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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 torch
from diffusers import IFImg2ImgSuperResolutionPipeline
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
pipeline_class = IFImg2ImgSuperResolutionPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"})
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
def get_dummy_components(self):
return self._get_superresolution_dummy_components()
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_float16(expected_max_diff=1e-1)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
def test_save_load_local(self):
self._test_save_load_local()
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,
)
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