File size: 5,794 Bytes
5a8a838 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
import os
import torch
import numpy as np
import cv2
from PIL import Image
from torch.nn.functional import interpolate
from omegaconf import OmegaConf
from sgm.util import instantiate_from_config
def get_state_dict(d):
return d.get('state_dict', d)
def load_state_dict(ckpt_path, location='cpu'):
_, extension = os.path.splitext(ckpt_path)
if extension.lower() == ".safetensors":
import safetensors.torch
state_dict = safetensors.torch.load_file(ckpt_path, device=location)
else:
state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
state_dict = get_state_dict(state_dict)
print(f'Loaded state_dict from [{ckpt_path}]')
return state_dict
def create_model(config_path):
config = OmegaConf.load(config_path)
model = instantiate_from_config(config.model).cpu()
print(f'Loaded model config from [{config_path}]')
return model
def create_SUPIR_model(config_path, SUPIR_sign=None, load_default_setting=False):
config = OmegaConf.load(config_path)
model = instantiate_from_config(config.model).cpu()
print(f'Loaded model config from [{config_path}]')
if config.SDXL_CKPT is not None:
model.load_state_dict(load_state_dict(config.SDXL_CKPT), strict=False)
if config.SUPIR_CKPT is not None:
model.load_state_dict(load_state_dict(config.SUPIR_CKPT), strict=False)
if SUPIR_sign is not None:
assert SUPIR_sign in ['F', 'Q']
if SUPIR_sign == 'F':
model.load_state_dict(load_state_dict(config.SUPIR_CKPT_F), strict=False)
elif SUPIR_sign == 'Q':
model.load_state_dict(load_state_dict(config.SUPIR_CKPT_Q), strict=False)
if load_default_setting:
default_setting = config.default_setting
return model, default_setting
return model
def load_QF_ckpt(config_path):
config = OmegaConf.load(config_path)
ckpt_F = torch.load(config.SUPIR_CKPT_F, map_location='cpu')
ckpt_Q = torch.load(config.SUPIR_CKPT_Q, map_location='cpu')
return ckpt_Q, ckpt_F
def PIL2Tensor(img, upsacle=1, min_size=1024, fix_resize=None):
'''
PIL.Image -> Tensor[C, H, W], RGB, [-1, 1]
'''
# size
w, h = img.size
w *= upsacle
h *= upsacle
w0, h0 = round(w), round(h)
if min(w, h) < min_size:
_upsacle = min_size / min(w, h)
w *= _upsacle
h *= _upsacle
if fix_resize is not None:
_upsacle = fix_resize / min(w, h)
w *= _upsacle
h *= _upsacle
w0, h0 = round(w), round(h)
w = int(np.round(w / 64.0)) * 64
h = int(np.round(h / 64.0)) * 64
x = img.resize((w, h), Image.BICUBIC)
x = np.array(x).round().clip(0, 255).astype(np.uint8)
x = x / 255 * 2 - 1
x = torch.tensor(x, dtype=torch.float32).permute(2, 0, 1)
return x, h0, w0
def Tensor2PIL(x, h0, w0):
'''
Tensor[C, H, W], RGB, [-1, 1] -> PIL.Image
'''
x = x.unsqueeze(0)
x = interpolate(x, size=(h0, w0), mode='bicubic')
x = (x.squeeze(0).permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
return Image.fromarray(x)
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def upscale_image(input_image, upscale, min_size=None, unit_resolution=64):
H, W, C = input_image.shape
H = float(H)
W = float(W)
H *= upscale
W *= upscale
if min_size is not None:
if min(H, W) < min_size:
_upsacle = min_size / min(W, H)
W *= _upsacle
H *= _upsacle
H = int(np.round(H / unit_resolution)) * unit_resolution
W = int(np.round(W / unit_resolution)) * unit_resolution
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if upscale > 1 else cv2.INTER_AREA)
img = img.round().clip(0, 255).astype(np.uint8)
return img
def fix_resize(input_image, size=512, unit_resolution=64):
H, W, C = input_image.shape
H = float(H)
W = float(W)
upscale = size / min(H, W)
H *= upscale
W *= upscale
H = int(np.round(H / unit_resolution)) * unit_resolution
W = int(np.round(W / unit_resolution)) * unit_resolution
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if upscale > 1 else cv2.INTER_AREA)
img = img.round().clip(0, 255).astype(np.uint8)
return img
def Numpy2Tensor(img):
'''
np.array[H, w, C] [0, 255] -> Tensor[C, H, W], RGB, [-1, 1]
'''
# size
img = np.array(img) / 255 * 2 - 1
img = torch.tensor(img, dtype=torch.float32).permute(2, 0, 1)
return img
def Tensor2Numpy(x, h0=None, w0=None):
'''
Tensor[C, H, W], RGB, [-1, 1] -> PIL.Image
'''
if h0 is not None and w0 is not None:
x = x.unsqueeze(0)
x = interpolate(x, size=(h0, w0), mode='bicubic')
x = x.squeeze(0)
x = (x.permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
return x
def convert_dtype(dtype_str):
if dtype_str == 'fp32':
return torch.float32
elif dtype_str == 'fp16':
return torch.float16
elif dtype_str == 'bf16':
return torch.bfloat16
else:
raise NotImplementedError
|