Revert "[LoRA] introduce LoraBaseMixin to promote reusability." (#8773)

Revert "[LoRA] introduce `LoraBaseMixin` to promote reusability. (#8670)"

This reverts commit a2071a1.

Author: sayakpaul

docs/source/en/api/loaders/lora.md

@@ -12,13 +12,10 @@ specific language governing permissions and limitations under the License.
 
 # LoRA
 
-LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([`UNet2DConditionModel`], for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights:
+LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the UNet, text encoder or both. There are two classes for loading LoRA weights:
 
 - [`LoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
 - [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`LoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
-- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
-- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
-- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 <Tip>
 
@@ -32,16 +29,4 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 ## StableDiffusionXLLoraLoaderMixin
 
-[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin
-
-## SD3LoraLoaderMixin
-
-[[autodoc]] loaders.lora.SD3LoraLoaderMixin
-
-## AmusedLoraLoaderMixin
-
-[[autodoc]] loaders.lora.AmusedLoraLoaderMixin
-
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
+[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin

examples/amused/train_amused.py

@@ -41,7 +41,7 @@
 
 import diffusers.optimization
 from diffusers import AmusedPipeline, AmusedScheduler, EMAModel, UVit2DModel, VQModel
-from diffusers.loaders import AmusedLoraLoaderMixin
+from diffusers.loaders import LoraLoaderMixin
 from diffusers.utils import is_wandb_available
 
 
@@ -532,7 +532,7 @@ def save_model_hook(models, weights, output_dir):
                 weights.pop()
 
             if transformer_lora_layers_to_save is not None or text_encoder_lora_layers_to_save is not None:
-                AmusedLoraLoaderMixin.save_lora_weights(
+                LoraLoaderMixin.save_lora_weights(
                     output_dir,
                     transformer_lora_layers=transformer_lora_layers_to_save,
                     text_encoder_lora_layers=text_encoder_lora_layers_to_save,
@@ -566,11 +566,11 @@ def load_model_hook(models, input_dir):
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
         if transformer is not None or text_encoder_ is not None:
-            lora_state_dict, network_alphas = AmusedLoraLoaderMixin.lora_state_dict(input_dir)
-            AmusedLoraLoaderMixin.load_lora_into_text_encoder(
+            lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
+            LoraLoaderMixin.load_lora_into_text_encoder(
                 lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_
             )
-            AmusedLoraLoaderMixin.load_lora_into_transformer(
+            LoraLoaderMixin.load_lora_into_transformer(
                 lora_state_dict, network_alphas=network_alphas, transformer=transformer
             )
 

src/diffusers/loaders/__init__.py

@@ -55,18 +55,11 @@ def text_encoder_attn_modules(text_encoder):
 
 if is_torch_available():
     _import_structure["single_file_model"] = ["FromOriginalModelMixin"]
-    _import_structure["transformer_sd3"] = ["SD3TransformerLoadersMixin"]
-
     _import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
     _import_structure["utils"] = ["AttnProcsLayers"]
     if is_transformers_available():
         _import_structure["single_file"] = ["FromSingleFileMixin"]
-        _import_structure["lora"] = [
-            "AmusedLoraLoaderMixin",
-            "LoraLoaderMixin",
-            "SD3LoraLoaderMixin",
-            "StableDiffusionXLLoraLoaderMixin",
-        ]
+        _import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin", "SD3LoraLoaderMixin"]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
         _import_structure["ip_adapter"] = ["IPAdapterMixin"]
 
@@ -76,18 +69,12 @@ def text_encoder_attn_modules(text_encoder):
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
         from .single_file_model import FromOriginalModelMixin
-        from .transformer_sd3 import SD3TransformerLoadersMixin
         from .unet import UNet2DConditionLoadersMixin
         from .utils import AttnProcsLayers
 
         if is_transformers_available():
             from .ip_adapter import IPAdapterMixin
-            from .lora import (
-                AmusedLoraLoaderMixin,
-                LoraLoaderMixin,
-                SD3LoraLoaderMixin,
-                StableDiffusionXLLoraLoaderMixin,
-            )
+            from .lora import LoraLoaderMixin, SD3LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin
             from .single_file import FromSingleFileMixin
             from .textual_inversion import TextualInversionLoaderMixin