output_type=output_type,
        joint_attention_kwargs=joint_attention_kwargs,
        callback_on_step_end=callback_on_step_end,
        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        max_sequence_length=max_sequence_length,
    )
    image = upsampling_output[0]

    output = []
    if output_type == "pil":
        # Each sample in the batch may have multiple output images. When returning as PIL images,
        # these images cannot be concatenated. Therefore, for each sample,
        # a list is used to represent all the output images.
        output = []
        start = 0
        for n in n_target_per_sample:
            output.append(image[start : start + n])
            start += n
    else:
        output = image

    if not return_dict:
        return (output,)

    return FluxPipelineOutput(images=output)

mindone.diffusers.VisualClozeGenerationPipeline

Bases: DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin

The VisualCloze pipeline for image generation with visual context. Reference: https://github.com/lzyhha/VisualCloze/tree/main This pipeline is designed to generate images based on visual in-context examples.

PARAMETER	DESCRIPTION
transformer	Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. TYPE: [`FluxTransformer2DModel`]
scheduler	A scheduler to be used in combination with transformer to denoise the encoded image latents. TYPE: [`FlowMatchEulerDiscreteScheduler`]
vae	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	CLIP, specifically the clip-vit-large-patch14 variant. TYPE: [`CLIPTextModel`]
text_encoder_2	T5, specifically the google/t5-v1_1-xxl variant. TYPE: [`T5EncoderModel`]
tokenizer	Tokenizer of class CLIPTokenizer. TYPE: `CLIPTokenizer`
tokenizer_2	Second Tokenizer of class T5TokenizerFast. TYPE: `T5TokenizerFast`
resolution	The resolution of each image when concatenating images from the query and in-context examples. TYPE: `int`, *optional*, defaults to 384 DEFAULT: 384

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

class VisualClozeGenerationPipeline(
    DiffusionPipeline,
    FluxLoraLoaderMixin,
    FromSingleFileMixin,
    TextualInversionLoaderMixin,
):
    r"""
    The VisualCloze pipeline for image generation with visual context. Reference:
    https://github.com/lzyhha/VisualCloze/tree/main This pipeline is designed to generate images based on visual
    in-context examples.

    Args:
        transformer ([`FluxTransformer2DModel`]):
            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
            A scheduler to be used in combination with `transformer` 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 ([`CLIPTextModel`]):
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        text_encoder_2 ([`T5EncoderModel`]):
            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
        tokenizer_2 (`T5TokenizerFast`):
            Second Tokenizer of class
            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
        resolution (`int`, *optional*, defaults to 384):
            The resolution of each image when concatenating images from the query and in-context examples.
    """

    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
    _optional_components = []
    _callback_tensor_inputs = ["latents", "prompt_embeds"]

    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        text_encoder_2: T5EncoderModel,
        tokenizer_2: T5TokenizerFast,
        transformer: FluxTransformer2DModel,
        resolution: int = 384,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            transformer=transformer,
            scheduler=scheduler,
        )
        self.resolution = resolution
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        # Flux latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible
        # by the patch size. So the vae scale factor is multiplied by the patch size to account for this
        self.latent_channels = self.vae.config.latent_channels if getattr(self, "vae", None) else 16
        self.image_processor = VisualClozeProcessor(
            vae_scale_factor=self.vae_scale_factor * 2, vae_latent_channels=self.latent_channels, resolution=resolution
        )
        self.tokenizer_max_length = (
            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
        )
        self.default_sample_size = 128

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_t5_prompt_embeds
    def _get_t5_prompt_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        num_images_per_prompt: int = 1,
        max_sequence_length: int = 512,
        dtype: Optional[ms.Type] = None,
    ):
        dtype = dtype or self.text_encoder.dtype

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

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

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

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

        with pynative_context():
            prompt_embeds = self.text_encoder_2(ms.Tensor.from_numpy(text_input_ids), output_hidden_states=False)[0]

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

        _, seq_len, _ = prompt_embeds.shape

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

        return prompt_embeds

    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_clip_prompt_embeds
    def _get_clip_prompt_embeds(
        self,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int = 1,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

