improve typing of ldm and sd1, introducing SD1Autoencoder class

src/refiners/foundationals/latent_diffusion/auto_encoder.py

@@ -197,13 +197,13 @@ class Decoder(Chain):
 
 class LatentDiffusionAutoencoder(Chain):
     structural_attrs = ["encoder_scale"]
+    encoder_scale = 0.18125
 
     def __init__(
         self,
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
-        self.encoder_scale: float = 0.18215
         super().__init__(
             Encoder(device=device, dtype=dtype),
             Decoder(device=device, dtype=dtype),

src/refiners/foundationals/latent_diffusion/model.py

@@ -66,19 +66,14 @@ class LatentDiffusionModel(fl.Module, ABC):
         return self.scheduler.steps
 
     @abstractmethod
-    def set_unet_context(self, timestep: Tensor, clip_text_embedding: Tensor, *args: Tensor) -> None:
+    def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
         ...
 
     def forward(
-        self,
-        x: Tensor,
-        step: int,
-        clip_text_embedding: Tensor,
-        *args: Tensor,
-        condition_scale: float = 7.5,
+        self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor
     ) -> Tensor:
         timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
-        self.set_unet_context(timestep=timestep, clip_text_embedding=clip_text_embedding, *args)
+        self.set_unet_context(timestep=timestep, clip_text_embedding=clip_text_embedding, **kwargs)
 
         latents = torch.cat(tensors=(x, x))  # for classifier-free guidance
         unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2)

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

@@ -11,6 +11,10 @@ import numpy as np
 from torch import device as Device, dtype as DType, Tensor
 
 
+class SD1Autoencoder(LatentDiffusionAutoencoder):
+    encoder_scale: float = 0.18215
+
+
 class StableDiffusion_1(LatentDiffusionModel):
     unet: SD1UNet
     clip_text_encoder: CLIPTextEncoderL
@@ -18,14 +22,14 @@ class StableDiffusion_1(LatentDiffusionModel):
     def __init__(
         self,
         unet: SD1UNet | None = None,
-        lda: LatentDiffusionAutoencoder | None = None,
+        lda: SD1Autoencoder | None = None,
         clip_text_encoder: CLIPTextEncoderL | None = None,
         scheduler: Scheduler | None = None,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
         unet = unet or SD1UNet(in_channels=4)
-        lda = lda or LatentDiffusionAutoencoder()
+        lda = lda or SD1Autoencoder()
         clip_text_encoder = clip_text_encoder or CLIPTextEncoderL()
         scheduler = scheduler or DPMSolver(num_inference_steps=30)
 
@@ -46,7 +50,7 @@ class StableDiffusion_1(LatentDiffusionModel):
         negative_embedding = self.clip_text_encoder(negative_text or "")
         return torch.cat(tensors=(negative_embedding, conditional_embedding), dim=0)
 
-    def set_unet_context(self, timestep: Tensor, clip_text_embedding: Tensor, *_: Tensor) -> None:
+    def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
         self.unet.set_timestep(timestep=timestep)
         self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
 
@@ -55,7 +59,7 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
     def __init__(
         self,
         unet: SD1UNet | None = None,
-        lda: LatentDiffusionAutoencoder | None = None,
+        lda: SD1Autoencoder | None = None,
         clip_text_encoder: CLIPTextEncoderL | None = None,
         scheduler: Scheduler | None = None,
         device: Device | str = "cpu",
@@ -68,12 +72,7 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
         )
 
     def forward(
-        self,
-        x: Tensor,
-        step: int,
-        clip_text_embedding: Tensor,
-        *_: Tensor,
-        condition_scale: float = 7.5,
+        self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **_: Tensor
     ) -> Tensor:
         assert self.mask_latents is not None
         assert self.target_image_latents is not None

src/refiners/training_utils/latent_diffusion.py

@@ -9,12 +9,12 @@ from torchvision.transforms import Compose, RandomCrop, RandomHorizontalFlip  #
 import refiners.fluxion.layers as fl
 from PIL import Image
 from functools import cached_property
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
 from refiners.training_utils.config import BaseConfig
 from refiners.foundationals.latent_diffusion import (
     StableDiffusion_1,
     DPMSolver,
     SD1UNet,
-    LatentDiffusionAutoencoder,
 )
 from refiners.foundationals.latent_diffusion.schedulers import DDPM
 from torch.nn.functional import mse_loss
@@ -136,9 +136,9 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
         return CLIPTextEncoderL(device=self.device).to(device=self.device)
 
     @cached_property
-    def lda(self) -> LatentDiffusionAutoencoder:
+    def lda(self) -> SD1Autoencoder:
         assert self.config.models["lda"] is not None, "The config must contain a lda entry."
-        return LatentDiffusionAutoencoder(device=self.device).to(device=self.device)
+        return SD1Autoencoder(device=self.device).to(device=self.device)
 
     def load_models(self) -> dict[str, fl.Module]:
         return {"unet": self.unet, "text_encoder": self.text_encoder, "lda": self.lda}