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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 IFInpaintingPipeline | |
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_INPAINTING_BATCH_PARAMS, | |
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS, | |
) | |
from ..test_pipelines_common import PipelineTesterMixin | |
from . import IFPipelineTesterMixin | |
class IFInpaintingPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase): | |
pipeline_class = IFInpaintingPipeline | |
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"} | |
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS | |
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"} | |
def get_dummy_components(self): | |
return self._get_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) | |
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
inputs = { | |
"prompt": "A painting of a squirrel eating a burger", | |
"image": image, | |
"mask_image": mask_image, | |
"generator": generator, | |
"num_inference_steps": 2, | |
"output_type": "numpy", | |
} | |
return inputs | |
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() | |
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, | |
) | |