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

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

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

        # Use pooled output of CLIPTextModel
        prompt_embeds = prompt_embeds[1]
        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype)

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

        return prompt_embeds

    # Modified from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.encode_prompt
    def encode_prompt(
        self,
        layout_prompt: Union[str, List[str]],
        task_prompt: Union[str, List[str]],
        content_prompt: Union[str, List[str]],
        num_images_per_prompt: int = 1,
        prompt_embeds: Optional[ms.Tensor] = None,
        pooled_prompt_embeds: Optional[ms.Tensor] = None,
        max_sequence_length: int = 512,
        lora_scale: Optional[float] = None,
    ):
        r"""

        Args:
            layout_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the number of in-context examples and the number of images involved in
                the task.
            task_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the task intention.
            content_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the content or caption of the target image to be generated.
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            pooled_prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if self.text_encoder is not None:
                scale_lora_layers(self.text_encoder, lora_scale)
            if self.text_encoder_2 is not None:
                scale_lora_layers(self.text_encoder_2, lora_scale)

        if isinstance(layout_prompt, str):
            layout_prompt = [layout_prompt]
            task_prompt = [task_prompt]
            content_prompt = [content_prompt]

        def _preprocess(prompt, content=False):
            if prompt is not None:
                return f"The last image of the last row depicts: {prompt}" if content else prompt
            else:
                return ""

        prompt = [
            f"{_preprocess(layout_prompt[i])} {_preprocess(task_prompt[i])} {_preprocess(content_prompt[i], content=True)}".strip()
            for i in range(len(layout_prompt))
        ]
        pooled_prompt_embeds = self._get_clip_prompt_embeds(
            prompt=prompt,
            num_images_per_prompt=num_images_per_prompt,
        )
        prompt_embeds = self._get_t5_prompt_embeds(
            prompt=prompt,
            num_images_per_prompt=num_images_per_prompt,
            max_sequence_length=max_sequence_length,
        )

        if self.text_encoder is not None:
            if isinstance(self, FluxLoraLoaderMixin):
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        if self.text_encoder_2 is not None:
            if isinstance(self, FluxLoraLoaderMixin):
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder_2, lora_scale)

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

        return prompt_embeds, pooled_prompt_embeds, text_ids

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_inpaint.StableDiffusion3InpaintPipeline._encode_vae_image
    def _encode_vae_image(self, image: ms.Tensor, generator: np.random.Generator):
        if isinstance(generator, list):
            image_latents = [
                retrieve_latents(self.vae, self.vae.encode(image[i : i + 1])[0], generator=generator[i])
                for i in range(image.shape[0])
            ]
            image_latents = mint.cat(image_latents, dim=0)
        else:
            image_latents = retrieve_latents(self.vae, self.vae.encode(image)[0], generator=generator)

        image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor

        return image_latents

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps
    def get_timesteps(self, num_inference_steps, strength):
        # get the original timestep using init_timestep
        init_timestep = min(num_inference_steps * strength, num_inference_steps)

        t_start = int(max(num_inference_steps - init_timestep, 0))
        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
        if hasattr(self.scheduler, "set_begin_index"):
            self.scheduler.set_begin_index(t_start * self.scheduler.order)

        return timesteps, num_inference_steps - t_start

    def check_inputs(
        self,
        image,
        task_prompt,
        content_prompt,
        prompt_embeds=None,
        pooled_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        max_sequence_length=None,
    ):
        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"  # noqa: E501
            )

        # Validate prompt inputs
        if (task_prompt is not None or content_prompt is not None) and prompt_embeds is not None:
            raise ValueError("Cannot provide both text `task_prompt` + `content_prompt` and `prompt_embeds`. ")

        if task_prompt is None and content_prompt is None and prompt_embeds is None:
            raise ValueError("Must provide either `task_prompt` + `content_prompt` or pre-computed `prompt_embeds`. ")

        # Validate prompt types and consistency
        if task_prompt is None:
            raise ValueError("`task_prompt` is missing.")

