diff --git a/src/diffusers/models/autoencoder_tiny.py b/src/diffusers/models/autoencoder_tiny.py
index fceaf0f130..ad36b7a2ce 100644
--- a/src/diffusers/models/autoencoder_tiny.py
+++ b/src/diffusers/models/autoencoder_tiny.py
@@ -312,9 +312,6 @@ class AutoencoderTiny(ModelMixin, ConfigMixin):
             output = torch.cat(output)
         else:
             output = self._tiled_decode(x) if self.use_tiling else self.decoder(x)
-        # Refer to the following discussion to know why this is needed.
-        # https://github.com/huggingface/diffusers/pull/4384#discussion_r1279401854
-        output = output.mul_(2).sub_(1)
 
         if not return_dict:
             return (output,)
@@ -333,8 +330,15 @@ class AutoencoderTiny(ModelMixin, ConfigMixin):
                 Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
         """
         enc = self.encode(sample).latents
+
+        # scale latents to be in [0, 1], then quantize latents to a byte tensor,
+        # as if we were storing the latents in an RGBA uint8 image.
         scaled_enc = self.scale_latents(enc).mul_(255).round_().byte()
-        unscaled_enc = self.unscale_latents(scaled_enc)
+
+        # unquantize latents back into [0, 1], then unscale latents back to their original range,
+        # as if we were loading the latents from an RGBA uint8 image.
+        unscaled_enc = self.unscale_latents(scaled_enc / 255.0)
+
         dec = self.decode(unscaled_enc)
 
         if not return_dict:
diff --git a/src/diffusers/models/vae.py b/src/diffusers/models/vae.py
index bd9562909d..d3c512b073 100644
--- a/src/diffusers/models/vae.py
+++ b/src/diffusers/models/vae.py
@@ -732,7 +732,8 @@ class EncoderTiny(nn.Module):
                 x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x)
 
         else:
-            x = self.layers(x)
+            # scale image from [-1, 1] to [0, 1] to match TAESD convention
+            x = self.layers(x.add(1).div(2))
 
         return x
 
@@ -790,4 +791,5 @@ class DecoderTiny(nn.Module):
         else:
             x = self.layers(x)
 
-        return x
+        # scale image from [0, 1] to [-1, 1] to match diffusers convention
+        return x.mul(2).sub(1)
diff --git a/tests/models/test_models_vae.py b/tests/models/test_models_vae.py
index 13c8652bf0..fe38b4fc21 100644
--- a/tests/models/test_models_vae.py
+++ b/tests/models/test_models_vae.py
@@ -312,10 +312,32 @@ class AutoencoderTinyIntegrationTests(unittest.TestCase):
         assert sample.shape == image.shape
 
         output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor([0.9858, 0.9262, 0.8629, 1.0974, -0.091, -0.2485, 0.0936, 0.0604])
+        expected_output_slice = torch.tensor([0.0093, 0.6385, -0.1274, 0.1631, -0.1762, 0.5232, -0.3108, -0.0382])
 
         assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
 
+    @parameterized.expand([(True,), (False,)])
+    def test_tae_roundtrip(self, enable_tiling):
+        # load the autoencoder
+        model = self.get_sd_vae_model()
+        if enable_tiling:
+            model.enable_tiling()
+
+        # make a black image with a white square in the middle,
+        # which is large enough to split across multiple tiles
+        image = -torch.ones(1, 3, 1024, 1024, device=torch_device)
+        image[..., 256:768, 256:768] = 1.0
+
+        # round-trip the image through the autoencoder
+        with torch.no_grad():
+            sample = model(image).sample
+
+        # the autoencoder reconstruction should match original image, sorta
+        def downscale(x):
+            return torch.nn.functional.avg_pool2d(x, model.spatial_scale_factor)
+
+        assert torch_all_close(downscale(sample), downscale(image), atol=0.125)
+
 
 @slow
 class AutoencoderKLIntegrationTests(unittest.TestCase):