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Fix tests and remove some testing code.

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This commit is contained in:
Daniel Gu
2023-05-14 21:26:20 -07:00
parent 4d656b50a0
commit be4abffe88
2 changed files with 2 additions and 27 deletions
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8
src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py
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@@ -1314,9 +1314,6 @@ class UniDiffuserPipeline(DiffusionPipeline):
elif mode in ["img2text", "text"]:
latents = prompt_embeds
print(f"Initial latents: {latents}")
print(f"Initial latents shape: {latents.shape}")
# 7. Check that shapes of latents and image match the UNet channels.
# TODO
@@ -1329,7 +1326,6 @@ class UniDiffuserPipeline(DiffusionPipeline):
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
print(f"Step {i} / timestep {t}")
# predict the noise residual
# Also applies classifier-free guidance as described in the UniDiffuser paper
noise_pred = self._get_noise_pred(
@@ -1348,15 +1344,11 @@ class UniDiffuserPipeline(DiffusionPipeline):
width,
)
print(f"noise_pred: {noise_pred}")
# TODO: do we need to worry about sigma space stuff for the scheduler?
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
print(f"New latents: {latents}")
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()

21
tests/pipelines/unidiffuser/test_unidiffuser.py
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@@ -585,7 +585,7 @@ class UniDiffuserPipelineSlowTests(unittest.TestCase):
inputs = self.get_inputs()
del inputs["prompt"]
sample = pipe(**inputs)
text = sample.images
text = sample.text
expected_text_prefix = "Astronaut "
assert text[0][:10] == expected_text_prefix
@@ -629,23 +629,6 @@ class UniDiffuserPipelineSlowTests(unittest.TestCase):
expected_slice = np.array([0.4702, 0.4666, 0.4446, 0.4829, 0.4468, 0.4565, 0.4663, 0.4956, 0.4277])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_unidiffuser_default_text2img_v1_fp16_no_cfg(self):
pipe = UniDiffuserPipeline.from_pretrained("dg845/unidiffuser-diffusers", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
del inputs["image"]
inputs["guidance_scale"] = 0.0
sample = pipe(**inputs)
image = sample.images
assert image.shape == (1, 512, 512, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.4702, 0.4666, 0.4446, 0.4829, 0.4468, 0.4565, 0.4663, 0.4956, 0.4277])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_unidiffuser_default_img2text_v1_fp16(self):
pipe = UniDiffuserPipeline.from_pretrained("dg845/unidiffuser-diffusers", torch_dtype=torch.float16)
pipe.to(torch_device)
@@ -655,7 +638,7 @@ class UniDiffuserPipelineSlowTests(unittest.TestCase):
inputs = self.get_inputs()
del inputs["prompt"]
sample = pipe(**inputs)
text = sample.images
text = sample.text
expected_text_prefix = "Astronaut "
assert text[0][:10] == expected_text_prefix