        if task_prompt is not None and not isinstance(task_prompt, (str, list)):
            raise ValueError(f"`task_prompt` must be str or list, got {type(task_prompt)}")

        if content_prompt is not None and not isinstance(content_prompt, (str, list)):
            raise ValueError(f"`content_prompt` must be str or list, got {type(content_prompt)}")

        if isinstance(task_prompt, list) or isinstance(content_prompt, list):
            if not isinstance(task_prompt, list) or not isinstance(content_prompt, list):
                raise ValueError(
                    f"`task_prompt` and `content_prompt` must both be lists, or both be of type str or None, "
                    f"got {type(task_prompt)} and {type(content_prompt)}"
                )
            if len(content_prompt) != len(task_prompt):
                raise ValueError("`task_prompt` and `content_prompt` must have the same length whe they are lists.")

            for sample in image:
                if not isinstance(sample, list) or not isinstance(sample[0], list):
                    raise ValueError("Each sample in the batch must have a 2D list of images.")
                if len({len(row) for row in sample}) != 1:
                    raise ValueError("Each in-context example and query should contain the same number of images.")
                if not any(img is None for img in sample[-1]):
                    raise ValueError("There are no targets in the query, which should be represented as None.")
                for row in sample[:-1]:
                    if any(img is None for img in row):
                        raise ValueError("Images are missing in in-context examples.")

        # Validate embeddings
        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."  # noqa: E501
            )

        # Validate sequence length
        if max_sequence_length is not None and max_sequence_length > 512:
            raise ValueError(f"max_sequence_length cannot exceed 512, got {max_sequence_length}")

    @staticmethod
    def _prepare_latent_image_ids(image, vae_scale_factor, dtype):
        latent_image_ids = []

        for idx, img in enumerate(image, start=1):
            img = img.squeeze(0)
            channels, height, width = img.shape

            num_patches_h = height // vae_scale_factor // 2
            num_patches_w = width // vae_scale_factor // 2

            patch_ids = mint.zeros((num_patches_h, num_patches_w, 3), dtype=dtype)
            patch_ids[..., 0] = idx
            patch_ids[..., 1] = mint.arange(num_patches_h, dtype=dtype)[:, None]
            patch_ids[..., 2] = mint.arange(num_patches_w, dtype=dtype)[None, :]

            patch_ids = patch_ids.reshape(-1, 3)
            latent_image_ids.append(patch_ids)

        return mint.cat(latent_image_ids, dim=0)

    @staticmethod
    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents
    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, sizes, vae_scale_factor):
        batch_size, num_patches, channels = latents.shape

        start = 0
        unpacked_latents = []
        for i in range(len(sizes)):
            cur_size = sizes[i]
            height = cur_size[0][0] // vae_scale_factor
            width = sum([size[1] for size in cur_size]) // vae_scale_factor

            end = start + (height * width) // 4

            cur_latents = latents[:, start:end]
            cur_latents = cur_latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
            cur_latents = cur_latents.permute(0, 3, 1, 4, 2, 5)
            cur_latents = cur_latents.reshape(batch_size, channels // (2 * 2), height, width)

            unpacked_latents.append(cur_latents)

            start = end

        return unpacked_latents

    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, image, mask, gen, vae_scale_factor, dtype):
        """Helper function to prepare latents for a single batch."""
        # Concatenate images and masks along width dimension
        image = [mint.cat(img, dim=3).to(dtype=dtype) for img in image]
        mask = [mint.cat(m, dim=3).to(dtype=dtype) for m in mask]

        # Generate latent image IDs
        latent_image_ids = self._prepare_latent_image_ids(image, vae_scale_factor, dtype)

        # For initial encoding, use actual images
        image_latent = [self._encode_vae_image(img, gen) for img in image]
        masked_image_latent = [img.clone() for img in image_latent]

        for i in range(len(image_latent)):
            # Rearrange latents and masks for patch processing
            num_channels_latents, height, width = image_latent[i].shape[1:]
            image_latent[i] = self._pack_latents(image_latent[i], 1, num_channels_latents, height, width)
            masked_image_latent[i] = self._pack_latents(masked_image_latent[i], 1, num_channels_latents, height, width)

