Handle batches and Tensors in `pipeline_stable_diffusion_inpaint.py:prepare_mask_and_masked_image` (#1003)
* Handle batches and Tensors in `prepare_mask_and_masked_image` * `blackfy` upgrade `black` * handle mask as `np.array` * add docstring * revert `black` changes with smaller line length * missing ValueError in docstring * raise `TypeError` for image as tensor but not mask * typo in mask shape selection * check for batch dim * fix: wrong indentation * add tests Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Victor Schmidt
GitHub
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@ -35,10 +35,87 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def prepare_mask_and_masked_image(image, mask):
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"""
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Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline.
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This means that those inputs will be converted to ``torch.Tensor`` with
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shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for
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the ``image`` and ``1`` for the ``mask``.
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The ``image`` will be converted to ``torch.float32`` and normalized to be in
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``[-1, 1]``. The ``mask`` will be binarized (``mask > 0.5``) and cast to
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``torch.float32`` too.
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Args:
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image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
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It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``
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or a ``channels x height x width`` ``torch.Tensor`` or a
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``batch x channels x height x width`` ``torch.Tensor``.
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mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
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It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or
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a ``1 x height x width`` ``torch.Tensor`` or a
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``batch x 1 x height x width`` ``torch.Tensor``.
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Raises:
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ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range.
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ValueError: ``torch.Tensor`` mask should be in the ``[0, 1]`` range.
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ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
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TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
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(ot the other way around).
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Returns:
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tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
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dimensions: ``batch x channels x height x width``.
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"""
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if isinstance(image, torch.Tensor):
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if not isinstance(mask, torch.Tensor):
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raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
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# Batch single image
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if image.ndim == 3:
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assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
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image = image.unsqueeze(0)
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# Batch and add channel dim for single mask
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if mask.ndim == 2:
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mask = mask.unsqueeze(0).unsqueeze(0)
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# Batch single mask or add channel dim
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if mask.ndim == 3:
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# Single batched mask, no channel dim or single mask not batched but channel dim
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if mask.shape[0] == 1:
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mask = mask.unsqueeze(0)
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# Batched masks no channel dim
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else:
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mask = mask.unsqueeze(1)
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assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
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assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
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assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
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# Check image is in [-1, 1]
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if image.min() < -1 or image.max() > 1:
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raise ValueError("Image should be in [-1, 1] range")
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# Check mask is in [0, 1]
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if mask.min() < 0 or mask.max() > 1:
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raise ValueError("Mask should be in [0, 1] range")
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# Binarize mask
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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# Image as float32
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image = image.to(dtype=torch.float32)
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elif isinstance(mask, torch.Tensor):
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raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
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else:
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if isinstance(image, PIL.Image.Image):
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image = np.array(image.convert("RGB"))
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
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if isinstance(mask, PIL.Image.Image):
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mask = np.array(mask.convert("L"))
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mask = mask.astype(np.float32) / 255.0
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mask = mask[None, None]
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@ -29,7 +29,10 @@ from diffusers import (
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UNet2DModel,
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VQModel,
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)
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from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
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from diffusers.utils.testing_utils import require_torch_gpu
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from PIL import Image
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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@ -506,3 +509,171 @@ class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
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mem_bytes = torch.cuda.max_memory_allocated()
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# make sure that less than 2.2 GB is allocated
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assert mem_bytes < 2.2 * 10**9
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class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
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def test_pil_inputs(self):
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im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
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im = Image.fromarray(im)
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mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
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mask = Image.fromarray((mask * 255).astype(np.uint8))
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t_mask, t_masked = prepare_mask_and_masked_image(im, mask)
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self.assertTrue(isinstance(t_mask, torch.Tensor))
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self.assertTrue(isinstance(t_masked, torch.Tensor))
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self.assertEqual(t_mask.ndim, 4)
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self.assertEqual(t_masked.ndim, 4)
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self.assertEqual(t_mask.shape, (1, 1, 32, 32))
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self.assertEqual(t_masked.shape, (1, 3, 32, 32))
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self.assertTrue(t_mask.dtype == torch.float32)
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self.assertTrue(t_masked.dtype == torch.float32)
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self.assertTrue(t_mask.min() >= 0.0)
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self.assertTrue(t_mask.max() <= 1.0)
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self.assertTrue(t_masked.min() >= -1.0)
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self.assertTrue(t_masked.min() <= 1.0)
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self.assertTrue(t_mask.sum() > 0.0)
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def test_np_inputs(self):
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im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
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im_pil = Image.fromarray(im_np)
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mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
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mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil)
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self.assertTrue((t_mask_np == t_mask_pil).all())
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self.assertTrue((t_masked_np == t_masked_pil).all())
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def test_torch_3D_2D_inputs(self):
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im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy().transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_3D_3D_inputs(self):
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im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy().transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_2D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_3D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_4D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0][0]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_batch_4D_3D(self):
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im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5
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im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
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mask_nps = [mask.numpy() for mask in mask_tensor]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
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t_mask_np = torch.cat([n[0] for n in nps])
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t_masked_np = torch.cat([n[1] for n in nps])
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_batch_4D_4D(self):
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im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (2, 1, 32, 32), dtype=torch.uint8) > 127.5
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im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
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mask_nps = [mask.numpy()[0] for mask in mask_tensor]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
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t_mask_np = torch.cat([n[0] for n in nps])
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t_masked_np = torch.cat([n[1] for n in nps])
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_shape_mismatch(self):
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# test height and width
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with self.assertRaises(AssertionError):
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prepare_mask_and_masked_image(torch.randn(3, 32, 32), torch.randn(64, 64))
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# test batch dim
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with self.assertRaises(AssertionError):
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prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 64, 64))
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# test batch dim
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with self.assertRaises(AssertionError):
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prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 1, 64, 64))
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def test_type_mismatch(self):
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# test tensors-only
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with self.assertRaises(TypeError):
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prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.rand(3, 32, 32).numpy())
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# test tensors-only
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with self.assertRaises(TypeError):
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prepare_mask_and_masked_image(torch.rand(3, 32, 32).numpy(), torch.rand(3, 32, 32))
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def test_channels_first(self):
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# test channels first for 3D tensors
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with self.assertRaises(AssertionError):
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prepare_mask_and_masked_image(torch.rand(32, 32, 3), torch.rand(3, 32, 32))
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def test_tensor_range(self):
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# test im <= 1
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with self.assertRaises(ValueError):
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prepare_mask_and_masked_image(torch.ones(3, 32, 32) * 2, torch.rand(32, 32))
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# test im >= -1
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with self.assertRaises(ValueError):
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prepare_mask_and_masked_image(torch.ones(3, 32, 32) * (-2), torch.rand(32, 32))
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# test mask <= 1
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with self.assertRaises(ValueError):
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prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * 2)
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# test mask >= 0
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with self.assertRaises(ValueError):
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prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * -1)
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