119 lines
4.2 KiB
Python
119 lines
4.2 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 random
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import unittest
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import numpy as np
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import torch
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import PIL
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from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel
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from diffusers.utils import floats_tensor, load_image, slow, torch_device
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from diffusers.utils.testing_utils import require_torch
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class LDMSuperResolutionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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@property
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def dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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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=6,
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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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@property
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def dummy_vq_model(self):
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torch.manual_seed(0)
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model = VQModel(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=3,
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)
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return model
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def test_inference_superresolution(self):
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unet = self.dummy_uncond_unet
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scheduler = DDIMScheduler()
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vqvae = self.dummy_vq_model
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ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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init_image = self.dummy_image.to(torch_device)
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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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_ = ldm(init_image, generator=generator, num_inference_steps=1, output_type="numpy").images
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generator = torch.manual_seed(0)
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image = ldm(init_image, generator=generator, num_inference_steps=2, output_type="numpy").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.8634, 0.8186, 0.6416, 0.6846, 0.4427, 0.5676, 0.4679, 0.6247, 0.5176])
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tolerance = 1e-2 if torch_device != "mps" else 3e-2
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assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
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@slow
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@require_torch
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class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):
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def test_inference_superresolution(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/vq_diffusion/teddy_bear_pool.png"
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)
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init_image = init_image.resize((64, 64), resample=PIL.Image.LANCZOS)
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ldm = LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution", device_map="auto")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = ldm(init_image, generator=generator, num_inference_steps=20, 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([0.7418, 0.7472, 0.7424, 0.7422, 0.7463, 0.726, 0.7382, 0.7248, 0.6828])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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