            # Rearrange masks for patch processing
            num_channels_latents, height, width = mask[i].shape[1:]
            mask[i] = mask[i].view(
                1,
                num_channels_latents,
                height // vae_scale_factor,
                vae_scale_factor,
                width // vae_scale_factor,
                vae_scale_factor,
            )
            mask[i] = mask[i].permute(0, 1, 3, 5, 2, 4)
            mask[i] = mask[i].reshape(
                1,
                num_channels_latents * (vae_scale_factor**2),
                height // vae_scale_factor,
                width // vae_scale_factor,
            )
            mask[i] = self._pack_latents(
                mask[i],
                1,
                num_channels_latents * (vae_scale_factor**2),
                height // vae_scale_factor,
                width // vae_scale_factor,
            )

        # Concatenate along batch dimension
        image_latent = mint.cat(image_latent, dim=1)
        masked_image_latent = mint.cat(masked_image_latent, dim=1)
        mask = mint.cat(mask, dim=1)

        return image_latent, masked_image_latent, mask, latent_image_ids

    def prepare_latents(
        self,
        input_image,
        input_mask,
        timestep,
        batch_size,
        dtype,
        generator,
        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."
            )

        # Process each batch
        masked_image_latents = []
        image_latents = []
        masks = []
        latent_image_ids = []

        for i in range(len(input_image)):
            _image_latent, _masked_image_latent, _mask, _latent_image_ids = self._prepare_latents(
                input_image[i],
                input_mask[i],
                generator if isinstance(generator, np.random.Generator) else generator[i],
                vae_scale_factor,
                dtype,
            )
            masked_image_latents.append(_masked_image_latent)
            image_latents.append(_image_latent)
            masks.append(_mask)
            latent_image_ids.append(_latent_image_ids)

        # Concatenate all batches
        masked_image_latents = mint.cat(masked_image_latents, dim=0)
        image_latents = mint.cat(image_latents, dim=0)
        masks = mint.cat(masks, dim=0)

        # Handle batch size expansion
        if batch_size > masked_image_latents.shape[0]:
            if batch_size % masked_image_latents.shape[0] == 0:
                # Expand batches by repeating
                additional_image_per_prompt = batch_size // masked_image_latents.shape[0]
                masked_image_latents = mint.cat([masked_image_latents] * additional_image_per_prompt, dim=0)
                image_latents = mint.cat([image_latents] * additional_image_per_prompt, dim=0)
                masks = mint.cat([masks] * additional_image_per_prompt, dim=0)
            else:
                raise ValueError(
                    f"Cannot expand batch size from {masked_image_latents.shape[0]} to {batch_size}. "
                    "Batch sizes must be multiples of each other."
                )

        # Add noise to latents
        noises = randn_tensor(image_latents.shape, generator=generator, dtype=dtype)
        latents = self.scheduler.scale_noise(image_latents, timestep, noises).to(dtype=dtype)

        # Combine masked latents with masks
        masked_image_latents = mint.cat((masked_image_latents, masks), dim=-1).to(dtype=dtype)

        return latents, masked_image_latents, latent_image_ids[0]

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

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

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

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

    def __call__(
        self,
        task_prompt: Union[str, List[str]] = None,
        content_prompt: Union[str, List[str]] = None,
        image: Optional[ms.Tensor] = None,
        num_inference_steps: int = 50,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 30.0,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        pooled_prompt_embeds: Optional[ms.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        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 = 512,
    ):
        r"""
        Function invoked when calling the VisualCloze pipeline for generation.

        Args:
            task_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the task intention.
            content_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to define the content or caption of the target image to be generated.
            image (`ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
                numpy array and mindspore tensor, the expected value range is between `[0, 1]` If it's a tensor or a
                list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
            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.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            guidance_scale (`float`, *optional*, defaults to 30.0):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
                One or a list of [numpy generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
                to make generation deterministic.
            latents (`ms.Tensor`, *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`.
            prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            pooled_prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            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 `False`):
                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.

