Spaces:
Running
Running
File size: 14,509 Bytes
a660631 a9864eb a660631 f521e88 a660631 e4d01a7 a660631 b5515fe da031b9 b5515fe da031b9 b5515fe da031b9 b5515fe da031b9 b5515fe a660631 f521e88 a660631 f521e88 a660631 e8325d8 a660631 e8325d8 f521e88 3da112e a660631 2e8f4d7 a660631 f521e88 a660631 e8325d8 a660631 f521e88 a660631 f521e88 a660631 c763397 a660631 f521e88 a660631 c763397 a660631 89efab8 e4d01a7 f521e88 a660631 f521e88 a660631 8fad46e a660631 c763397 a660631 89efab8 e4d01a7 f521e88 a660631 f521e88 a660631 f521e88 a660631 f521e88 a660631 8fad46e a660631 c763397 a660631 89efab8 e4d01a7 f521e88 a660631 f521e88 a660631 8fad46e a660631 c763397 a660631 89efab8 e4d01a7 f521e88 a660631 f521e88 a660631 8fad46e a660631 c763397 a660631 89efab8 e4d01a7 f521e88 cce8954 a660631 f521e88 a660631 f521e88 a660631 da031b9 f521e88 a660631 f521e88 a660631 cce8954 8fad46e cce8954 8fad46e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 |
from __future__ import annotations
import gc
import numpy as np
import PIL.Image
import spaces
import torch
from controlnet_aux.util import HWC3
from diffusers import (
ControlNetModel,
DiffusionPipeline,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
from cv_utils import resize_image
from preprocessor import Preprocessor
from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
CONTROLNET_MODEL_IDS = {
"Canny": "checkpoints/canny/controlnet",
"softedge": "checkpoints/hed/controlnet",
"segmentation": "checkpoints/seg/controlnet",
"depth": "checkpoints/depth/controlnet",
"lineart": "checkpoints/lineart/controlnet",
}
def download_all_controlnet_weights() -> None:
for model_id in CONTROLNET_MODEL_IDS.values():
ControlNetModel.from_pretrained(model_id)
class Model:
def __init__(self, base_model_id: str = "runwayml/stable-diffusion-v1-5", task_name: str = "Canny"):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.base_model_id = ""
self.task_name = ""
self.pipe = self.load_pipe(base_model_id, task_name)
self.preprocessor = Preprocessor()
def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
if (
base_model_id == self.base_model_id
and task_name == self.task_name
and hasattr(self, "pipe")
and self.pipe is not None
):
return self.pipe
model_id = CONTROLNET_MODEL_IDS[task_name]
controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_id, safety_checker=None, controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# if self.device.type == "cuda":
# pipe.disable_xformers_memory_efficient_attention()
pipe.to(self.device)
torch.cuda.empty_cache()
gc.collect()
self.base_model_id = base_model_id
self.task_name = task_name
return pipe
def set_base_model(self, base_model_id: str) -> str:
if not base_model_id or base_model_id == self.base_model_id:
return self.base_model_id
del self.pipe
torch.cuda.empty_cache()
gc.collect()
try:
self.pipe = self.load_pipe(base_model_id, self.task_name)
except Exception:
self.pipe = self.load_pipe(self.base_model_id, self.task_name)
return self.base_model_id
def load_controlnet_weight(self, task_name: str) -> None:
if task_name == self.task_name:
return
if self.pipe is not None and hasattr(self.pipe, "controlnet"):
del self.pipe.controlnet
torch.cuda.empty_cache()
gc.collect()
model_id = CONTROLNET_MODEL_IDS[task_name]
controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)
controlnet.to(self.device)
torch.cuda.empty_cache()
gc.collect()
self.pipe.controlnet = controlnet
self.task_name = task_name
def get_prompt(self, prompt: str, additional_prompt: str) -> str:
if not prompt:
prompt = additional_prompt
else:
prompt = f"{prompt}, {additional_prompt}"
return prompt
@torch.autocast("cuda")
def run_pipe(
self,
prompt: str,
negative_prompt: str,
control_image: PIL.Image.Image,
num_images: int,
num_steps: int,
guidance_scale: float,
seed: int,
) -> list[PIL.Image.Image]:
self.pipe.to(self.device)
self.pipe.controlnet.to(self.device)
generator = torch.Generator().manual_seed(seed)
return self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images,
num_inference_steps=num_steps,
generator=generator,
image=control_image,
).images
@torch.inference_mode()
