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

AutoencoderKLQwenImage

The model can be loaded with the following code snippet.

from diffusers import AutoencoderKLQwenImage

vae = AutoencoderKLQwenImage.from_pretrained("Qwen/QwenImage-20B", subfolder="vae")

AutoencoderKLQwenImage[[diffusers.AutoencoderKLQwenImage]]

diffusers.AutoencoderKLQwenImage[[diffusers.AutoencoderKLQwenImage]]

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A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.

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

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

wrapper[[diffusers.AutoencoderKLQwenImage.encode]]

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enable_tiling[[diffusers.AutoencoderKLQwenImage.enable_tiling]]

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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.

Parameters:

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

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

tile_sample_stride_height (int, optional) : The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are no tiling artifacts produced across the height dimension.

tile_sample_stride_width (int, optional) : The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling artifacts produced across the width dimension.

forward[[diffusers.AutoencoderKLQwenImage.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.AutoencoderKLQwenImage.tiled_decode]]

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Decode a batch of images using a tiled decoder.

Parameters:

z (torch.Tensor) : Input batch of latent vectors.

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.AutoencoderKLQwenImage.tiled_encode]]

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Encode a batch of images using a tiled encoder.

Parameters:

x (torch.Tensor) : Input batch of videos.

Returns:

torch.Tensor

The latent representation of the encoded videos.

AutoencoderKLOutput[[diffusers.models.modeling_outputs.AutoencoderKLOutput]]

diffusers.models.modeling_outputs.AutoencoderKLOutput[[diffusers.models.modeling_outputs.AutoencoderKLOutput]]

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Output of AutoencoderKL encoding method.

Parameters:

latent_dist (DiagonalGaussianDistribution) : Encoded outputs of Encoder represented as the mean and logvar of DiagonalGaussianDistribution. DiagonalGaussianDistribution allows for sampling latents from the distribution.

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.