        Examples:

        Returns:
            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
            images.
        """

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            image,
            task_prompt,
            content_prompt,
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
            max_sequence_length=max_sequence_length,
        )

        self._guidance_scale = guidance_scale
        self._joint_attention_kwargs = joint_attention_kwargs
        self._interrupt = False

        processor_output = self.image_processor.preprocess(
            task_prompt, content_prompt, image, vae_scale_factor=self.vae_scale_factor
        )

        # 2. Define call parameters
        if processor_output["task_prompt"] is not None and isinstance(processor_output["task_prompt"], str):
            batch_size = 1
        elif processor_output["task_prompt"] is not None and isinstance(processor_output["task_prompt"], list):
            batch_size = len(processor_output["task_prompt"])

        # 3. Prepare prompt embeddings
        lora_scale = self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
        prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
            layout_prompt=processor_output["layout_prompt"],
            task_prompt=processor_output["task_prompt"],
            content_prompt=processor_output["content_prompt"],
            prompt_embeds=prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            num_images_per_prompt=num_images_per_prompt,
            max_sequence_length=max_sequence_length,
            lora_scale=lora_scale,
        )

        # 4. Prepare timesteps
        # Calculate sequence length and shift factor
        image_seq_len = sum(
            (size[0] // self.vae_scale_factor // 2) * (size[1] // self.vae_scale_factor // 2)
            for sample in processor_output["image_size"][0]
            for size in sample
        )

        # Calculate noise schedule parameters
        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),
        )

        # Get timesteps
        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            sigmas=sigmas,
            mu=mu,
        )
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1.0)

        # 5. Prepare latent variables
        latent_timestep = timesteps[:1].tile((batch_size * num_images_per_prompt,))
        latents, masked_image_latents, latent_image_ids = self.prepare_latents(
            processor_output["init_image"],
            processor_output["mask"],
            latent_timestep,
            batch_size * num_images_per_prompt,
            prompt_embeds.dtype,
            generator,
            vae_scale_factor=self.vae_scale_factor,
        )

        # Calculate warmup steps
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        self._num_timesteps = len(timesteps)

        # Prepare guidance
        if self.transformer.config.guidance_embeds:
            guidance = mint.full([1], guidance_scale, dtype=ms.float32)
            guidance = guidance.broadcast_to((latents.shape[0],))
        else:
            guidance = None

        # 6. Denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                # Broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.broadcast_to((latents.shape[0],)).to(latents.dtype)
                latent_model_input = mint.cat((latents, masked_image_latents), dim=2)

                noise_pred = self.transformer(
                    hidden_states=latent_model_input,
                    timestep=timestep / 1000,
                    guidance=guidance,
                    pooled_projections=pooled_prompt_embeds,
                    encoder_hidden_states=prompt_embeds,
                    txt_ids=text_ids,
                    img_ids=latent_image_ids,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    return_dict=False,
                )[0]

                # 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 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)

                # 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()

        # 7. Post-process the image
        # Crop the target image
        # Since the generated image is a concatenation of the conditional and target regions,
        # we need to extract only the target regions based on their positions
        image = []
        if output_type == "latent":
            image = latents
        else:
            for b in range(len(latents)):
                cur_image_size = processor_output["image_size"][b % batch_size]
                cur_target_position = processor_output["target_position"][b % batch_size]
                cur_latent = self._unpack_latents(latents[b].unsqueeze(0), cur_image_size, self.vae_scale_factor)[-1]
                cur_latent = (cur_latent / self.vae.config.scaling_factor) + self.vae.config.shift_factor
                cur_image = self.vae.decode(cur_latent, return_dict=False)[0]
                cur_image = self.image_processor.postprocess(cur_image, output_type=output_type)[0]

