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* update * refactor transformer part 1 * refactor part 2 * refactor part 3 * make style * refactor part 4; modeling tests * make style * refactor part 5 * refactor part 6 * gradient checkpointing * pipeline tests (broken atm) * update * add coauthor Co-Authored-By: Huan Yang <hyang@fastmail.com> * refactor part 7 * add docs * make style * add coauthor Co-Authored-By: YiYi Xu <yixu310@gmail.com> * make fix-copies * undo unrelated change * revert changes to embeddings, normalization, transformer * refactor part 8 * make style * refactor part 9 * make style * fix * apply suggestions from review * Apply suggestions from code review Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * update example * remove attention mask for self-attention * update * copied from * update * update --------- Co-authored-by: Huan Yang <hyang@fastmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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1.3 KiB
AutoencoderKLAllegro
The 3D variational autoencoder (VAE) model with KL loss used in Allegro was introduced in Allegro: Open the Black Box of Commercial-Level Video Generation Model by RhymesAI.
The model can be loaded with the following code snippet.
from diffusers import AutoencoderKLAllegro
vae = AutoencoderKLCogVideoX.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
AutoencoderKLAllegro
autodoc AutoencoderKLAllegro - decode - encode - all
AutoencoderKLOutput
autodoc models.autoencoders.autoencoder_kl.AutoencoderKLOutput
DecoderOutput
autodoc models.autoencoders.vae.DecoderOutput