diffusers/tests/pipelines/flux/test_pipeline_flux_redux.py at main · huggingface/diffusers · GitHub

Name: diffusers/tests/pipelines/flux/test_pipeline_flux_redux.py at main · huggingface/diffusers · GitHub
Rating: 4.7 (8856 reviews)
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importgc
importunittest

importnumpyasnp
importpytest
importtorch

fromdiffusersimportFluxPipeline, FluxPriorReduxPipeline
fromdiffusers.utilsimportload_image
fromdiffusers.utils.testing_utilsimport (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow,
torch_device,
)


@slow
@require_big_accelerator
@pytest.mark.big_gpu_with_torch_cuda
classFluxReduxSlowTests(unittest.TestCase):
pipeline_class=FluxPriorReduxPipeline
repo_id="black-forest-labs/FLUX.1-Redux-dev"
base_pipeline_class=FluxPipeline
base_repo_id="black-forest-labs/FLUX.1-schnell"

defsetUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)

deftearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)

defget_inputs(self, device, seed=0):
init_image=load_image(
"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png"
 )
return {"image": init_image}

defget_base_pipeline_inputs(self, device, seed=0):
ifstr(device).startswith("mps"):
generator=torch.manual_seed(seed)
else:
generator=torch.Generator(device="cpu").manual_seed(seed)

return {
"num_inference_steps": 2,
"guidance_scale": 2.0,
"output_type": "np",
"generator": generator,
 }

deftest_flux_redux_inference(self):
pipe_redux=self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.bfloat16)
pipe_base=self.base_pipeline_class.from_pretrained(
self.base_repo_id, torch_dtype=torch.bfloat16, text_encoder=None, text_encoder_2=None
 )
pipe_redux.to(torch_device)
pipe_base.enable_model_cpu_offload(device=torch_device)

inputs=self.get_inputs(torch_device)
base_pipeline_inputs=self.get_base_pipeline_inputs(torch_device)

redux_pipeline_output=pipe_redux(**inputs)
image=pipe_base(**base_pipeline_inputs, **redux_pipeline_output).images[0]

image_slice=image[0, :10, :10]
expected_slices=Expectations(
 {
 ("cuda", 7): np.array(
 [
0.30078125,
0.37890625,
0.46875,
0.28125,
0.36914062,
0.47851562,
0.28515625,
0.375,
0.4765625,
0.28125,
0.375,
0.48046875,
0.27929688,
0.37695312,
0.47851562,
0.27734375,
0.38085938,
0.4765625,
0.2734375,
0.38085938,
0.47265625,
0.27539062,
0.37890625,
0.47265625,
0.27734375,
0.37695312,
0.47070312,
0.27929688,
0.37890625,
0.47460938,
 ],
dtype=np.float32,
 ),
 ("xpu", 3): np.array(
 [
0.20507812,
0.30859375,
0.3984375,
0.18554688,
0.30078125,
0.41015625,
0.19921875,
0.3125,
0.40625,
0.19726562,
0.3125,
0.41601562,
0.19335938,
0.31445312,
0.4140625,
0.1953125,
0.3203125,
0.41796875,
0.19726562,
0.32421875,
0.41992188,
0.19726562,
0.32421875,
0.41992188,
0.20117188,
0.32421875,
0.41796875,
0.203125,
0.32617188,
0.41796875,
 ],
dtype=np.float32,
 ),
 }
 )
expected_slice=expected_slices.get_expectation()

max_diff=numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())

assertmax_diff<1e-4