File size: 17,187 Bytes
15acbf0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from diffusers.models import UNet2DModel, VQModel
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
import copy
class LDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(self, vae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler, torch_dtype=torch.float16):
super().__init__()
self.register_modules(vae=vae, unet=unet, scheduler=scheduler)
self.torch_dtype = torch_dtype
@torch.no_grad()
def __call__(
self,
batch_size: int = 8,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
eta: float = 0.0,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.model.ImagePipelineOutput`] if `return_dict` is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
"""
if not isinstance(self.unet.config.sample_size,tuple):
self.unet.config.sample_size = (self.unet.config.sample_size,self.unet.config.sample_size)
latents = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1]),
generator=generator,
)
latents = latents.to(self.device).type(self.torch_dtype)
# scale the initial noise by the standard deviation required by the scheduler (need to check)
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latent_model_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VAE
latents /= self.vae.config.scaling_factor#(0.18215)
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
class SDMLDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(self, vae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler, torch_dtype=torch.float16, resolution=512, resolution_type="city"):
super().__init__()
self.register_modules(vae=vae, unet=unet, scheduler=scheduler)
self.torch_dtype = torch_dtype
self.resolution = resolution
self.resolution_type = resolution_type
@torch.no_grad()
def __call__(
self,
segmap = None,
batch_size: int = 8,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
eta: float = 0.0,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
every_step_save: int = None,
s: int = 1,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.model.ImagePipelineOutput`] if `return_dict` is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
"""
# self.unet.config.sample_size = (64, 64) # (135,180)
# self.unet.config.sample_size = (135,180)
if self.resolution_type == "crack":
self.unet.config.sample_size = (64,64)
elif self.resolution_type == "crack_256":
self.unet.config.sample_size = (256,256)
else:
sc = 1080 // self.resolution
latent_size = (self.resolution // 4, 1440 // (sc*4))
self.unet.config.sample_size = latent_size
#
if not isinstance(self.unet.config.sample_size, tuple):
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
if segmap is None:
print("Didn't inpute any segmap, use the empty as the input")
segmap = torch.zeros(batch_size,self.unet.config.segmap_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1])
segmap = segmap.to(self.device).type(self.torch_dtype)
latents = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1]),
generator=generator,
)
latents = latents.to(self.device).type(self.torch_dtype)
# scale the initial noise by the standard deviation required by the scheduler (need to check)
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps=num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
step_latent = []
learn_sigma = True if hasattr(self.scheduler, "variance_type") else False
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latent_model_input, segmap, t).sample
# compute the previous noisy sample x_t -> x_t-1
if learn_sigma and "learn" in self.scheduler.variance_type:
model_pred, var_pred = torch.split(noise_prediction, latents.shape[1], dim=1)
else:
model_pred = noise_prediction
if s > 1.0:
model_output_zero = self.unet(latent_model_input, torch.zeros_like(segmap), t).sample
if learn_sigma and "learn" in self.scheduler.variance_type:
model_output_zero,_ = torch.split(model_output_zero, latents.shape[1], dim=1)
model_pred = model_pred + s * (model_pred - model_output_zero)
if learn_sigma and "learn" in self.scheduler.variance_type:
recombined = torch.cat((model_pred, var_pred), dim=1)
# when apply different scheduler, mean only !!
if learn_sigma and "learn" in self.scheduler.variance_type:
latents = self.scheduler.step(recombined, t, latents, **extra_kwargs).prev_sample
else:
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
if every_step_save is not None:
if (i+1) % every_step_save == 0:
step_latent.append(copy.deepcopy(latents))
# decode the image latents with the VAE
if every_step_save is not None:
image = []
for i, l in enumerate(step_latent):
l /= self.vae.config.scaling_factor # (0.18215)
#latents /= 7.706491063029163
l = self.vae.decode(l, segmap)
l = (l / 2 + 0.5).clamp(0, 1)
l = l.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
l = self.numpy_to_pil(l)
image.append(l)
else:
latents /= self.vae.config.scaling_factor#(0.18215)
#latents /= 7.706491063029163
# image = self.vae.decode(latents, segmap).sample
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
class SDMPipeline(DiffusionPipeline):
r"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vae ([`VQModel`]):
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(self, unet: UNet2DModel, scheduler: DDIMScheduler, torch_dtype=torch.float16, vae=None):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
self.torch_dtype = torch_dtype
@torch.no_grad()
def __call__(
self,
segmap = None,
batch_size: int = 8,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
eta: float = 0.0,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
s: int = 1,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.model.ImagePipelineOutput`] if `return_dict` is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
"""
self.unet.config.sample_size = (270,360)
if not isinstance(self.unet.config.sample_size, tuple):
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
if segmap is None:
print("Didn't inpute any segmap, use the empty as the input")
segmap = torch.zeros(batch_size,self.unet.config.segmap_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1])
segmap = segmap.to(self.device).type(self.torch_dtype)
latents = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1]),
generator=generator,
)
latents = latents.to(self.device).type(self.torch_dtype)
# scale the initial noise by the standard deviation required by the scheduler (need to check)
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latent_model_input, segmap, t).sample
#noise_prediction = noise_prediction[]
if s > 1.0:
model_output_zero = self.unet(latent_model_input, torch.zeros_like(segmap), t).sample
noise_prediction[:, :3] = model_output_zero[:, :3] + s * (noise_prediction[:, :3] - model_output_zero[:, :3])
#noise_prediction = noise_prediction[:, :3]
# compute the previous noisy sample x_t -> x_t-1
#breakpoint()
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VAE
# latents /= self.vae.config.scaling_factor#(0.18215)
# image = self.vae.decode(latents).sample
image = latents
#image = (image + 1) / 2.0
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
|