@spaces.GPU(enable_queue=True)
def process_canny(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
low_threshold: int,
high_threshold: int,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
self.preprocessor.load("Canny")
control_image = self.preprocessor(
image=image, low_threshold=low_threshold, high_threshold=high_threshold, detect_resolution=image_resolution
)
self.load_controlnet_weight("Canny")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x, low_threshold=low_threshold, high_threshold=high_threshold, detect_resolution=image_resolution
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.inference_mode()
@spaces.GPU(enable_queue=True)
def process_softedge(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ["HED", "HED safe"]:
safe = "safe" in preprocessor_name
self.preprocessor.load("HED")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=safe,
)
elif preprocessor_name in ["PidiNet", "PidiNet safe"]:
safe = "safe" in preprocessor_name
self.preprocessor.load("PidiNet")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
safe=safe,
)
else:
raise ValueError
self.load_controlnet_weight("softedge")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
scribble=safe,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.inference_mode()
@spaces.GPU(enable_queue=True)
def process_segmentation(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.preprocessor.load(preprocessor_name)
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
self.load_controlnet_weight("segmentation")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
self.preprocessor.load('UPerNet')
conditions_of_generated_imgs = [
self.preprocessor(
image=np.array(x),
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.inference_mode()
@spaces.GPU(enable_queue=True)
def process_depth(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name == "None":
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
else:
self.preprocessor.load(preprocessor_name)
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
self.load_controlnet_weight("depth")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
return [control_image] * num_images + results + conditions_of_generated_imgs
@torch.inference_mode()
@spaces.GPU(enable_queue=True)
def process_lineart(
self,
image: np.ndarray,
prompt: str,
additional_prompt: str,
negative_prompt: str,
num_images: int,
image_resolution: int,
preprocess_resolution: int,
num_steps: int,
guidance_scale: float,
seed: int,
preprocessor_name: str,
) -> list[PIL.Image.Image]:
if image is None:
raise ValueError
if image_resolution > MAX_IMAGE_RESOLUTION:
raise ValueError
if num_images > MAX_NUM_IMAGES:
raise ValueError
if preprocessor_name in ["None", "None (anime)"]:
image = 255 - HWC3(image)
image = resize_image(image, resolution=image_resolution)
control_image = PIL.Image.fromarray(image)
elif preprocessor_name in ["Lineart", "Lineart coarse"]:
coarse = "coarse" in preprocessor_name
self.preprocessor.load("Lineart")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
coarse=coarse,
)
elif preprocessor_name == "Lineart (anime)":
self.preprocessor.load("LineartAnime")
control_image = self.preprocessor(
image=image,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
)
# NOTE: We still use the general lineart model
if "anime" in preprocessor_name:
self.load_controlnet_weight("lineart_anime")
else:
self.load_controlnet_weight("lineart")
results = self.run_pipe(
prompt=self.get_prompt(prompt, additional_prompt),
negative_prompt=negative_prompt,
control_image=control_image,
num_images=num_images,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
)
self.preprocessor.load("Lineart")
conditions_of_generated_imgs = [
self.preprocessor(
image=x,
image_resolution=image_resolution,
detect_resolution=preprocess_resolution,
) for x in results
]
control_image = PIL.Image.fromarray((255 - np.array(control_image)).astype(np.uint8))
conditions_of_generated_imgs = [PIL.Image.fromarray((255 - np.array(x)).astype(np.uint8)) for x in conditions_of_generated_imgs]
return [control_image] * num_images + results + conditions_of_generated_imgs
|