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# coding=utf-8 | |
# Copyright 2023 HuggingFace Inc. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import unittest | |
import numpy as np | |
import torch | |
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel | |
from diffusers.utils.testing_utils import require_torch, slow, torch_device | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class ScoreSdeVeipelineFastTests(unittest.TestCase): | |
def dummy_uncond_unet(self): | |
torch.manual_seed(0) | |
model = UNet2DModel( | |
block_out_channels=(32, 64), | |
layers_per_block=2, | |
sample_size=32, | |
in_channels=3, | |
out_channels=3, | |
down_block_types=("DownBlock2D", "AttnDownBlock2D"), | |
up_block_types=("AttnUpBlock2D", "UpBlock2D"), | |
) | |
return model | |
def test_inference(self): | |
unet = self.dummy_uncond_unet | |
scheduler = ScoreSdeVeScheduler() | |
sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler) | |
sde_ve.to(torch_device) | |
sde_ve.set_progress_bar_config(disable=None) | |
generator = torch.manual_seed(0) | |
image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images | |
generator = torch.manual_seed(0) | |
image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[ | |
0 | |
] | |
image_slice = image[0, -3:, -3:, -1] | |
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
assert image.shape == (1, 32, 32, 3) | |
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
class ScoreSdeVePipelineIntegrationTests(unittest.TestCase): | |
def test_inference(self): | |
model_id = "google/ncsnpp-church-256" | |
model = UNet2DModel.from_pretrained(model_id) | |
scheduler = ScoreSdeVeScheduler.from_pretrained(model_id) | |
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler) | |
sde_ve.to(torch_device) | |
sde_ve.set_progress_bar_config(disable=None) | |
generator = torch.manual_seed(0) | |
image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 256, 256, 3) | |
expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |