AttentionModuleMixin.set_attention_slice references two attributes that do not exist

[yupengtang]

Describe the bug

AttentionModuleMixin.set_attention_slice in src/diffusers/models/attention.py refers to two attributes that do not exist anywhere in the codebase, so it raises AttributeError for every argument:

if slice_size is not None:
    processor = self._get_compatible_processor("sliced")   # no such method anywhere in src/

if processor is None:
    processor = self.default_processor_cls()               # the attribute is `_default_processor_cls`

grep -rn "_get_compatible_processor" src/ returns only that one call site, and default_processor_cls (without the underscore) is likewise only that one use. The class attribute defined on the mixin at line 122 is _default_processor_cls. Thirty-six files under models/transformers, models/autoencoders and models/condition_embedders define AttentionModuleMixin subclasses, so they all inherit this method.

I could not find a public entry point that routes into this method today. The models that recurse into submodule set_attention_slice (unet_2d_condition, unet_3d_condition, the three controlnets, audioldm2, versatile_diffusion) all hold attention_processor.Attention modules, which have their own working set_attention_slice. So this looks latent rather than something users are hitting. It stops being latent as soon as a model built on AttentionModuleMixin gets wired into that recursion, which the # TODO: the following will not be required when everything is refactored to AttentionModuleMixin comment in modeling_utils.py suggests is the direction.

I am reporting rather than sending a patch because only half of it is a typo. Renaming default_processor_cls to _default_processor_cls fixes the slice_size=None path, but the other branch needs _get_compatible_processor to exist, and what "a compatible sliced processor" should mean under the new mixin is a design call. None of the _available_processors lists in the tree contain a sliced processor, so there is nothing for it to return yet. Happy to send a PR for whichever shape you want.

Reproduction

Since nothing routes into the method, the smallest failing thing is the method itself on a real attention module. AnimaTextConditionerAttention is one of the AttentionModuleMixin subclasses and is cheap to build:

from diffusers.models.condition_embedders.condition_embedder_anima import AnimaTextConditionerAttention

attn = AnimaTextConditionerAttention(
    query_dim=64, context_dim=64, num_attention_heads=4, attention_head_dim=16
)
print(attn._default_processor_cls.__name__)   # AnimaTextConditionerAttnProcessor

for slice_size in (None, 1):
    try:
        attn.set_attention_slice(slice_size)
        print(slice_size, "->", type(attn.processor).__name__)
    except AttributeError as e:
        print(slice_size, "->", e)

Any of the other subclasses behaves the same way, since both names are missing from the mixin itself:

from diffusers.models.attention import AttentionModuleMixin

hasattr(AttentionModuleMixin, "_default_processor_cls")   # True
hasattr(AttentionModuleMixin, "default_processor_cls")    # False
hasattr(AttentionModuleMixin, "_get_compatible_processor")  # False

Logs

AnimaTextConditionerAttnProcessor
None -> 'AnimaTextConditionerAttention' object has no attribute 'default_processor_cls'
1 -> 'AnimaTextConditionerAttention' object has no attribute '_get_compatible_processor'

System Info

• diffusers: 0.41.0.dev0, main at c5469b7
• PyTorch: 2.11.0+cu130
• Python: 3.13.15
• Platform: Linux, CPU

The reproduction constructs a module directly and downloads nothing.

Who can help?

@DN6 @yiyixuxu