Deprecate attention block #2697
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| " If you are only loading a model instead of a whole pipeline, the same instructions apply with `ModelMixin#save_pretrained`." | ||
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| deprecate("AttentionBlock", "0.18.0", deprecation_message, standard_warn=True) |
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@patrickvonplaten is this the right deprecation version? iirc we talked about two minor versions
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Yeah, I'd maybe even bumb it up a bit more to "0.20.0" maybe
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| @@ -783,6 +785,71 @@ def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool | |||
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| return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable) | |||
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| def _convert_deprecated_attention_blocks(self): | |||
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@patrickvonplaten Is it ok to put all of the conversion code for each class in this one method and determine which model it's in by checking self.__class__.__name__? I think it's a bit more straightforward than splitting it up in the different model definitions
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| bias=True, | ||
| upcast_softmax=True, | ||
| norm_num_groups=resnet_groups, | ||
| processor=SpatialAttnProcessor(), |
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Hmm is there really a difference between Spatial Attention and "normal" attention - isn't it just some reshaping before and after that's different?
Could we maybe try to use the normal attention processor and do the reshaping outside of the attention (e.g. in the VAE directly) - this would also allow us to use all the other attention processors out of the box (e.g. LoRA, xfomers, torch 2.0)
Wdyt?
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wip commit up 5aa3446
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There are some failures when running the integration tests against this branch #2759 I'll need to fix those before we merge |
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Ah yeah, can you also update the testing branch? A bunch of test failures have recently been fixed on main |
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re: #1880
PR description
We are in the process of deprecating the
AttentionBlockclass. All attention will be done throughattention_processor.Attention.The goal of this PR is to remove the body of
AttentionBlockand replace it withSpatialAttnProcessorandXFormersSpatialAttnProcessor. This will allow checkpoints which have weights in the format ofAttentionBlockto useattention_processor.Attention.After model load, we directly replace all instances of AttentionBlock with
attention_processor.Attention. The forward method ofAttentionBlockthrows an error.The next step will be to re-upload
AttentionBlockweights on the hub to be in the same format asattention_processor.Attentionalong with a configuration flag to useattention_processor.Attention.Hub stats for attention blocks in diffusers
from https://github.com/williamberman/attention-block-refactor
UNet2DModel, VQModel, and AutoEncoderKL are top level models which all have the deprecated attention block as a sub module. The conditional unet can be configured to use the deprecated attention block but there are no hub uploads which are configured as such.
The most common model is AutoEncoderKL because all stable diffusion checkpoints use it
UNet2DModel
1039 hub uploads
blocks using deprecated attention
VQModel
13 hub uploads
blocks using deprecated attention
AutoencoderKL
2927 hub uploads
blocks using deprecated attention
UNet2DModel, VQModel, and AutoencoderKL all have the flag
attention_block_typeadded. This flag is passed down to AttnDownBlock2D, AttnUpBlock2D, AttnSkipDownBlock2D, AttnSkipUpBlock2D, UNetMidBlock2D, AttnDownEncoderBlock2D, and AttnUpDecoderBlock2D for switching to the non-deprecated attention blockWhen do we convert the attention blocks
_convert_deprecated_attention_blocksis the method added to the ModelMixin which converts all deprecated attention blocks.We cannot call
_convert_deprecated_attention_blockin the constructor of the model because weights in the old format can be loaded after the constructor is called.ModelMixinare created byModelMixin#from_pretrained. After the weights have been loaded, convert the attention blocks.ModelMixinare created only with their constructor and passed to the pipeline's constructor. Convert the attention blocks inDiffusionPipeline#register_modules.