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backend add outpainting

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Qing 2023-08-30 13:28:31 +08:00
parent 0b3a9a68a2
commit c7c309cb89
5 changed files with 354 additions and 13 deletions
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95
lama_cleaner/model/base.py
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@ -12,6 +12,7 @@ from lama_cleaner.helper import (
pad_img_to_modulo,
switch_mps_device,
)
from lama_cleaner.model.g_diffuser_bot import expand_image, np_img_grey_to_rgb
from lama_cleaner.schema import Config, HDStrategy
@ -266,7 +267,12 @@ class DiffusionInpaintModel(InpaintModel):
"""
# boxes = boxes_from_mask(mask)
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
if config.croper_is_outpainting:
inpaint_result = self._do_outpainting(image, config)
else:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(
image, mask, config
)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
@ -275,6 +281,79 @@ class DiffusionInpaintModel(InpaintModel):
return inpaint_result
def _do_outpainting(self, image, config: Config):
# cropper 和 image 在同一个坐标系下，croper_x/y 可能为负数
# 从 image 中 crop 出 outpainting 区域
image_h, image_w = image.shape[:2]
cropper_l = config.croper_x
cropper_t = config.croper_y
cropper_r = config.croper_x + config.croper_width
cropper_b = config.croper_y + config.croper_height
image_l = 0
image_t = 0
image_r = image_w
image_b = image_h
# 类似求 IOU
l = max(cropper_l, image_l)
t = max(cropper_t, image_t)
r = min(cropper_r, image_r)
b = min(cropper_b, image_b)
assert (
0 <= l < r and 0 <= t < b
), f"cropper and image not overlap, {l},{t},{r},{b}"
cropped_image = image[t:b, l:r, :]
padding_l = max(0, image_l - cropper_l)
padding_t = max(0, image_t - cropper_t)
padding_r = max(0, cropper_r - image_r)
padding_b = max(0, cropper_b - image_b)
zero_padding_count = [padding_l, padding_t, padding_r, padding_b].count(0)
if zero_padding_count not in [0, 3]:
logger.warning(
f"padding count({zero_padding_count}) not 0 or 3, may result in bad edge outpainting"
)
expanded_image, mask_image = expand_image(
cropped_image,
left=padding_l,
top=padding_t,
right=padding_r,
bottom=padding_b,
softness=config.sd_outpainting_softness,
space=config.sd_outpainting_space,
)
# 最终扩大了的 image, BGR
expanded_cropped_result_image = self._scaled_pad_forward(
expanded_image, mask_image, config
)
# RGB -> BGR
outpainting_image = cv2.copyMakeBorder(
image,
left=padding_l,
top=padding_t,
right=padding_r,
bottom=padding_b,
borderType=cv2.BORDER_CONSTANT,
value=0,
)[:, :, ::-1]
# 把 cropped_result_image 贴到 outpainting_image 上，这一步不需要 blend
paste_t = 0 if config.croper_y < 0 else config.croper_y
paste_l = 0 if config.croper_x < 0 else config.croper_x
outpainting_image[
paste_t : paste_t + expanded_cropped_result_image.shape[0],
paste_l : paste_l + expanded_cropped_result_image.shape[1],
:,
] = expanded_cropped_result_image
return outpainting_image
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
@ -291,8 +370,14 @@ class DiffusionInpaintModel(InpaintModel):
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][
original_pixel_indices
]
# blend result, copy from g_diffuser_bot
# mask_rgb = 1.0 - np_img_grey_to_rgb(mask / 255.0)
# inpaint_result = np.clip(
# inpaint_result * (1.0 - mask_rgb) + image * mask_rgb, 0.0, 255.0
# )
# original_pixel_indices = mask < 127
# inpaint_result[original_pixel_indices] = image[:, :, ::-1][
# original_pixel_indices
# ]
return inpaint_result

