make basic adapters a part of Fluxion

README.md

@@ -179,7 +179,7 @@ The `Adapter` API lets you **easily patch models** by injecting parameters in ta
 E.g. to inject LoRA layers in all attention's linear layers:
 
 ```python
-from refiners.adapters.lora import SingleLoraAdapter
+from refiners.fluxion.adapters.lora import SingleLoraAdapter
 
 for layer in vit.layers(fl.Attention):
     for linear, parent in layer.walk(fl.Linear):

scripts/conversion/convert_diffusers_lora.py

@@ -12,7 +12,7 @@ from diffusers import DiffusionPipeline  # type: ignore
 import refiners.fluxion.layers as fl
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import save_to_safetensors
-from refiners.adapters.lora import Lora, LoraAdapter
+from refiners.fluxion.adapters.lora import Lora, LoraAdapter
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget, lora_targets
 

src/refiners/fluxion/adapters/lora.py

@@ -1,7 +1,7 @@
 from typing import Iterable, Generic, TypeVar, Any
 
 import refiners.fluxion.layers as fl
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 
 from torch import Tensor, device as Device, dtype as DType
 from torch.nn import Parameter as TorchParameter

src/refiners/foundationals/clip/concepts.py

@@ -1,4 +1,4 @@
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 import refiners.fluxion.layers as fl

src/refiners/foundationals/latent_diffusion/lora.py

@@ -7,8 +7,8 @@ from torch import Tensor
 import refiners.fluxion.layers as fl
 from refiners.fluxion.utils import load_from_safetensors, load_metadata_from_safetensors
 
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
-from refiners.adapters.lora import SingleLoraAdapter, LoraAdapter
+from refiners.fluxion.adapters.lora import SingleLoraAdapter, LoraAdapter
 
 from refiners.foundationals.clip.text_encoder import FeedForward, TransformerLayer
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d

src/refiners/foundationals/latent_diffusion/range_adapter.py

@@ -2,7 +2,7 @@ import math
 from torch import Tensor, arange, float32, exp, sin, cat, cos, device as Device, dtype as DType
 from jaxtyping import Float, Int
 
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 import refiners.fluxion.layers as fl
 
 

src/refiners/foundationals/latent_diffusion/reference_only_control.py

@@ -9,7 +9,7 @@ from refiners.fluxion.layers import (
     Identity,
     Parallel,
 )
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock
 from torch import Tensor

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py

@@ -7,7 +7,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
     ResidualBlock,
     TimestepEncoder,
 )
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d
 from typing import cast, Iterable
 from torch import Tensor, device as Device, dtype as DType

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py

@@ -1,6 +1,6 @@
 from typing import cast
 from torch import device as Device, dtype as DType, Tensor, cat
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
 import refiners.fluxion.layers as fl
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderG, CLIPTextEncoderL

src/refiners/training_utils/dropout.py

@@ -5,7 +5,7 @@ from torch.nn import Dropout as TorchDropout
 
 import refiners.fluxion.layers as fl
 from refiners.training_utils.callback import Callback
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 
 if TYPE_CHECKING:
     from refiners.training_utils.config import BaseConfig

tests/adapters/test_adapter.py

@@ -1,5 +1,5 @@
 import pytest
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.layers import Chain, Linear
 
 

tests/adapters/test_lora.py

@@ -1,4 +1,4 @@
-from refiners.adapters.lora import Lora, SingleLoraAdapter, LoraAdapter
+from refiners.fluxion.adapters.lora import Lora, SingleLoraAdapter, LoraAdapter
 from torch import randn, allclose
 import refiners.fluxion.layers as fl
 

tests/adapters/test_range_adapter.py

@@ -1,5 +1,5 @@
 import torch
-from refiners.adapters.adapter import Adapter
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.latent_diffusion.range_adapter import RangeEncoder
 from refiners.fluxion.layers import Chain, Linear