Spaces:
Build error
Build error
File size: 6,862 Bytes
7a11626 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
from pathlib import Path
import numpy as np
import torch
from misc import torch_samps_to_imgs
from adapt import Karras, ScoreAdapter, power_schedule
from adapt_gddpm import GuidedDDPM
from adapt_ncsn import NCSN as _NCSN
# from adapt_vesde import VESDE # not included to prevent import conflicts
from adapt_sd import StableDiffusion
from my.utils import tqdm, EventStorage, HeartBeat, EarlyLoopBreak
from my.config import BaseConf, dispatch
from my.utils.seed import seed_everything
class GDDPM(BaseConf):
"""Guided DDPM from OpenAI"""
model: str = "m_lsun_256"
lsun_cat: str = "bedroom"
imgnet_cat: int = -1
def make(self):
args = self.dict()
model = GuidedDDPM(**args)
return model
class SD(BaseConf):
"""Stable Diffusion"""
variant: str = "v1"
v2_highres: bool = False
prompt: str = "a photograph of an astronaut riding a horse"
scale: float = 3.0 # classifier free guidance scale
precision: str = 'autocast'
def make(self):
args = self.dict()
model = StableDiffusion(**args)
return model
class SDE(BaseConf):
def make(self):
args = self.dict()
model = VESDE(**args)
return model
class NCSN(BaseConf):
def make(self):
args = self.dict()
model = _NCSN(**args)
return model
class KarrasGen(BaseConf):
family: str = "gddpm"
gddpm: GDDPM = GDDPM()
sd: SD = SD()
# sde: SDE = SDE()
ncsn: NCSN = NCSN()
batch_size: int = 10
num_images: int = 1250
num_t: int = 40
σ_max: float = 80.0
heun: bool = True
langevin: bool = False
cls_scaling: float = 1.0 # classifier guidance scaling
def run(self):
args = self.dict()
family = args.pop("family")
model = getattr(self, family).make()
self.karras_generate(model, **args)
@staticmethod
def karras_generate(
model: ScoreAdapter,
batch_size, num_images, σ_max, num_t, langevin, heun, cls_scaling,
**kwargs
):
del kwargs # removed extra args
num_batches = num_images // batch_size
fuse = EarlyLoopBreak(5)
with tqdm(total=num_batches) as pbar, \
HeartBeat(pbar) as hbeat, \
EventStorage() as metric:
all_imgs = []
for _ in range(num_batches):
if fuse.on_break():
break
pipeline = Karras.inference(
model, batch_size, num_t,
init_xs=None, heun=heun, σ_max=σ_max,
langevin=langevin, cls_scaling=cls_scaling
)
for imgs in tqdm(pipeline, total=num_t+1, disable=False):
# _std = imgs.std().item()
# print(_std)
hbeat.beat()
pass
if isinstance(model, StableDiffusion):
imgs = model.decode(imgs)
imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
all_imgs.append(imgs)
pbar.update()
all_imgs = np.concatenate(all_imgs, axis=0)
metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
metric.step()
hbeat.done()
class SMLDGen(BaseConf):
family: str = "ncsn"
gddpm: GDDPM = GDDPM()
# sde: SDE = SDE()
ncsn: NCSN = NCSN()
batch_size: int = 16
num_images: int = 16
num_stages: int = 80
num_steps: int = 15
σ_max: float = 80.0
ε: float = 1e-5
def run(self):
args = self.dict()
family = args.pop("family")
model = getattr(self, family).make()
self.smld_generate(model, **args)
@staticmethod
def smld_generate(
model: ScoreAdapter,
batch_size, num_images, num_stages, num_steps, σ_max, ε,
**kwargs
):
num_batches = num_images // batch_size
σs = power_schedule(σ_max, model.σ_min, num_stages)
σs = [model.snap_t_to_nearest_tick(σ)[0] for σ in σs]
fuse = EarlyLoopBreak(5)
with tqdm(total=num_batches) as pbar, \
HeartBeat(pbar) as hbeat, \
EventStorage() as metric:
all_imgs = []
for _ in range(num_batches):
if fuse.on_break():
break
init_xs = torch.rand(batch_size, *model.data_shape(), device=model.device)
if model.samps_centered():
init_xs = init_xs * 2 - 1 # [0, 1] -> [-1, 1]
pipeline = smld_inference(
model, σs, num_steps, ε, init_xs
)
for imgs in tqdm(pipeline, total=(num_stages * num_steps)+1, disable=False):
pbar.set_description(f"{imgs.max().item():.3f}")
metric.put_scalars(
max=imgs.max().item(), min=imgs.min().item(), std=imgs.std().item()
)
metric.step()
hbeat.beat()
pbar.update()
imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
all_imgs.append(imgs)
all_imgs = np.concatenate(all_imgs, axis=0)
metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
metric.step()
hbeat.done()
def smld_inference(model, σs, num_steps, ε, init_xs):
from math import sqrt
# not doing conditioning or cls guidance; for gddpm only lsun works; fine.
xs = init_xs
yield xs
for i in range(len(σs)):
α_i = ε * ((σs[i] / σs[-1]) ** 2)
for _ in range(num_steps):
grad = model.score(xs, σs[i])
z = torch.randn_like(xs)
xs = xs + α_i * grad + sqrt(2 * α_i) * z
yield xs
def load_np_imgs(fname):
fname = Path(fname)
data = np.load(fname)
if fname.suffix == ".npz":
imgs = data['arr_0']
else:
imgs = data
return imgs
def visualize(max_n_imgs=16):
import torchvision.utils as vutils
from imageio import imwrite
from einops import rearrange
all_imgs = load_np_imgs("imgs/step_0.npy")
imgs = all_imgs[:max_n_imgs]
imgs = rearrange(imgs, "N H W C -> N C H W", C=3)
imgs = torch.from_numpy(imgs)
pane = vutils.make_grid(imgs, padding=2, nrow=4)
pane = rearrange(pane, "C H W -> H W C", C=3)
pane = pane.numpy()
imwrite("preview.jpg", pane)
if __name__ == "__main__":
seed_everything(0)
dispatch(KarrasGen)
visualize(16)
|