update ic_light adapter, bugfix + improve docstrings

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

@@ -1,4 +1,5 @@
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
     SD1Autoencoder,
@@ -16,4 +17,5 @@ __all__ = [
     "SD1ControlnetAdapter",
     "SD1IPAdapter",
     "SD1T2IAdapter",
+    "ICLight",
 ]

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

@@ -3,7 +3,7 @@ from PIL import Image
 from torch.nn.init import zeros_ as zero_init
 
 from refiners.fluxion import layers as fl
-from refiners.fluxion.utils import image_to_tensor, no_grad
+from refiners.fluxion.utils import no_grad
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder, StableDiffusion_1
@@ -11,12 +11,14 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import Down
 
 
 class ICLight(StableDiffusion_1):
-    """
+    """IC-Light is a Stable Diffusion model that can be used to relight a reference image.
-    IC-Light is a Stable Diffusion model that can be used to relight a reference image.
 
-    At initialization, the UNet will be patched to accept four additional input channels. Only the text-conditioned relighting model is supported for now.
+    At initialization, the UNet will be patched to accept four additional input channels.
+    Only the text-conditioned relighting model is supported for now.
 
-    ```example
+
+    Example:
+        ```py
         import torch
         from huggingface_hub import hf_hub_download
         from PIL import Image
@@ -84,6 +86,7 @@ class ICLight(StableDiffusion_1):
         predicted_image = sd.lda.latents_to_image(x)
 
         predicted_image.save("ic-light-output.png")
+        ```
     """
 
     def __init__(
@@ -109,9 +112,7 @@ class ICLight(StableDiffusion_1):
 
     @no_grad()
     def _extend_conv_in(self) -> None:
-        """
+        """Extend to 8 the input channels of the first convolutional layer of the UNet."""
-        Extend to 8 the input channels of the first convolutional layer of the UNet.
-        """
         down_blocks = self.unet.ensure_find(DownBlocks)
         first_block = down_blocks.layer(0, fl.Chain)
         conv_in = first_block.ensure_find(fl.Conv2d)
@@ -129,9 +130,7 @@ class ICLight(StableDiffusion_1):
         first_block.replace(old_module=conv_in, new_module=new_conv_in)
 
     def _apply_patch(self, weights: dict[str, torch.Tensor]) -> None:
-        """
+        """Apply the weights patch to the UNet, modifying inplace the state dict."""
-        Apply the patch weights to the UNet, modifying inplace the state dict.
-        """
         current_state_dict = self.unet.state_dict()
         new_state_dict = {
             key: tensor + weights[key].to(tensor.device, tensor.dtype) for key, tensor in current_state_dict.items()
@@ -139,9 +138,17 @@ class ICLight(StableDiffusion_1):
         self.unet.load_state_dict(new_state_dict)
 
     @staticmethod
-    def compute_gray_composite(image: Image.Image, mask: Image.Image) -> Image.Image:
+    def compute_gray_composite(
-        """
+        image: Image.Image,
-        Compute a grayscale composite of an image and a mask.
+        mask: Image.Image,
+    ) -> Image.Image:
+        """Compute a grayscale composite of an image and a mask.
+
+        IC-Light will recreate the image
+
+        Args:
+            image: The image to composite.
+            mask: The mask to use for the composite.
         """
         assert mask.mode == "L", "Mask must be a grayscale image"
         assert image.size == mask.size, "Image and mask must have the same size"
@@ -149,28 +156,31 @@ class ICLight(StableDiffusion_1):
         return Image.composite(image, background, mask)
 
     def set_ic_light_condition(
-        self, image: Image.Image, mask: Image.Image | None = None, use_rescaled_image: bool = False
+        self,
+        image: Image.Image,
+        mask: Image.Image | None = None,
     ) -> None:
-        """
+        """Set the IC light condition.
-        Set the IC light condition.
+
+        Args:
+            image: The reference image.
+            mask: The mask to use for the reference image.
 
         If a mask is provided, it will be used to compute a grayscale composite of the image and the mask ; otherwise,
         the image will be used as is, but note that IC-Light requires a 127-valued gray background to work.
-
-        `use_rescaled_image` is used to rescale the image to [-1, 1] range. This is the expected range when using the
-        Stable Diffusion autoencoder. But in the original code this part is skipped, giving different results.
-        see https://github.com/lllyasviel/IC-Light/blob/788687452a2bad59633a401281c8aee91bdd3750/gradio_demo.py#L262-L265
         """
         if mask is not None:
             image = self.compute_gray_composite(image=image, mask=mask)
-        image_tensor = image_to_tensor(image, device=self.device, dtype=self.dtype)
+        latents = self.lda.image_to_latents(image)
-        if use_rescaled_image:
-            image_tensor = 2 * image_tensor - 1
-        latents = self.lda.encode(image_tensor)
         self._ic_light_condition = latents
 
     def __call__(
-        self, x: torch.Tensor, step: int, *, clip_text_embedding: torch.Tensor, condition_scale: float = 2.0
+        self,
+        x: torch.Tensor,
+        step: int,
+        *,
+        clip_text_embedding: torch.Tensor,
+        condition_scale: float = 2.0,
     ) -> torch.Tensor:
         assert self._ic_light_condition is not None, "Reference image not set, use `set_ic_light_condition` first"
         x = torch.cat((x, self._ic_light_condition), dim=1)