                start = 0
                cropped = []
                for i, size in enumerate(cur_image_size[-1]):
                    if cur_target_position[i]:
                        if output_type == "pil":
                            cropped.append(cur_image.crop((start, 0, start + size[1], size[0])))
                        else:
                            cropped.append(cur_image[0 : size[0], start : start + size[1]])
                    start += size[1]
                image.append(cropped)
            if output_type != "pil":
                image = np.concatenate([arr[None] for sub_image in image for arr in sub_image], axis=0)

        if not return_dict:
            return (image,)

        return FluxPipelineOutput(images=image)

mindone.diffusers.VisualClozeGenerationPipeline.__call__(task_prompt=None, content_prompt=None, image=None, num_inference_steps=50, sigmas=None, guidance_scale=30.0, num_images_per_prompt=1, generator=None, latents=None, prompt_embeds=None, pooled_prompt_embeds=None, output_type='pil', return_dict=False, joint_attention_kwargs=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], max_sequence_length=512)

Function invoked when calling the VisualCloze pipeline for generation.

PARAMETER	DESCRIPTION
task_prompt	The prompt or prompts to define the task intention. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
content_prompt	The prompt or prompts to define the content or caption of the target image to be generated. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
image	Image, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and mindspore tensor, the expected value range is between [0, 1] If it's a tensor or a list or tensors, the expected shape should be (B, C, H, W) or (C, H, W). If it is a numpy array or a list of arrays, the expected shape should be (B, H, W, C) or (H, W, C). TYPE: `ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]` DEFAULT: None
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to 50 DEFAULT: 50
sigmas	Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. TYPE: `List[float]`, *optional* DEFAULT: None
guidance_scale	Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. 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. TYPE: `float`, *optional*, defaults to 30.0 DEFAULT: 30.0
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
generator	One or a list of numpy generator(s) to make generation deterministic. TYPE: `np.random.Generator` or `List[np.random.Generator]`, *optional* DEFAULT: None
latents	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
pooled_prompt_embeds	Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.flux.FluxPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
joint_attention_kwargs	A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. TYPE: `dict`, *optional* DEFAULT: None
callback_on_step_end	A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs. TYPE: `Callable`, *optional* DEFAULT: None
callback_on_step_end_tensor_inputs	The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class. TYPE: `List`, *optional* DEFAULT: ['latents']
max_sequence_length	Maximum sequence length to use with the prompt. TYPE: `int` defaults to 512 DEFAULT: 512

RETURNS	DESCRIPTION
	[~pipelines.flux.FluxPipelineOutput] or tuple: [~pipelines.flux.FluxPipelineOutput] if return_dict
	is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated
	images.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

def __call__(
    self,
    task_prompt: Union[str, List[str]] = None,
    content_prompt: Union[str, List[str]] = None,
    image: Optional[ms.Tensor] = None,
    num_inference_steps: int = 50,
    sigmas: Optional[List[float]] = None,
    guidance_scale: float = 30.0,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    pooled_prompt_embeds: Optional[ms.Tensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = False,
    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 = 512,
):
    r"""
    Function invoked when calling the VisualCloze pipeline for generation.

    Args:
        task_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the task intention.
        content_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the content or caption of the target image to be generated.
        image (`ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
            `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
            numpy array and mindspore tensor, the expected value range is between `[0, 1]` If it's a tensor or a
            list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
            list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
        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.
        sigmas (`List[float]`, *optional*):
            Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
            their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
            will be used.
        guidance_scale (`float`, *optional*, defaults to 30.0):
            Guidance scale as defined in [Classifier-Free Diffusion
            Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
            of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
            One or a list of [numpy generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
            to make generation deterministic.
        latents (`ms.Tensor`, *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`.
        prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
            provided, text embeddings will be generated from `prompt` input argument.
        pooled_prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
            If not provided, pooled text embeddings will be generated from `prompt` input argument.
        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 `False`):
            Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
        joint_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        callback_on_step_end (`Callable`, *optional*):
            A function that calls at the end of each denoising steps during the inference. The function is called
            with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
            callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
            `callback_on_step_end_tensor_inputs`.
        callback_on_step_end_tensor_inputs (`List`, *optional*):
            The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
            will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
            `._callback_tensor_inputs` attribute of your pipeline class.
        max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.

