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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 random | |
import unittest | |
import numpy as np | |
import torch | |
from PIL import Image | |
from transformers import CLIPImageProcessor, CLIPVisionConfig | |
from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel | |
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder | |
from diffusers.utils import floats_tensor, load_image, slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
from ...pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS | |
from ...test_pipelines_common import PipelineTesterMixin | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = PaintByExamplePipeline | |
params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS | |
batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS | |
def get_dummy_components(self): | |
torch.manual_seed(0) | |
unet = UNet2DConditionModel( | |
block_out_channels=(32, 64), | |
layers_per_block=2, | |
sample_size=32, | |
in_channels=9, | |
out_channels=4, | |
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
cross_attention_dim=32, | |
) | |
scheduler = PNDMScheduler(skip_prk_steps=True) | |
torch.manual_seed(0) | |
vae = AutoencoderKL( | |
block_out_channels=[32, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=4, | |
) | |
torch.manual_seed(0) | |
config = CLIPVisionConfig( | |
hidden_size=32, | |
projection_dim=32, | |
intermediate_size=37, | |
layer_norm_eps=1e-05, | |
num_attention_heads=4, | |
num_hidden_layers=5, | |
image_size=32, | |
patch_size=4, | |
) | |
image_encoder = PaintByExampleImageEncoder(config, proj_size=32) | |
feature_extractor = CLIPImageProcessor(crop_size=32, size=32) | |
components = { | |
"unet": unet, | |
"scheduler": scheduler, | |
"vae": vae, | |
"image_encoder": image_encoder, | |
"safety_checker": None, | |
"feature_extractor": feature_extractor, | |
} | |
return components | |
def convert_to_pt(self, image): | |
image = np.array(image.convert("RGB")) | |
image = image[None].transpose(0, 3, 1, 2) | |
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
return image | |
def get_dummy_inputs(self, device="cpu", seed=0): | |
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched | |
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
image = image.cpu().permute(0, 2, 3, 1)[0] | |
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64)) | |
example_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32)) | |
if str(device).startswith("mps"): | |
generator = torch.manual_seed(seed) | |
else: | |
generator = torch.Generator(device=device).manual_seed(seed) | |
inputs = { | |
"example_image": example_image, | |
"image": init_image, | |
"mask_image": mask_image, | |
"generator": generator, | |
"num_inference_steps": 2, | |
"guidance_scale": 6.0, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_paint_by_example_inpaint(self): | |
components = self.get_dummy_components() | |
# make sure here that pndm scheduler skips prk | |
pipe = PaintByExamplePipeline(**components) | |
pipe = pipe.to("cpu") | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inputs() | |
output = pipe(**inputs) | |
image = output.images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 64, 64, 3) | |
expected_slice = np.array([0.4701, 0.5555, 0.3994, 0.5107, 0.5691, 0.4517, 0.5125, 0.4769, 0.4539]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_paint_by_example_image_tensor(self): | |
device = "cpu" | |
inputs = self.get_dummy_inputs() | |
inputs.pop("mask_image") | |
image = self.convert_to_pt(inputs.pop("image")) | |
mask_image = image.clamp(0, 1) / 2 | |
# make sure here that pndm scheduler skips prk | |
pipe = PaintByExamplePipeline(**self.get_dummy_components()) | |
pipe = pipe.to(device) | |
pipe.set_progress_bar_config(disable=None) | |
output = pipe(image=image, mask_image=mask_image[:, 0], **inputs) | |
out_1 = output.images | |
image = image.cpu().permute(0, 2, 3, 1)[0] | |
mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0] | |
image = Image.fromarray(np.uint8(image)).convert("RGB") | |
mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB") | |
output = pipe(**self.get_dummy_inputs()) | |
out_2 = output.images | |
assert out_1.shape == (1, 64, 64, 3) | |
assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2 | |
class PaintByExamplePipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_paint_by_example(self): | |
# make sure here that pndm scheduler skips prk | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/paint_by_example/dog_in_bucket.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/paint_by_example/mask.png" | |
) | |
example_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/paint_by_example/panda.jpg" | |
) | |
pipe = PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example") | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
generator = torch.manual_seed(321) | |
output = pipe( | |
image=init_image, | |
mask_image=mask_image, | |
example_image=example_image, | |
generator=generator, | |
guidance_scale=5.0, | |
num_inference_steps=50, | |
output_type="np", | |
) | |
image = output.images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |