diffusers/pipelines/flux2/pipeline_output.py

from dataclasses import dataclass

import numpy as np
import PIL.Image

from ...utils import BaseOutput


@dataclass
class Flux2PipelineOutput(BaseOutput):
    """
    Output class for Flux2 image generation pipelines.

    Args:
        images (`list[PIL.Image.Image]` or `torch.Tensor` or `np.ndarray`)
            List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,
            height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion
            pipeline. Torch tensors can represent either the denoised images or the intermediate latents ready to be
            passed to the decoder.
    """

    images: list[PIL.Image.Image, np.ndarray]