ldm: properly resize non-square init image

src/refiners/foundationals/latent_diffusion/model.py

@@ -49,16 +49,16 @@ class LatentDiffusionModel(fl.Module, ABC):
         first_step: int = 0,
         noise: Tensor | None = None,
     ) -> Tensor:
+        height, width = size
         if noise is None:
-            height, width = size
             noise = torch.randn(1, 4, height // 8, width // 8, device=self.device)
         assert list(noise.shape[2:]) == [
-            size[0] // 8,
-            size[1] // 8,
+            height // 8,
+            width // 8,
         ], f"noise shape is not compatible: {noise.shape}, with size: {size}"
         if init_image is None:
             return noise
-        encoded_image = self.lda.encode_image(image=init_image.resize(size=size))
+        encoded_image = self.lda.encode_image(image=init_image.resize(size=(width, height)))
         return self.scheduler.add_noise(x=encoded_image, noise=noise, step=self.steps[first_step])
 
     @property

tests/e2e/test_diffusion.py

@@ -492,6 +492,21 @@ def test_diffusion_std_init_image(
     ensure_similar_images(predicted_image, expected_image_std_init_image)
 
 
+@torch.no_grad()
+def test_rectangular_init_latents(
+    sd15_std: StableDiffusion_1,
+    cutecat_init: Image.Image,
+):
+    sd15 = sd15_std
+
+    # Just check latents initialization with a non-square image (and not the entire diffusion)
+    width, height = 512, 504
+    rect_init_image = cutecat_init.crop((0, 0, width, height))
+    x = sd15.init_latents((height, width), rect_init_image)
+
+    assert sd15.lda.decode_latents(x).size == (width, height)
+
+
 @torch.no_grad()
 def test_diffusion_inpainting(
     sd15_inpainting: StableDiffusion_1_Inpainting,