diffusers/pr_12595/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]]

class diffusers.AutoencoderKLHunyuanImagediffusers.AutoencoderKLHunyuanImagehttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L413[{"name": "in_channels", "val": ": int"}, {"name": "out_channels", "val": ": int"}, {"name": "latent_channels", "val": ": int"}, {"name": "block_out_channels", "val": ": typing.Tuple[int, ...]"}, {"name": "layers_per_block", "val": ": int"}, {"name": "spatial_compression_ratio", "val": ": int"}, {"name": "sample_size", "val": ": int"}, {"name": "scaling_factor", "val": ": float = None"}, {"name": "downsample_match_channel", "val": ": bool = True"}, {"name": "upsample_match_channel", "val": ": bool = True"}]

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_12595/src/diffusers/utils/accelerate_utils.py#L43[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]

disable_slicingdiffusers.AutoencoderKLHunyuanImage.disable_slicinghttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L503[]

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

disable_tilingdiffusers.AutoencoderKLHunyuanImage.disable_tilinghttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L489[]

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

enable_slicingdiffusers.AutoencoderKLHunyuanImage.enable_slicinghttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L496[]

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.

enable_tilingdiffusers.AutoencoderKLHunyuanImage.enable_tilinghttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L468[{"name": "tile_sample_min_size", "val": ": typing.Optional[int] = None"}, {"name": "tile_overlap_factor", "val": ": typing.Optional[float] = None"}]- 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.0

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.

forwarddiffusers.AutoencoderKLHunyuanImage.forwardhttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L689[{"name": "sample", "val": ": Tensor"}, {"name": "sample_posterior", "val": ": bool = False"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "generator", "val": ": typing.Optional[torch._C.Generator] = None"}]- sample (torch.Tensor) -- Input sample.

• return_dict (bool, optional, defaults to True) -- Whether or not to return a DecoderOutput instead of a plain tuple.0

tiled_decodediffusers.AutoencoderKLHunyuanImage.tiled_decodehttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L644[{"name": "z", "val": ": Tensor"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0~models.vae.DecoderOutput or tupleIf return_dict is True, a ~models.vae.DecoderOutput is returned, otherwise a plain tuple is returned.

Decode latent using spatial tiling strategy.

tiled_encodediffusers.AutoencoderKLHunyuanImage.tiled_encodehttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py#L604[{"name": "x", "val": ": Tensor"}]- x (torch.Tensor) -- Input tensor of shape (B, C, T, H, W).0torch.TensorThe latent representation of the encoded images.

Encode input using spatial tiling strategy.

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

class diffusers.models.autoencoders.vae.DecoderOutputdiffusers.models.autoencoders.vae.DecoderOutputhttps://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/models/autoencoders/vae.py#L47[{"name": "sample", "val": ": Tensor"}, {"name": "commit_loss", "val": ": typing.Optional[torch.FloatTensor] = None"}]- sample (torch.Tensor of shape (batch_size, num_channels, height, width)) -- The decoded output sample from the last layer of the model.0

Output of decoding method.