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AutoencoderKL: clamp indices of blend_h and blend_v to input size #2660

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Merged
merged 1 commit into from
Mar 14, 2023
Merged

AutoencoderKL: clamp indices of blend_h and blend_v to input size #2660

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4 changes: 2 additions & 2 deletions src/diffusers/models/autoencoder_kl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,12 +190,12 @@ def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[Decode
return DecoderOutput(sample=decoded)

def blend_v(self, a, b, blend_extent):
for y in range(blend_extent):
for y in range(min(a.shape[2], b.shape[2], blend_extent)):
b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b

def blend_h(self, a, b, blend_extent):
for x in range(blend_extent):
for x in range(min(a.shape[3], b.shape[3], blend_extent)):
b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b

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6 changes: 6 additions & 0 deletions tests/pipelines/stable_diffusion/test_stable_diffusion.py
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Expand Up @@ -445,6 +445,12 @@ def test_stable_diffusion_vae_tiling(self):

assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 5e-1

# test that tiled decode works with various shapes
shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
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Awesome addition to the test!

for shape in shapes:
zeros = torch.zeros(shape).to(device)
sd_pipe.vae.decode(zeros)

def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
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