diffusers/pr_11739/en/api/models/autoencoder_kl_hunyuanimage.md

AutoencoderKLHunyuanImage

The 2D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1].

The model can be loaded with the following code snippet.

from diffusers import AutoencoderKLHunyuanImage

vae = AutoencoderKLHunyuanImage.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)

AutoencoderKLHunyuanImage[[diffusers.AutoencoderKLHunyuanImage]]

diffusers.AutoencoderKLHunyuanImage[[diffusers.AutoencoderKLHunyuanImage]]

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A VAE model for 2D images with spatial tiling support.

This model inherits from ModelMixin. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving).

wrapperdiffusers.AutoencoderKLHunyuanImage.decodehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/utils/accelerate_utils.py#L43[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]

enable_tiling[[diffusers.AutoencoderKLHunyuanImage.enable_tiling]]

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Enable spatial 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.

Parameters:

tile_sample_min_size (int, optional) : The minimum size required for a sample to be separated into tiles across the spatial dimension.

tile_overlap_factor (float, optional) : The overlap factor required for a latent to be separated into tiles across the spatial dimension.

forward[[diffusers.AutoencoderKLHunyuanImage.forward]]

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Parameters:

sample (torch.Tensor) : Input sample.

return_dict (bool, optional, defaults to True) : Whether or not to return a DecoderOutput instead of a plain tuple.

tiled_decode[[diffusers.AutoencoderKLHunyuanImage.tiled_decode]]

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Decode latent using spatial tiling strategy.

Parameters:

z (torch.Tensor) : Latent tensor of shape (B, C, H, W).

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.vae.DecoderOutput instead of a plain tuple.

Returns:

~models.vae.DecoderOutput` or `tuple

If return_dict is True, a ~models.vae.DecoderOutput is returned, otherwise a plain tuple is returned.

tiled_encode[[diffusers.AutoencoderKLHunyuanImage.tiled_encode]]

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Encode input using spatial tiling strategy.

Parameters:

x (torch.Tensor) : Input tensor of shape (B, C, T, H, W).

Returns:

torch.Tensor

The latent representation of the encoded images.

DecoderOutput[[diffusers.models.autoencoders.vae.DecoderOutput]]

diffusers.models.autoencoders.vae.DecoderOutput[[diffusers.models.autoencoders.vae.DecoderOutput]]

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Output of decoding method.

Parameters:

sample (torch.Tensor of shape (batch_size, num_channels, height, width)) : The decoded output sample from the last layer of the model.