    Examples:

    Returns:
        [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
        is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
        images.
    """

    # 1. Check inputs. Raise error if not correct
    self.check_inputs(
        image,
        task_prompt,
        content_prompt,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    processor_output = self.image_processor.preprocess(
        task_prompt, content_prompt, image, vae_scale_factor=self.vae_scale_factor
    )

    # 2. Define call parameters
    if processor_output["task_prompt"] is not None and isinstance(processor_output["task_prompt"], str):
        batch_size = 1
    elif processor_output["task_prompt"] is not None and isinstance(processor_output["task_prompt"], list):
        batch_size = len(processor_output["task_prompt"])

    # 3. Prepare prompt embeddings
    lora_scale = self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
        layout_prompt=processor_output["layout_prompt"],
        task_prompt=processor_output["task_prompt"],
        content_prompt=processor_output["content_prompt"],
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )

    # 4. Prepare timesteps
    # Calculate sequence length and shift factor
    image_seq_len = sum(
        (size[0] // self.vae_scale_factor // 2) * (size[1] // self.vae_scale_factor // 2)
        for sample in processor_output["image_size"][0]
        for size in sample
    )

    # Calculate noise schedule parameters
    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),
    )

    # Get timesteps
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        sigmas=sigmas,
        mu=mu,
    )
    timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1.0)

    # 5. Prepare latent variables
    latent_timestep = timesteps[:1].tile((batch_size * num_images_per_prompt,))
    latents, masked_image_latents, latent_image_ids = self.prepare_latents(
        processor_output["init_image"],
        processor_output["mask"],
        latent_timestep,
        batch_size * num_images_per_prompt,
        prompt_embeds.dtype,
        generator,
        vae_scale_factor=self.vae_scale_factor,
    )

    # Calculate warmup steps
    num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
    self._num_timesteps = len(timesteps)

    # Prepare guidance
    if self.transformer.config.guidance_embeds:
        guidance = mint.full([1], guidance_scale, dtype=ms.float32)
        guidance = guidance.broadcast_to((latents.shape[0],))
    else:
        guidance = None

    # 6. Denoising loop
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            if self.interrupt:
                continue

            # Broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            timestep = t.broadcast_to((latents.shape[0],)).to(latents.dtype)
            latent_model_input = mint.cat((latents, masked_image_latents), dim=2)

            noise_pred = self.transformer(
                hidden_states=latent_model_input,
                timestep=timestep / 1000,
                guidance=guidance,
                pooled_projections=pooled_prompt_embeds,
                encoder_hidden_states=prompt_embeds,
                txt_ids=text_ids,
                img_ids=latent_image_ids,
                joint_attention_kwargs=self.joint_attention_kwargs,
                return_dict=False,
            )[0]

            # 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 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)

            # 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()

    # 7. Post-process the image
    # Crop the target image
    # Since the generated image is a concatenation of the conditional and target regions,
    # we need to extract only the target regions based on their positions
    image = []
    if output_type == "latent":
        image = latents
    else:
        for b in range(len(latents)):
            cur_image_size = processor_output["image_size"][b % batch_size]
            cur_target_position = processor_output["target_position"][b % batch_size]
            cur_latent = self._unpack_latents(latents[b].unsqueeze(0), cur_image_size, self.vae_scale_factor)[-1]
            cur_latent = (cur_latent / self.vae.config.scaling_factor) + self.vae.config.shift_factor
            cur_image = self.vae.decode(cur_latent, return_dict=False)[0]
            cur_image = self.image_processor.postprocess(cur_image, output_type=output_type)[0]

            start = 0
            cropped = []
            for i, size in enumerate(cur_image_size[-1]):
                if cur_target_position[i]:
                    if output_type == "pil":
                        cropped.append(cur_image.crop((start, 0, start + size[1], size[0])))
                    else:
                        cropped.append(cur_image[0 : size[0], start : start + size[1]])
                start += size[1]
            image.append(cropped)
        if output_type != "pil":
            image = np.concatenate([arr[None] for sub_image in image for arr in sub_image], axis=0)

    if not return_dict:
        return (image,)

    return FluxPipelineOutput(images=image)

mindone.diffusers.VisualClozeGenerationPipeline.disable_vae_slicing()

