| from dataclasses import dataclass |
| from typing import List, Union |
|
|
| import numpy as np |
| import PIL.Image |
| import torch |
|
|
| from ...utils import BaseOutput |
|
|
|
|
| @dataclass |
| class FluxPipelineOutput(BaseOutput): |
| """ |
| Output class for Flux 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: Union[List[PIL.Image.Image], np.ndarray] |
|
|
|
|
| @dataclass |
| class FluxPriorReduxPipelineOutput(BaseOutput): |
| """ |
| Output class for Flux Prior Redux pipelines. |
| |
| Args: |
| images (`List[PIL.Image.Image]` or `np.ndarray`) |
| List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, |
| num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. |
| """ |
|
|
| prompt_embeds: torch.Tensor |
| pooled_prompt_embeds: torch.Tensor |
|
|
