Fix using non-square images with UNet2DModel and DDIM/DDPM pipelines

src/diffusers/models/unet_2d.py

@@ -43,8 +43,8 @@ class UNet2DModel(ModelMixin, ConfigMixin):
     implements for all the model (such as downloading or saving, etc.)
 
     Parameters:
-        sample_size (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
-            Input sample size.
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
         in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.
         out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
         center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
@@ -71,7 +71,7 @@ class UNet2DModel(ModelMixin, ConfigMixin):
     @register_to_config
     def __init__(
         self,
-        sample_size: Optional[int] = None,
+        sample_size: Optional[Union[int, Tuple[int, int]]] = None,
         in_channels: int = 3,
         out_channels: int = 3,
         center_input_sample: bool = False,

src/diffusers/models/unet_2d_condition.py

@@ -56,7 +56,8 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin):
     implements for all the models (such as downloading or saving, etc.)
 
     Parameters:
-        sample_size (`int`, *optional*): The size of the input sample.
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
         in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
         out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
         center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -89,7 +89,11 @@ def __call__(
             generator = None
 
         # Sample gaussian noise to begin loop
-        image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
+        if isinstance(self.unet.sample_size, int):
+            image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
+        else:
+            image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
+
         if self.device.type == "mps":
             # randn does not work reproducibly on mps
             image = torch.randn(image_shape, generator=generator)

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -94,7 +94,11 @@ def __call__(
             generator = None
 
         # Sample gaussian noise to begin loop
-        image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
+        if isinstance(self.unet.sample_size, int):
+            image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
+        else:
+            image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
+
         if self.device.type == "mps":
             # randn does not work reproducibly on mps
             image = torch.randn(image_shape, generator=generator)

tests/test_pipelines.py

@@ -18,6 +18,7 @@
 import random
 import tempfile
 import unittest
+from functools import partial
 
 import numpy as np
 import torch
@@ -46,6 +47,7 @@
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
 from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
+from parameterized import parameterized
 from PIL import Image
 from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
@@ -247,7 +249,6 @@ def test_load_pipeline_from_git(self):
 
 
 class PipelineFastTests(unittest.TestCase):
-    @property
     def dummy_image(self):
         batch_size = 1
         num_channels = 3
@@ -256,27 +257,25 @@ def dummy_image(self):
         image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
         return image
 
-    @property
-    def dummy_uncond_unet(self):
+    def dummy_uncond_unet(self, sample_size=32):
         torch.manual_seed(0)
         model = UNet2DModel(
             block_out_channels=(32, 64),
             layers_per_block=2,
-            sample_size=32,
+            sample_size=sample_size,
             in_channels=3,
             out_channels=3,
             down_block_types=("DownBlock2D", "AttnDownBlock2D"),
             up_block_types=("AttnUpBlock2D", "UpBlock2D"),
         )
         return model
 
-    @property
-    def dummy_cond_unet(self):
+    def dummy_cond_unet(self, sample_size=32):
         torch.manual_seed(0)
         model = UNet2DConditionModel(
             block_out_channels=(32, 64),
             layers_per_block=2,
-            sample_size=32,
+            sample_size=sample_size,
             in_channels=4,
             out_channels=4,
             down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
@@ -285,13 +284,12 @@ def dummy_cond_unet(self):
         )
         return model
 
-    @property
-    def dummy_cond_unet_inpaint(self):
+    def dummy_cond_unet_inpaint(self, sample_size=32):
         torch.manual_seed(0)
         model = UNet2DConditionModel(
             block_out_channels=(32, 64),
             layers_per_block=2,
-            sample_size=32,
+            sample_size=sample_size,
             in_channels=9,
             out_channels=4,
             down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
@@ -300,7 +298,6 @@ def dummy_cond_unet_inpaint(self):
         )
         return model
 
-    @property
     def dummy_vq_model(self):
         torch.manual_seed(0)
         model = VQModel(
@@ -313,7 +310,6 @@ def dummy_vq_model(self):
         )
         return model
 
-    @property
     def dummy_vae(self):
         torch.manual_seed(0)
         model = AutoencoderKL(
@@ -326,7 +322,6 @@ def dummy_vae(self):
         )
         return model
 
-    @property
     def dummy_text_encoder(self):
         torch.manual_seed(0)
         config = CLIPTextConfig(
@@ -342,7 +337,6 @@ def dummy_text_encoder(self):
         )
         return CLIPTextModel(config)
 
-    @property
     def dummy_extractor(self):
         def extract(*args, **kwargs):
             class Out:
@@ -357,15 +351,43 @@ def to(self, device):
 
         return extract
 
-    def test_components(self):
+    @parameterized.expand(
+        [
+            [DDIMScheduler, DDIMPipeline, 32],
+            [partial(DDPMScheduler, predict_epsilon=True), DDPMPipeline, 32],
+            [DDIMScheduler, DDIMPipeline, (32, 64)],
+            [partial(DDPMScheduler, predict_epsilon=True), DDPMPipeline, (64, 32)],
+        ]
+    )
+    def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
+        unet = self.dummy_uncond_unet(sample_size)
+        # DDIM doesn't take `predict_epsilon`, and DDPM requires it -- so using partial in parameterized decorator
+        scheduler = scheduler_fn()
+        pipeline = pipeline_fn(unet, scheduler).to(torch_device)
+
+        # Device type MPS is not supported for torch.Generator() api.
+        if torch_device == "mps":
+            generator = torch.manual_seed(0)
+        else:
+            generator = torch.Generator(device=torch_device).manual_seed(0)
+
+        out_image = pipeline(
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+        ).images
+        sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
+        assert out_image.shape == (1, *sample_size, 3)
+
+    def test_stable_diffusion_components(self):
         """Test that components property works correctly"""
-        unet = self.dummy_cond_unet
+        unet = self.dummy_cond_unet()
         scheduler = PNDMScheduler(skip_prk_steps=True)
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
+        vae = self.dummy_vae()
+        bert = self.dummy_text_encoder()
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
 
-        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
+        image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]
         init_image = Image.fromarray(np.uint8(image)).convert("RGB")
         mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
 
@@ -377,7 +399,7 @@ def test_components(self):
             text_encoder=bert,
             tokenizer=tokenizer,
             safety_checker=None,
-            feature_extractor=self.dummy_extractor,
+            feature_extractor=self.dummy_extractor(),
         ).to(torch_device)
         img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
         text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)