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

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()

mindone.diffusers.VisualClozeGenerationPipeline.disable_vae_tiling()

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

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()

mindone.diffusers.VisualClozeGenerationPipeline.enable_vae_slicing()

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.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

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()

mindone.diffusers.VisualClozeGenerationPipeline.enable_vae_tiling()

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.

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

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()

mindone.diffusers.VisualClozeGenerationPipeline.encode_prompt(layout_prompt, task_prompt, content_prompt, num_images_per_prompt=1, prompt_embeds=None, pooled_prompt_embeds=None, max_sequence_length=512, lora_scale=None)

PARAMETER	DESCRIPTION
layout_prompt	The prompt or prompts to define the number of in-context examples and the number of images involved in the task. TYPE: `str` or `List[str]`, *optional*
task_prompt	The prompt or prompts to define the task intention. TYPE: `str` or `List[str]`, *optional*
content_prompt	The prompt or prompts to define the content or caption of the target image to be generated. TYPE: `str` or `List[str]`, *optional*
num_images_per_prompt	number of images that should be generated per prompt TYPE: `int` DEFAULT: 1
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
pooled_prompt_embeds	Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
lora_scale	A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. TYPE: `float`, *optional* DEFAULT: None

Source code in mindone/diffusers/pipelines/visualcloze/pipeline_visualcloze_generation.py

def encode_prompt(
    self,
    layout_prompt: Union[str, List[str]],
    task_prompt: Union[str, List[str]],
    content_prompt: Union[str, List[str]],
    num_images_per_prompt: int = 1,
    prompt_embeds: Optional[ms.Tensor] = None,
    pooled_prompt_embeds: Optional[ms.Tensor] = None,
    max_sequence_length: int = 512,
    lora_scale: Optional[float] = None,
):
    r"""

    Args:
        layout_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the number of in-context examples and the number of images involved in
            the task.
        task_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the task intention.
        content_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to define the content or caption of the target image to be generated.
        num_images_per_prompt (`int`):
            number of images that should be generated per prompt
        prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
            provided, text embeddings will be generated from `prompt` input argument.
        pooled_prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
            If not provided, pooled text embeddings will be generated from `prompt` input argument.
        lora_scale (`float`, *optional*):
            A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
    """
    # set lora scale so that monkey patched LoRA
    # function of text encoder can correctly access it
    if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
        self._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        if self.text_encoder is not None:
            scale_lora_layers(self.text_encoder, lora_scale)
        if self.text_encoder_2 is not None:
            scale_lora_layers(self.text_encoder_2, lora_scale)

    if isinstance(layout_prompt, str):
        layout_prompt = [layout_prompt]
        task_prompt = [task_prompt]
        content_prompt = [content_prompt]

    def _preprocess(prompt, content=False):
        if prompt is not None:
            return f"The last image of the last row depicts: {prompt}" if content else prompt
        else:
            return ""

    prompt = [
        f"{_preprocess(layout_prompt[i])} {_preprocess(task_prompt[i])} {_preprocess(content_prompt[i], content=True)}".strip()
        for i in range(len(layout_prompt))
    ]
    pooled_prompt_embeds = self._get_clip_prompt_embeds(
        prompt=prompt,
        num_images_per_prompt=num_images_per_prompt,
    )
    prompt_embeds = self._get_t5_prompt_embeds(
        prompt=prompt,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
    )

    if self.text_encoder is not None:
        if isinstance(self, FluxLoraLoaderMixin):
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder, lora_scale)

    if self.text_encoder_2 is not None:
        if isinstance(self, FluxLoraLoaderMixin):
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder_2, lora_scale)

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

    return prompt_embeds, pooled_prompt_embeds, text_ids