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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 CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from ...pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_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)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, 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))
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**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, 64, 64, 3)
expected_slice = np.array([0.4723, 0.5731, 0.3939, 0.5441, 0.5922, 0.4392, 0.5059, 0.4651, 0.4474])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_inpaint_image_tensor(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
out_pil = output.images
inputs = self.get_dummy_inputs(device)
inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0)
inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0)
output = sd_pipe(**inputs)
out_tensor = output.images
assert out_pil.shape == (1, 64, 64, 3)
assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794])
assert np.abs(expected_slice - image_slice).max() < 1e-4
def test_stable_diffusion_inpaint_fp16(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1350, 0.1123, 0.1350, 0.1641, 0.1328, 0.1230, 0.1289, 0.1531, 0.1687])
assert np.abs(expected_slice - image_slice).max() < 5e-2
def test_stable_diffusion_inpaint_pndm(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272])
assert np.abs(expected_slice - image_slice).max() < 1e-4
def test_stable_diffusion_inpaint_k_lms(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None
)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633])
assert np.abs(expected_slice - image_slice).max() < 1e-4
def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
@nightly
@require_torch_gpu
class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def test_inpaint_ddim(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_pndm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_lms(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_inpaint_dpm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 30
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
def test_pil_inputs(self):
im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
im = Image.fromarray(im)
mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
mask = Image.fromarray((mask * 255).astype(np.uint8))
t_mask, t_masked = prepare_mask_and_masked_image(im, mask)
self.assertTrue(isinstance(t_mask, torch.Tensor))
self.assertTrue(isinstance(t_masked, torch.Tensor))
self.assertEqual(t_mask.ndim, 4)
self.assertEqual(t_masked.ndim, 4)
self.assertEqual(t_mask.shape, (1, 1, 32, 32))
self.assertEqual(t_masked.shape, (1, 3, 32, 32))
self.assertTrue(t_mask.dtype == torch.float32)
self.assertTrue(t_masked.dtype == torch.float32)
self.assertTrue(t_mask.min() >= 0.0)
self.assertTrue(t_mask.max() <= 1.0)
self.assertTrue(t_masked.min() >= -1.0)
self.assertTrue(t_masked.min() <= 1.0)
self.assertTrue(t_mask.sum() > 0.0)
def test_np_inputs(self):
im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
im_pil = Image.fromarray(im_np)
mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil)
self.assertTrue((t_mask_np == t_mask_pil).all())
self.assertTrue((t_masked_np == t_masked_pil).all())
def test_torch_3D_2D_inputs(self):
im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
im_np = im_tensor.numpy().transpose(1, 2, 0)
mask_np = mask_tensor.numpy()
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_3D_3D_inputs(self):
im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
im_np = im_tensor.numpy().transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0]
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_4D_2D_inputs(self):
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_4D_3D_inputs(self):
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0]
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_4D_4D_inputs(self):
im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5
im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
mask_np = mask_tensor.numpy()[0][0]
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_batch_4D_3D(self):
im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
mask_nps = [mask.numpy() for mask in mask_tensor]
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
t_mask_np = torch.cat([n[0] for n in nps])
t_masked_np = torch.cat([n[1] for n in nps])
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_torch_batch_4D_4D(self):
im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
mask_tensor = torch.randint(0, 255, (2, 1, 32, 32), dtype=torch.uint8) > 127.5
im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
mask_nps = [mask.numpy()[0] for mask in mask_tensor]
t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
t_mask_np = torch.cat([n[0] for n in nps])
t_masked_np = torch.cat([n[1] for n in nps])
self.assertTrue((t_mask_tensor == t_mask_np).all())
self.assertTrue((t_masked_tensor == t_masked_np).all())
def test_shape_mismatch(self):
# test height and width
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(torch.randn(3, 32, 32), torch.randn(64, 64))
# test batch dim
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 64, 64))
# test batch dim
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 1, 64, 64))
def test_type_mismatch(self):
# test tensors-only
with self.assertRaises(TypeError):
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.rand(3, 32, 32).numpy())
# test tensors-only
with self.assertRaises(TypeError):
prepare_mask_and_masked_image(torch.rand(3, 32, 32).numpy(), torch.rand(3, 32, 32))
def test_channels_first(self):
# test channels first for 3D tensors
with self.assertRaises(AssertionError):
prepare_mask_and_masked_image(torch.rand(32, 32, 3), torch.rand(3, 32, 32))
def test_tensor_range(self):
# test im <= 1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(torch.ones(3, 32, 32) * 2, torch.rand(32, 32))
# test im >= -1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(torch.ones(3, 32, 32) * (-2), torch.rand(32, 32))
# test mask <= 1
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * 2)
# test mask >= 0
with self.assertRaises(ValueError):
prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * -1)
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