Fix the total_downscale_factor returned by FullAdapterXL T2IAdapters (#5134)

* Fix FullAdapterXL.total_downscale_factor.

* Fix incorrect error message in T2IAdapter.__init__(...).

* Move IP-Adapter test_total_downscale_factor(...) to pipeline test file (requested in code review).

* Add more info to error message about an unsupported T2I-Adapter adapter_type.

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/models/adapter.py

@@ -252,7 +252,10 @@ class T2IAdapter(ModelMixin, ConfigMixin):
         elif adapter_type == "light_adapter":
             self.adapter = LightAdapter(in_channels, channels, num_res_blocks, downscale_factor)
         else:
-            raise ValueError(f"unknown adapter_type: {type}. Choose either 'full_adapter' or 'simple_adapter'")
+            raise ValueError(
+                f"Unsupported adapter_type: '{adapter_type}'. Choose either 'full_adapter' or "
+                "'full_adapter_xl' or 'light_adapter'."
+            )
 
     def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
         return self.adapter(x)
@@ -331,8 +334,8 @@ class FullAdapterXL(nn.Module):
                 self.body.append(AdapterBlock(channels[i], channels[i], num_res_blocks))
 
         self.body = nn.ModuleList(self.body)
-        # XL has one fewer downsampling
-        self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 2)
+        # XL has only one downsampling AdapterBlock.
+        self.total_downscale_factor = downscale_factor * 2
 
     def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
         x = self.unshuffle(x)

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -18,6 +18,7 @@ import unittest
 
 import numpy as np
 import torch
+from parameterized import parameterized
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 import diffusers
@@ -184,6 +185,37 @@ class StableDiffusionXLAdapterPipelineFastTests(PipelineTesterMixin, unittest.Te
         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
 
 
+    @parameterized.expand(["full_adapter", "full_adapter_xl", "light_adapter"])
+    def test_total_downscale_factor(self, adapter_type):
+        """Test that the T2IAdapter correctly reports its total_downscale_factor."""
+        batch_size = 1
+        in_channels = 3
+        out_channels = [320, 640, 1280, 1280]
+        in_image_size = 512
+
+        adapter = T2IAdapter(
+            in_channels=in_channels,
+            channels=out_channels,
+            num_res_blocks=2,
+            downscale_factor=8,
+            adapter_type=adapter_type,
+        )
+        adapter.to(torch_device)
+
+        in_image = floats_tensor((batch_size, in_channels, in_image_size, in_image_size)).to(torch_device)
+
+        adapter_state = adapter(in_image)
+
+        # Assume that the last element in `adapter_state` has been downsampled the most, and check
+        # that it matches the `total_downscale_factor`.
+        expected_out_image_size = in_image_size // adapter.total_downscale_factor
+        assert adapter_state[-1].shape == (
+            batch_size,
+            out_channels[-1],
+            expected_out_image_size,
+            expected_out_image_size,
+        )
+
 class StableDiffusionXLMultiAdapterPipelineFastTests(
     StableDiffusionXLAdapterPipelineFastTests, PipelineTesterMixin, unittest.TestCase
 ):