87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import torch
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from diffusers import KarrasVePipeline, KarrasVeScheduler, UNet2DModel
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from diffusers.utils.testing_utils import require_torch, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class KarrasVePipelineFastTests(unittest.TestCase):
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@property
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def dummy_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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return model
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def test_inference(self):
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unet = self.dummy_uncond_unet
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scheduler = KarrasVeScheduler()
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pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
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generator = torch.manual_seed(0)
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image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch
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class KarrasVePipelineIntegrationTests(unittest.TestCase):
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def test_inference(self):
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model_id = "google/ncsnpp-celebahq-256"
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model = UNet2DModel.from_pretrained(model_id)
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scheduler = KarrasVeScheduler()
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pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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