Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,142 Bytes
7f51798 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 |
import pathlib
from dataclasses import asdict, dataclass
from enum import Enum
from typing import Optional
from omegaconf import OmegaConf
from sgm.inference.helpers import (Img2ImgDiscretizationWrapper, do_img2img,
do_sample)
from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
DPMPP2SAncestralSampler,
EulerAncestralSampler,
EulerEDMSampler,
HeunEDMSampler,
LinearMultistepSampler)
from sgm.util import load_model_from_config
class ModelArchitecture(str, Enum):
SD_2_1 = "stable-diffusion-v2-1"
SD_2_1_768 = "stable-diffusion-v2-1-768"
SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
class Sampler(str, Enum):
EULER_EDM = "EulerEDMSampler"
HEUN_EDM = "HeunEDMSampler"
EULER_ANCESTRAL = "EulerAncestralSampler"
DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
DPMPP2M = "DPMPP2MSampler"
LINEAR_MULTISTEP = "LinearMultistepSampler"
class Discretization(str, Enum):
LEGACY_DDPM = "LegacyDDPMDiscretization"
EDM = "EDMDiscretization"
class Guider(str, Enum):
VANILLA = "VanillaCFG"
IDENTITY = "IdentityGuider"
class Thresholder(str, Enum):
NONE = "None"
@dataclass
class SamplingParams:
width: int = 1024
height: int = 1024
steps: int = 50
sampler: Sampler = Sampler.DPMPP2M
discretization: Discretization = Discretization.LEGACY_DDPM
guider: Guider = Guider.VANILLA
thresholder: Thresholder = Thresholder.NONE
scale: float = 6.0
aesthetic_score: float = 5.0
negative_aesthetic_score: float = 5.0
img2img_strength: float = 1.0
orig_width: int = 1024
orig_height: int = 1024
crop_coords_top: int = 0
crop_coords_left: int = 0
sigma_min: float = 0.0292
sigma_max: float = 14.6146
rho: float = 3.0
s_churn: float = 0.0
s_tmin: float = 0.0
s_tmax: float = 999.0
s_noise: float = 1.0
eta: float = 1.0
order: int = 4
@dataclass
class SamplingSpec:
width: int
height: int
channels: int
factor: int
is_legacy: bool
config: str
ckpt: str
is_guided: bool
model_specs = {
ModelArchitecture.SD_2_1: SamplingSpec(
height=512,
width=512,
channels=4,
factor=8,
is_legacy=True,
config="sd_2_1.yaml",
ckpt="v2-1_512-ema-pruned.safetensors",
is_guided=True,
),
ModelArchitecture.SD_2_1_768: SamplingSpec(
height=768,
width=768,
channels=4,
factor=8,
is_legacy=True,
config="sd_2_1_768.yaml",
ckpt="v2-1_768-ema-pruned.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=False,
config="sd_xl_base.yaml",
ckpt="sd_xl_base_0.9.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=True,
config="sd_xl_refiner.yaml",
ckpt="sd_xl_refiner_0.9.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=False,
config="sd_xl_base.yaml",
ckpt="sd_xl_base_1.0.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=True,
config="sd_xl_refiner.yaml",
ckpt="sd_xl_refiner_1.0.safetensors",
is_guided=True,
),
}
class SamplingPipeline:
def __init__(
self,
model_id: ModelArchitecture,
model_path="checkpoints",
config_path="configs/inference",
device="cuda",
use_fp16=True,
) -> None:
if model_id not in model_specs:
raise ValueError(f"Model {model_id} not supported")
self.model_id = model_id
self.specs = model_specs[self.model_id]
self.config = str(pathlib.Path(config_path, self.specs.config))
self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
self.device = device
self.model = self._load_model(device=device, use_fp16=use_fp16)
def _load_model(self, device="cuda", use_fp16=True):
config = OmegaConf.load(self.config)
model = load_model_from_config(config, self.ckpt)
if model is None:
raise ValueError(f"Model {self.model_id} could not be loaded")
model.to(device)
if use_fp16:
model.conditioner.half()
model.model.half()
return model
def text_to_image(
self,
params: SamplingParams,
prompt: str,
negative_prompt: str = "",
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
value_dict = asdict(params)
value_dict["prompt"] = prompt
value_dict["negative_prompt"] = negative_prompt
value_dict["target_width"] = params.width
value_dict["target_height"] = params.height
return do_sample(
self.model,
sampler,
value_dict,
samples,
params.height,
params.width,
self.specs.channels,
self.specs.factor,
force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
return_latents=return_latents,
filter=None,
)
def image_to_image(
self,
params: SamplingParams,
image,
prompt: str,
negative_prompt: str = "",
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
if params.img2img_strength < 1.0:
sampler.discretization = Img2ImgDiscretizationWrapper(
sampler.discretization,
strength=params.img2img_strength,
)
height, width = image.shape[2], image.shape[3]
value_dict = asdict(params)
value_dict["prompt"] = prompt
value_dict["negative_prompt"] = negative_prompt
value_dict["target_width"] = width
value_dict["target_height"] = height
return do_img2img(
image,
self.model,
sampler,
value_dict,
samples,
force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
return_latents=return_latents,
filter=None,
)
def refiner(
self,
params: SamplingParams,
image,
prompt: str,
negative_prompt: Optional[str] = None,
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
value_dict = {
"orig_width": image.shape[3] * 8,
"orig_height": image.shape[2] * 8,
"target_width": image.shape[3] * 8,
"target_height": image.shape[2] * 8,
"prompt": prompt,
"negative_prompt": negative_prompt,
"crop_coords_top": 0,
"crop_coords_left": 0,
"aesthetic_score": 6.0,
"negative_aesthetic_score": 2.5,
}
return do_img2img(
image,
self.model,
sampler,
value_dict,
samples,
skip_encode=True,
return_latents=return_latents,
filter=None,
)
def get_guider_config(params: SamplingParams):
if params.guider == Guider.IDENTITY:
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
}
elif params.guider == Guider.VANILLA:
scale = params.scale
thresholder = params.thresholder
if thresholder == Thresholder.NONE:
dyn_thresh_config = {
"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
}
else:
raise NotImplementedError
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
"params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
}
else:
raise NotImplementedError
return guider_config
def get_discretization_config(params: SamplingParams):
if params.discretization == Discretization.LEGACY_DDPM:
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
}
elif params.discretization == Discretization.EDM:
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
"params": {
"sigma_min": params.sigma_min,
"sigma_max": params.sigma_max,
"rho": params.rho,
},
}
else:
raise ValueError(f"unknown discretization {params.discretization}")
return discretization_config
def get_sampler_config(params: SamplingParams):
discretization_config = get_discretization_config(params)
guider_config = get_guider_config(params)
sampler = None
if params.sampler == Sampler.EULER_EDM:
return EulerEDMSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=params.s_churn,
s_tmin=params.s_tmin,
s_tmax=params.s_tmax,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.HEUN_EDM:
return HeunEDMSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=params.s_churn,
s_tmin=params.s_tmin,
s_tmax=params.s_tmax,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.EULER_ANCESTRAL:
return EulerAncestralSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=params.eta,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
return DPMPP2SAncestralSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=params.eta,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.DPMPP2M:
return DPMPP2MSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
verbose=True,
)
if params.sampler == Sampler.LINEAR_MULTISTEP:
return LinearMultistepSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
order=params.order,
verbose=True,
)
raise ValueError(f"unknown sampler {params.sampler}!")
|