shorter import paths

scripts/conversion/convert_diffusers_controlnet.py

@@ -6,9 +6,11 @@ from torch import nn
 from diffusers import ControlNetModel  # type: ignore
 from refiners.fluxion.utils import save_to_safetensors
 from refiners.fluxion.model_converter import ModelConverter
-from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1Controlnet
+from refiners.foundationals.latent_diffusion import (
-from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
+    SD1UNet,
-from refiners.foundationals.latent_diffusion import SD1UNet
+    SD1Controlnet,
+    DPMSolver,
+)
 
 
 class Args(argparse.Namespace):

scripts/conversion/convert_diffusers_lora.py

@@ -9,7 +9,7 @@ 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.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
+from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget, apply_loras_to_target
 from refiners.adapters.lora import Lora
 

scripts/conversion/convert_refiners_lora_to_sdwebui.py

@@ -12,8 +12,8 @@ from convert_transformers_clip_text_model import (
     Args as TextEncoderConversionArgs,
 )
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
+from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget
-from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1UNet
 import refiners.fluxion.layers as fl
 
 

src/refiners/foundationals/latent_diffusion/__init__.py

@@ -5,20 +5,14 @@ from refiners.foundationals.clip.text_encoder import (
     CLIPTextEncoderL,
 )
 from refiners.foundationals.latent_diffusion.schedulers import Scheduler, DPMSolver
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
+from refiners.foundationals.latent_diffusion.stable_diffusion_1 import (
     StableDiffusion_1,
     StableDiffusion_1_Inpainting,
-)
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
     SD1UNet,
-)
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import (
     SD1Controlnet,
 )
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import (
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl import (
     SDXLUNet,
-)
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import (
     DoubleTextEncoder,
 )
 

src/refiners/foundationals/latent_diffusion/self_attention_injection.py

@@ -10,7 +10,7 @@ from refiners.fluxion.layers import (
     Parallel,
 )
 from refiners.adapters.adapter import Adapter
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
+from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock
 from torch import Tensor
 

src/refiners/training_utils/latent_diffusion.py

@@ -1,20 +1,22 @@
 from dataclasses import dataclass
 from typing import Any, TypeVar, TypedDict, cast
 from pydantic import BaseModel
-from refiners.foundationals.latent_diffusion.schedulers.ddpm import DDPM
 from torch import device as Device, Tensor, randn, dtype as DType, Generator, cat
 from loguru import logger
 from torch.utils.data import Dataset
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
-from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from torchvision.transforms import RandomCrop  # type: ignore
 import refiners.fluxion.layers as fl
 from PIL import Image
 from functools import cached_property
 from refiners.training_utils.config import BaseConfig
-from refiners.foundationals.latent_diffusion import StableDiffusion_1
+from refiners.foundationals.latent_diffusion import (
-from refiners.foundationals.latent_diffusion.schedulers import DPMSolver
+    StableDiffusion_1,
+    DPMSolver,
+    SD1UNet,
+    LatentDiffusionAutoencoder,
+)
+from refiners.foundationals.latent_diffusion.schedulers import DDPM
 from torch.nn.functional import mse_loss
 import random
 from refiners.training_utils.wandb import WandbLoggable

tests/e2e/test_diffusion.py

@@ -8,9 +8,12 @@ from PIL import Image
 from pathlib import Path
 
 from refiners.fluxion.utils import load_from_safetensors, image_to_tensor, manual_seed
-from refiners.foundationals.latent_diffusion import StableDiffusion_1, StableDiffusion_1_Inpainting
+from refiners.foundationals.latent_diffusion import (
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
+    StableDiffusion_1,
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1Controlnet
+    StableDiffusion_1_Inpainting,
+    SD1UNet,
+    SD1Controlnet,
+)
 from refiners.foundationals.latent_diffusion.lora import LoraWeights
 from refiners.foundationals.latent_diffusion.schedulers import DDIM
 from refiners.foundationals.latent_diffusion.self_attention_injection import SelfAttentionInjection

tests/foundationals/latent_diffusion/test_unet.py

@@ -1,4 +1,4 @@
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
+from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.fluxion import manual_seed
 import torch