unet's sample_size attribute is to accept tuple(h, w) in StableDiffusionPipeline

src/diffusers/models/unets/unet_2d_condition.py

@@ -170,7 +170,7 @@ class conditioning with `class_embed_type` equal to `None`.
     @register_to_config
     def __init__(
         self,
-        sample_size: Optional[int] = None,
+        sample_size: Optional[Union[int, Tuple[int, int]]] = None,
         in_channels: int = 4,
         out_channels: int = 4,
         center_input_sample: bool = False,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -255,7 +255,12 @@ def __init__(
         is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
             version.parse(unet.config._diffusers_version).base_version
         ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        self._is_unet_config_sample_size_int = isinstance(unet.config.sample_size, int)
+        is_unet_sample_size_less_64 = (
+            hasattr(unet.config, "sample_size")
+            and self._is_unet_config_sample_size_int
+            and unet.config.sample_size < 64
+        )
         if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
             deprecation_message = (
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
@@ -902,8 +907,18 @@ def __call__(
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
+        if not height or not width:
+            height = (
+                self.unet.config.sample_size
+                if self._is_unet_config_sample_size_int
+                else self.unet.config.sample_size[0]
+            )
+            width = (
+                self.unet.config.sample_size
+                if self._is_unet_config_sample_size_int
+                else self.unet.config.sample_size[1]
+            )
+            height, width = height * self.vae_scale_factor, width * self.vae_scale_factor
         # to deal with lora scaling and other possible forward hooks
 
         # 1. Check inputs. Raise error if not correct

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -840,6 +840,14 @@ def callback_on_step_end(pipe, i, t, callback_kwargs):
         # they should be the same
         assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
 
+    def test_pipeline_accept_tuple_type_unet_sample_size(self):
+        # the purpose of this test is to see whether the pipeline would accept a unet with the tuple-typed sample size
+        sd_repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+        sample_size = [60, 80]
+        customised_unet = UNet2DConditionModel(sample_size=sample_size)
+        pipe = StableDiffusionPipeline.from_pretrained(sd_repo_id, unet=customised_unet)
+        assert pipe.unet.config.sample_size == sample_size
+
 
 @slow
 @require_torch_gpu