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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 gc
import unittest
import numpy as np
import torch
from diffusers import RePaintPipeline, RePaintScheduler, UNet2DModel
from diffusers.utils.testing_utils import load_image, load_numpy, nightly, require_torch_gpu, skip_mps, torch_device
from ...pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class RepaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = RePaintPipeline
params = IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
required_optional_params = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
batch_params = IMAGE_INPAINTING_BATCH_PARAMS
test_cpu_offload = False
def get_dummy_components(self):
torch.manual_seed(0)
torch.manual_seed(0)
unet = UNet2DModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=3,
out_channels=3,
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
)
scheduler = RePaintScheduler()
components = {"unet": unet, "scheduler": scheduler}
return 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 = np.random.RandomState(seed).standard_normal((1, 3, 32, 32))
image = torch.from_numpy(image).to(device=device, dtype=torch.float32)
mask = (image > 0).to(device=device, dtype=torch.float32)
inputs = {
"image": image,
"mask_image": mask,
"generator": generator,
"num_inference_steps": 5,
"eta": 0.0,
"jump_length": 2,
"jump_n_sample": 2,
"output_type": "numpy",
}
return inputs
def test_repaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = RePaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
# RePaint can hardly be made deterministic since the scheduler is currently always
# nondeterministic
@unittest.skip("non-deterministic pipeline")
def test_inference_batch_single_identical(self):
return super().test_inference_batch_single_identical()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass()
@nightly
@require_torch_gpu
class RepaintPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_celebahq(self):
original_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"repaint/celeba_hq_256.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
"repaint/celeba_hq_256_result.npy"
)
model_id = "google/ddpm-ema-celebahq-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = RePaintScheduler.from_pretrained(model_id)
repaint = RePaintPipeline(unet=unet, scheduler=scheduler).to(torch_device)
repaint.set_progress_bar_config(disable=None)
repaint.enable_attention_slicing()
generator = torch.manual_seed(0)
output = repaint(
original_image,
mask_image,
num_inference_steps=250,
eta=0.0,
jump_length=10,
jump_n_sample=10,
generator=generator,
output_type="np",
)
image = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image).mean() < 1e-2
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