181
lama_cleaner/model/g_diffuser_bot.py Normal file
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@ -0,0 +1,181 @@
# code copy from: https://github.com/parlance-zz/g-diffuser-bot
import cv2
import numpy as np
def np_img_grey_to_rgb(data):
if data.ndim == 3:
return data
return np.expand_dims(data, 2) * np.ones((1, 1, 3))
def convolve(data1, data2): # fast convolution with fft
if data1.ndim != data2.ndim: # promote to rgb if mismatch
if data1.ndim < 3:
data1 = np_img_grey_to_rgb(data1)
if data2.ndim < 3:
data2 = np_img_grey_to_rgb(data2)
return ifft2(fft2(data1) * fft2(data2))
def fft2(data):
if data.ndim > 2: # multiple channels
out_fft = np.zeros(
(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
)
for c in range(data.shape[2]):
c_data = data[:, :, c]
out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
else: # single channel
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
return out_fft
def ifft2(data):
if data.ndim > 2: # multiple channels
out_ifft = np.zeros(
(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
)
for c in range(data.shape[2]):
c_data = data[:, :, c]
out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
else: # single channel
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
return out_ifft
def get_gradient_kernel(width, height, std=3.14, mode="linear"):
window_scale_x = float(
width / min(width, height)
) # for non-square aspect ratios we still want a circular kernel
window_scale_y = float(height / min(width, height))
if mode == "gaussian":
x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
kx = np.exp(-x * x * std)
if window_scale_x != window_scale_y:
y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
ky = np.exp(-y * y * std)
else:
y = x
ky = kx
return np.outer(kx, ky)
elif mode == "linear":
x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
if window_scale_x != window_scale_y:
y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
else:
y = x
return np.clip(1.0 - np.sqrt(np.add.outer(x * x, y * y)) * std / 3.14, 0.0, 1.0)
else:
raise Exception("Error: Unknown mode in get_gradient_kernel: {0}".format(mode))
def image_blur(data, std=3.14, mode="linear"):
width = data.shape[0]
height = data.shape[1]
kernel = get_gradient_kernel(width, height, std, mode=mode)
return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel))))
def soften_mask(np_rgba_image, softness, space):
if softness == 0:
return np_rgba_image
softness = min(softness, 1.0)
space = np.clip(space, 0.0, 1.0)
original_max_opacity = np.max(np_rgba_image[:, :, 3])
out_mask = np_rgba_image[:, :, 3] <= 0.0
blurred_mask = image_blur(np_rgba_image[:, :, 3], 3.5 / softness, mode="linear")
blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0)
np_rgba_image[
:, :, 3
] *= blurred_mask # preserve partial opacity in original input mask
np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # renormalize
np_rgba_image[:, :, 3] = np.clip(
np_rgba_image[:, :, 3] - space, 0.0, 1.0
) # make space
np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # and renormalize again
np_rgba_image[:, :, 3] *= original_max_opacity # restore original max opacity
return np_rgba_image
def expand_image(
cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float
):
origin_h, origin_w = cv2_img.shape[:2]
new_width = cv2_img.shape[1] + left + right
new_height = cv2_img.shape[0] + top + bottom
new_img = np.zeros((new_height, new_width, 4), np.uint8) # expanded image is rgba
print(
"Expanding input image from {0}x{1} to {2}x{3}".format(
cv2_img.shape[1], cv2_img.shape[0], new_width, new_height
)
)
if cv2_img.shape[2] == 3: # rgb input image
new_img[
top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 0:3
] = cv2_img
new_img[
top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 3
] = 255 # fully opaque
elif cv2_img.shape[2] == 4: # rgba input image
new_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = cv2_img
else:
raise Exception(
"Unsupported image format: {0} channels".format(cv2_img.shape[2])
)
if softness > 0.0:
new_img = soften_mask(new_img / 255.0, softness / 100.0, space / 100.0)
new_img = (np.clip(new_img, 0.0, 1.0) * 255.0).astype(np.uint8)
mask_image = 255.0 - new_img[:, :, 3] # extract mask from alpha channel and invert
rgb_init_image = (
0.0 + new_img[:, :, 0:3]
) # strip mask from init_img leaving only rgb channels
hard_mask = np.zeros_like(cv2_img[:, :, 0])
if top != 0:
hard_mask[0 : origin_h // 2, :] = 255
if bottom != 0:
hard_mask[origin_h // 2 :, :] = 255
if left != 0:
hard_mask[:, 0 : origin_w // 2] = 255
if right != 0:
hard_mask[:, origin_w // 2 :] = 255
hard_mask = cv2.copyMakeBorder(
hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255
)
mask_image = np.where(hard_mask > 0, mask_image, 0)
return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8)
if __name__ == "__main__":
from pathlib import Path
current_dir = Path(__file__).parent.absolute().resolve()
image_path = current_dir.parent / "tests" / "bunny.jpeg"
init_image = cv2.imread(str(image_path))
init_image, mask_image = expand_image(
init_image,
200,
200,
0,
0,
60,
50,
)
print(mask_image.dtype, mask_image.min(), mask_image.max())
print(init_image.dtype, init_image.min(), init_image.max())
mask_image = mask_image.astype(np.uint8)
init_image = init_image.astype(np.uint8)
cv2.imwrite("expanded_image.png", init_image)
cv2.imwrite("expanded_mask.png", mask_image)

5
lama_cleaner/schema.py
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@ -55,6 +55,7 @@ class Config(BaseModel):
# Crop image to this size before doing sd inpainting
# The value is always on the original image scale
use_croper: bool = False
croper_is_outpainting: bool = False
croper_x: int = None
croper_y: int = None
croper_height: int = None
@ -78,6 +79,10 @@ class Config(BaseModel):
sd_seed: int = 42
sd_match_histograms: bool = False
# out-painting
sd_outpainting_softness: float = 30.0
sd_outpainting_space: float = 50.0
# Configs for opencv inpainting
# opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07
cv2_flag: str = "INPAINT_NS"

70
lama_cleaner/tests/test_outpainting.py Normal file
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@ -0,0 +1,70 @@
import os
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
from pathlib import Path
import pytest
import torch
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import HDStrategy, SDSampler
from lama_cleaner.tests.test_model import get_config, assert_equal
current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / "result"
save_dir.mkdir(exist_ok=True, parents=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
@pytest.mark.parametrize("sd_device", ["mps"])
@pytest.mark.parametrize(
"rect",
[
[0, -100, 512, 512 - 128 + 100],
[0, 128, 512, 512 - 128 + 100],
[128, 0, 512 - 128 + 100, 512],
[-100, 0, 512 - 128 + 100, 512],
[0, 0, 512, 512 + 200],
[0, 0, 512 + 200, 512],
[-100, -100, 512 + 200, 512 + 200],
],
)
def test_sdxl_outpainting(sd_device, rect):
def callback(i, t, latents):
pass
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=True,
sd_cpu_textencoder=False,
callback=callback,
)
cfg = get_config(
HDStrategy.ORIGINAL,
prompt="a dog sitting on a bench in the park",
sd_steps=30,
use_croper=True,
croper_is_outpainting=True,
croper_x=rect[0],
croper_y=rect[1],
croper_width=rect[2],
croper_height=rect[3],
sd_guidance_scale=14,
sd_sampler=SDSampler.dpm_plus_plus,
)
assert_equal(
model,
cfg,
f"sd15_outpainting_dpm++_{'_'.join(map(str, rect))}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)