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Improve pipeline callback tests #697

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106 changes: 57 additions & 49 deletions tests/test_pipelines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1450,31 +1450,35 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506]
[1.8279, 1.2858, -0.1022, 1.2406, -2.3068, 1.0748, -0.0819, -0.6522, -2.9496]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
if step == 50:
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@jamestiotio jamestiotio Oct 2, 2022

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It might be better to make it more explicit that this block is mutually exclusive from the step == 0 block. Something like:

Suggested change
if step == 50:
elif step == 50:

latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[1.0940, 1.5760, 0.2619, -0.0501, -1.7947, -0.3565, -0.4667, -1.0703, -1.1592]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

test_callback_fn.has_been_called = False

pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_dtype=torch.float16
)
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
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@patrickvonplaten patrickvonplaten Oct 4, 2022

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Not sure if we have enough memory for this

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@patrickvonplaten patrickvonplaten Oct 4, 2022

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Also why disable it? It gives the same results

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@pcuenca pcuenca Oct 4, 2022

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It was added by @jamestiotio because they didn't have a GPU with enough memory, and I thought our tests were performed with attention slicing disabled. But now I see they enable it, so I'll restore those lines.


prompt = "Andromeda galaxy in a bottle"

generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast(torch_device):
pipe(
prompt=prompt,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
pipe(
prompt=prompt,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 51

Expand All @@ -1491,7 +1495,13 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.9052, -0.0184, 0.4810, 0.2898, 0.5851, 1.4920, 0.5362, 1.9838, 0.0530])
expected_slice = np.array([0.9052, -0.0187, 0.4808, 0.2900, 0.5852, 1.4922, 0.5364, 1.9840, 0.0534])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
if step == 37:
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@jamestiotio jamestiotio Oct 2, 2022

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Same as above:

Suggested change
if step == 37:
elif step == 37:

latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.7063, 0.7833, 0.8345, 1.8114, 1.7867, 1.9398, 1.3653, 1.6623, 1.2869])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

test_callback_fn.has_been_called = False
Expand All @@ -1502,27 +1512,23 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
)
init_image = init_image.resize((768, 512))

pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_dtype=torch.float16
)
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()

prompt = "A fantasy landscape, trending on artstation"

generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast(torch_device):
pipe(
prompt=prompt,
init_image=init_image,
strength=0.75,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
pipe(
prompt=prompt,
init_image=init_image,
strength=0.75,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 38

Expand All @@ -1540,9 +1546,15 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.5472, 1.1218, -0.5505, -0.9390, -1.0794, 0.4063, 0.5158, 0.6429, -1.5246]
[-0.5472, 1.1218, -0.5504, -0.9391, -1.0795, 0.4064, 0.5158, 0.6427, -1.5245]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
if step == 37:
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@jamestiotio jamestiotio Oct 2, 2022

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Same as above:

Suggested change
if step == 37:
elif step == 37:

latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([0.4783, 1.1574, 0.6261, 0.2289, 0.2550, -0.1438, 0.7085, -0.1604, -0.5655])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

test_callback_fn.has_been_called = False

Expand All @@ -1555,28 +1567,24 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)

pipe = StableDiffusionInpaintPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_dtype=torch.float16
)
pipe = StableDiffusionInpaintPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()

prompt = "A red cat sitting on a park bench"

generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast(torch_device):
pipe(
prompt=prompt,
init_image=init_image,
mask_image=mask_image,
strength=0.75,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
pipe(
prompt=prompt,
init_image=init_image,
mask_image=mask_image,
strength=0.75,
num_inference_steps=50,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 38

Expand All @@ -1592,14 +1600,14 @@ def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.6254, -0.2742, -1.0710, 0.2296, -1.1683, 0.6913, -2.0605, -0.0682, 0.9700]
[-0.6255, -0.2742, -1.071, 0.2296, -1.1683, 0.6913, -2.0606, -0.0683, 0.9700]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

test_callback_fn.has_been_called = False

pipe = StableDiffusionOnnxPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="onnx", provider="CPUExecutionProvider"
"CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="onnx", provider="CUDAExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)

Expand Down