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fix pipeline __setattr__ value == None #3063

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Merged
merged 2 commits into from
Apr 12, 2023
Merged

fix pipeline __setattr__ value == None #3063

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522cee3
fix pipeline __setattr__
williamberman Apr 12, 2023
ffd48fa
add test
patrickvonplaten Apr 12, 2023
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2 changes: 1 addition & 1 deletion src/diffusers/pipelines/pipeline_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -510,7 +510,7 @@ def __setattr__(self, name: str, value: Any):
if hasattr(self, name) and hasattr(self.config, name):
# We need to overwrite the config if name exists in config
if isinstance(getattr(self.config, name), (tuple, list)):
if self.config[name][0] is not None:
if value is not None and self.config[name][0] is not None:
class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__)
else:
Comment on lines 510 to 515
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@williamberman williamberman Apr 12, 2023 •
edited
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the setting value can be None with a defined class library tuple on the config -- i.e. if the text encoder is an optional component

I think overwriting the config class library tuple to None in this case is not ideal but acceptable as it isn't common behavior

class_library_tuple = (None, None)
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40 changes: 40 additions & 0 deletions tests/test_pipelines.py
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Expand Up @@ -929,6 +929,46 @@ def test_set_scheduler(self):
sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)

def test_set_component_to_none(self):
unet = self.dummy_cond_unet()
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

pipeline = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=None,
feature_extractor=self.dummy_extractor,
)

generator = torch.Generator(device="cpu").manual_seed(0)

prompt = "This is a flower"

out_image = pipeline(
prompt=prompt,
generator=generator,
num_inference_steps=1,
output_type="np",
).images

pipeline.feature_extractor = None
generator = torch.Generator(device="cpu").manual_seed(0)
out_image_2 = pipeline(
prompt=prompt,
generator=generator,
num_inference_steps=1,
output_type="np",
).images

assert out_image.shape == (1, 64, 64, 3)
assert np.abs(out_image - out_image_2).max() < 1e-3

def test_set_scheduler_consistency(self):
unet = self.dummy_cond_unet()
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
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