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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 random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch
torch.backends.cuda.matmul.allow_tf32 = False
class LDMSuperResolutionPipelineFastTests(unittest.TestCase):
@property
def dummy_image(self):
batch_size = 1
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
return image
@property
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=6,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
return model
@property
def dummy_vq_model(self):
torch.manual_seed(0)
model = VQModel(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=3,
)
return model
def test_inference_superresolution(self):
device = "cpu"
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vqvae = self.dummy_vq_model
ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)
ldm.to(device)
ldm.set_progress_bar_config(disable=None)
init_image = self.dummy_image.to(device)
generator = torch.Generator(device=device).manual_seed(0)
image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
def test_inference_superresolution_fp16(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vqvae = self.dummy_vq_model
# put models in fp16
unet = unet.half()
vqvae = vqvae.half()
ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
init_image = self.dummy_image.to(torch_device)
image = ldm(init_image, num_inference_steps=2, output_type="numpy").images
assert image.shape == (1, 64, 64, 3)
@slow
@require_torch
class LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):
def test_inference_superresolution(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool.png"
)
init_image = init_image.resize((64, 64), resample=PIL_INTERPOLATION["lanczos"])
ldm = LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution", device_map="auto")
ldm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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