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  1. 19 Apr, 2023 1 commit
  3. 18 Apr, 2023 2 commits
  5. 17 Apr, 2023 4 commits
  7. 16 Apr, 2023 2 commits
  9. 14 Apr, 2023 3 commits
  11. 13 Apr, 2023 3 commits
  13. 12 Apr, 2023 14 commits
  15. 11 Apr, 2023 10 commits
    • Will Berman's avatar
      Attn added kv processor torch 2.0 block (#3023) · ea39cd7e
      Will Berman authored 
      add AttnAddedKVProcessor2_0 block
      ea39cd7e
    • Will Berman's avatar
      Attention processor cross attention norm group norm (#3021) · 98c5e5da
      Will Berman authored 
      add group norm type to attention processor cross attention norm

This lets the cross attention norm use both a group norm block and a
layer norm block.

The group norm operates along the channels dimension
and requires input shape (batch size, channels, *) where as the layer norm with a single
`normalized_shape` dimension only operates over the least significant
dimension i.e. (*, channels).

The channels we want to normalize are the hidden dimension of the encoder hidden states.

By convention, the encoder hidden states are always passed as (batch size, sequence
length, hidden states).

This means the layer norm can operate on the tensor without modification, but the group
norm requires flipping the last two dimensions to operate on (batch size, hidden states, sequence length).

All existing attention processors will have the same logic and we can
consolidate it in a helper function `prepare_encoder_hidden_states`

prepare_encoder_hidden_states -> norm_encoder_hidden_states re: @patrickvonplaten

move norm_cross defined check to outside norm_encoder_hidden_states

add missing attn.norm_cross check
      98c5e5da
    • Will Berman's avatar
      unet time embedding activation function (#3048) · 2d52e81c
      Will Berman authored 
      * unet time embedding activation function

* typo act_fn -> time_embedding_act_fn

* flatten conditional
      2d52e81c
    • Chanchana Sornsoontorn's avatar
      Fix typo and format BasicTransformerBlock attributes (#2953) · 52c4d32d
      Chanchana Sornsoontorn authored 
      * ⚙️chore(train_controlnet) fix typo in logger message

* ⚙️chore(models) refactor modules order; make them the same as calling order

When printing the BasicTransformerBlock to stdout, I think it's crucial that the attributes order are shown in proper order. And also previously the "3. Feed Forward" comment was not making sense. It should have been close to self.ff but it's instead next to self.norm3

* correct many tests

* remove bogus file

* make style

* correct more tests

* finish tests

* fix one more

* make style

* make unclip deterministic

* ⚙

️chore(models/attention) reorganize comments in BasicTransformerBlock class

---------
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      52c4d32d
    • Will Berman's avatar
      add only cross attention to simple attention blocks (#3011) · c6180a31
      Will Berman authored 
      * add only cross attention to simple attention blocks

* add test for only_cross_attention re: @patrickvonplaten

* mid_block_only_cross_attention better default

allow mid_block_only_cross_attention to default to
`only_cross_attention` when `only_cross_attention` is given
as a single boolean
      c6180a31
    • Pedro Cuenca's avatar
      Fix scheduler type mismatch (#3041) · 526827c3
      Pedro Cuenca authored 
      When doing generation manually and using guidance_scale as a static
argument.
      526827c3
    • George Ogden's avatar
      Update documentation (#2996) · cb63febf
      George Ogden authored 
      * Update documentation

Based on sampling, the width and height must be powers of 2 as the samples halve in size each time

* make style
      cb63febf
    • Will Berman's avatar
      `AttentionProcessor.group_norm` num_channels should be `query_dim` (#3046) · 8c6b47cf
      Will Berman authored 
      * `AttentionProcessor.group_norm` num_channels should be `query_dim`

The group_norm on the attention processor should really norm the number
of channels in the query _not_ the inner dim. This wasn't caught before
because the group_norm is only used by the added kv attention processors
and the added kv attention processors are only used by the karlo models
which are configured such that the inner dim is the same as the query
dim.

* add_{k,v}_proj should be projecting to inner_dim
      8c6b47cf
    • Will Berman's avatar
      config fixes (#3060) · 80bc0c0c
      Will Berman authored
      80bc0c0c
    • Patrick von Platen's avatar
      Fix config prints and save, load of pipelines (#2849) · 8b451eb6
      Patrick von Platen authored 
      * [Config] Fix config prints and save, load

* Only use potential nn.Modules for dtype and device

* Correct vae image processor

* make sure in_channels is not accessed directly

* make sure in channels is only accessed via config

* Make sure schedulers only access config attributes

* Make sure to access config in SAG

* Fix vae processor and make style

* add tests

* uP

* make style

* Fix more naming issues

* Final fix with vae config

* change more
      8b451eb6
  17. 10 Apr, 2023 1 commit
    • Andranik Movsisyan's avatar
      [Pipeline] Add TextToVideoZeroPipeline (#2954) · ba49272d
      Andranik Movsisyan authored 
      

* add TextToVideoZeroPipeline and CrossFrameAttnProcessor

* add docs for text-to-video zero

* add teaser image for text-to-video zero docs

* Fix review changes. Add Documentation. Add test

* clean up the codes in pipeline_text_to_video.py. Add descriptive comments and docstrings

* make style && make quality

* make fix-copies

* make requested changes to docs. use huggingface server links for resources, delete res folder

* make style && make quality && make fix-copies

* make style && make quality

* Apply suggestions from code review

---------
Co-authored-by: default avatarSayak Paul <spsayakpaul@gmail.com>
      ba49272d