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support stable diffusion v2

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Fazzie
2022-12-12 17:35:23 +08:00
parent e99edfcb51
commit cea4292ae5
51 changed files with 5597 additions and 3302 deletions
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17
examples/images/diffusion/ldm/modules/image_degradation/bsrgan_light.py
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@@ -25,7 +25,6 @@ import ldm.modules.image_degradation.utils_image as util
# --------------------------------------------
"""
def modcrop_np(img, sf):
'''
Args:
@@ -254,7 +253,7 @@ def srmd_degradation(x, k, sf=3):
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
@@ -277,7 +276,7 @@ def dpsr_degradation(x, k, sf=3):
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
@@ -290,7 +289,7 @@ def classical_degradation(x, k, sf=3):
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
@@ -335,7 +334,7 @@ def add_blur(img, sf=4):
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
@@ -497,7 +496,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
@@ -531,7 +530,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
@@ -589,7 +588,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
@@ -617,6 +616,8 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
if up:
image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then
example = {"image": image}
return example