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[df-if II] add additional input checks to ensure the input is divisible by 8 #7844

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[df-if II] add additional input checks to ensure the input is divisible by 8 #7844

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ffaa08f
#7842 add additional input checks to ensure the input is divisible by 8
May 2, 2024
ea22092
add a superresolution pipeline regression test for odd input sizes
May 2, 2024
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Merge branch 'main' into issue/7842
bghira May 3, 2024
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Merge branch 'main' into issue/7842
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Merge branch 'main' into issue/7842
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Merge branch 'main' into issue/7842
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Update src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresoluti…
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Merge branch 'main' into issue/7842
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15 changes: 15 additions & 0 deletions src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -543,12 +543,27 @@ def check_inputs(

if isinstance(image, list):
image_batch_size = len(image)
# Check that each image is the same size:
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I think it is better to do this in a separate code block:
So we keep this section as it is to check image_batch_size

and then

if isinstance(image, list):
    check_image_size = image[0]
else:
    check_image_size = image

if isinstance(check_image_size, PIL.Image.Image):
    image_size = check_image_size.size
elif isinstance(check_image_size, torch.Tensor):
   image_size = check_image_size.shape[2:]
elif isinstanc(..., np.ndarray):
    image_size = check_image.shape[:1]

if image_size ....:
    raise ValueError(...)

The current code does not work for list of array or tensors

if not all([i.size == image[0].size for i in image]):
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raise ValueError("All images must be the same size")
# Check that the size is divisible by 8:
if (image[0].size[0] % 8 != 0 or image[0].size[1] % 8 != 0):
raise ValueError("Image size must be divisible by 8")
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
# Check that the size is divisible by 8:
if (image.shape[2] % 8 != 0 or image.shape[3] % 8 != 0):
raise ValueError("Image size must be divisible by 8")
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
# Check that the size is divisible by 8:
if (image.size[0] % 8 != 0 or image.size[1] % 8 != 0):
raise ValueError("Image size must be divisible by 8")
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
# Check that the size is divisible by 8:
if (image.shape[1] % 8 != 0 or image.shape[2] % 8 != 0):
raise ValueError("Image size must be divisible by 8")
else:
assert False

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7 changes: 7 additions & 0 deletions tests/pipelines/deepfloyd_if/test_if_superresolution.py
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Expand Up @@ -57,6 +57,13 @@ def get_dummy_inputs(self, device, seed=0):

return inputs

def test_incorrect_input_size(self):
# Put an image non-divisible by 8 into the pipeline and check that it throws an Exception.
image = floats_tensor((1, 3, 31, 31), rng=random.Random(0)).to(torch_device)
generator = torch.Generator(device="cpu").manual_seed(0)
with self.assertRaises(ValueError):
self.pipeline(prompt="elegant destruction", image=image, generator=generator, num_inference_steps=2, output_type="np")

@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
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