mirror of
https://github.com/huggingface/diffusers.git
synced 2025-12-16 01:14:47 +08:00
* update
* udpate
* update transformer
* make style
* fix
* add conversion script
* update
* fix
* update
* fix
* update
* fixes
* make style
* update
* update
* update
* init
* update
* update
* add
* up
* up
* up
* update
* mochi transformer
* remove original implementation
* make style
* update inits
* update conversion script
* docs
* Update src/diffusers/pipelines/mochi/pipeline_mochi.py
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* Update src/diffusers/pipelines/mochi/pipeline_mochi.py
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fix docs
* pipeline fixes
* make style
* invert sigmas in scheduler; fix pipeline
* fix pipeline num_frames
* flip proj and gate in swiglu
* make style
* fix
* make style
* fix tests
* latent mean and std fix
* update
* cherry-pick 1069d210e1
* remove additional sigma already handled by flow match scheduler
* fix
* remove hardcoded value
* replace conv1x1 with linear
* Update src/diffusers/pipelines/mochi/pipeline_mochi.py
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* framewise decoding and conv_cache
* make style
* Apply suggestions from code review
* mochi vae encoder changes
* rebase correctly
* Update scripts/convert_mochi_to_diffusers.py
* fix tests
* fixes
* make style
* update
* make style
* update
* add framewise and tiled encoding
* make style
* make original vae implementation behaviour the default; note: framewise encoding does not work
* remove framewise encoding implementation due to presence of attn layers
* fight test 1
* fight test 2
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail.com>
1.1 KiB
1.1 KiB
MochiTransformer3DModel
A Diffusion Transformer model for 3D video-like data was introduced in Mochi-1 Preview by Genmo.
The model can be loaded with the following code snippet.
from diffusers import MochiTransformer3DModel
vae = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
MochiTransformer3DModel
autodoc MochiTransformer3DModel
Transformer2DModelOutput
autodoc models.modeling_outputs.Transformer2DModelOutput