[VAE] fix the downsample block in Encoder. (#156)

* pass downsample_padding in encoder

* update tests

src/diffusers/models/vae.py

@@ -40,6 +40,7 @@ class Encoder(nn.Module):
                 out_channels=output_channel,
                 add_downsample=not is_final_block,
                 resnet_eps=1e-6,
+                downsample_padding=0,
                 resnet_act_fn=act_fn,
                 attn_num_head_channels=None,
                 temb_channels=None,

tests/test_modeling_utils.py

@@ -555,11 +555,11 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
 
     def prepare_init_args_and_inputs_for_common(self):
         init_dict = {
-            "block_out_channels": [64],
+            "block_out_channels": [32, 64],
             "in_channels": 3,
             "out_channels": 3,
-            "down_block_types": ["DownEncoderBlock2D"],
-            "up_block_types": ["UpDecoderBlock2D"],
+            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
             "latent_channels": 3,
         }
         inputs_dict = self.dummy_input
@@ -595,7 +595,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
 
         output_slice = output[0, -1, -3:, -3:].flatten()
         # fmt: off
-        expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218])
+        expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
 
@@ -623,22 +623,11 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
 
     def prepare_init_args_and_inputs_for_common(self):
         init_dict = {
-            "ch": 64,
-            "ch_mult": (1,),
-            "embed_dim": 4,
-            "in_channels": 3,
-            "attn_resolutions": [],
-            "num_res_blocks": 1,
-            "out_ch": 3,
-            "resolution": 32,
-            "z_channels": 4,
-        }
-        init_dict = {
-            "block_out_channels": [64],
+            "block_out_channels": [32, 64],
             "in_channels": 3,
             "out_channels": 3,
-            "down_block_types": ["DownEncoderBlock2D"],
-            "up_block_types": ["UpDecoderBlock2D"],
+            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
             "latent_channels": 4,
         }
         inputs_dict = self.dummy_input
@@ -674,7 +663,7 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
 
         output_slice = output[0, -1, -3:, -3:].flatten()
         # fmt: off
-        expected_output_slice = torch.tensor([-0.3900, -0.2800, 0.1281, -0.4449, -0.4890, -0.0207, 0.0784, -0.1258, -0.0409])
+        expected_output_slice = torch.tensor([-4.0078e-01, -3.8304e-04, -1.2681e-01, -1.1462e-01,  2.0095e-01, 1.0893e-01, -8.8248e-02, -3.0361e-01, -9.8646e-03])
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))