File size: 11,501 Bytes
86248f3 19b3da3 86248f3 1bc457e 19b3da3 86248f3 19b3da3 1bc457e 19b3da3 1bc457e 19b3da3 b71808f 19b3da3 b71808f 19b3da3 a3d6c18 86248f3 b71808f 19b3da3 b71808f 1bc457e 19b3da3 a3d6c18 19b3da3 a3d6c18 19b3da3 b71808f 19b3da3 1bc457e 19b3da3 b71808f 19b3da3 1bc457e 19b3da3 b71808f 19b3da3 1bc457e 19b3da3 b71808f 19b3da3 86248f3 1bc457e 86248f3 b71808f 86248f3 1bc457e 86248f3 b71808f 86248f3 19b3da3 1bc457e 19b3da3 1bc457e 19b3da3 86248f3 a3d6c18 86248f3 a3d6c18 86248f3 a3d6c18 86248f3 19b3da3 f256b62 19b3da3 86248f3 a3d6c18 86248f3 19b3da3 1bc457e 19b3da3 |
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 |
from typing import List, Union
import cv2
import numpy as np
import torch
from controlnet_aux import HEDdetector, LineartDetector, OpenposeDetector
from diffusers import (ControlNetModel, DiffusionPipeline,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler)
from PIL import Image
from torch.nn import Linear
from tqdm import gui
from internals.data.result import Result
from internals.pipelines.commons import AbstractPipeline
from internals.pipelines.tileUpscalePipeline import \
StableDiffusionControlNetImg2ImgPipeline
from internals.util.cache import clear_cuda_and_gc
from internals.util.commons import download_image
from internals.util.config import get_hf_cache_dir, get_hf_token, get_model_dir
class ControlNet(AbstractPipeline):
__current_task_name = ""
__loaded = False
def load(self):
if self.__loaded:
return
if not self.controlnet:
self.load_pose()
# controlnet pipeline for tile upscaler
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
get_model_dir(),
controlnet=self.controlnet,
torch_dtype=torch.float16,
use_auth_token=get_hf_token(),
cache_dir=get_hf_cache_dir(),
).to("cuda")
# pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
self.pipe = pipe
# controlnet pipeline for canny and pose
pipe2 = StableDiffusionControlNetPipeline(**pipe.components).to("cuda")
pipe2.scheduler = UniPCMultistepScheduler.from_config(pipe2.scheduler.config)
pipe2.enable_xformers_memory_efficient_attention()
self.pipe2 = pipe2
self.__loaded = True
def load_canny(self):
if self.__current_task_name == "canny":
return
canny = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_canny",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
self.__current_task_name = "canny"
self.controlnet = canny
self.load()
if hasattr(self, "pipe"):
self.pipe.controlnet = canny
if hasattr(self, "pipe2"):
self.pipe2.controlnet = canny
clear_cuda_and_gc()
def load_pose(self):
if self.__current_task_name == "pose":
return
pose = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
self.__current_task_name = "pose"
self.controlnet = pose
self.load()
if hasattr(self, "pipe"):
self.pipe.controlnet = pose
if hasattr(self, "pipe2"):
self.pipe2.controlnet = pose
clear_cuda_and_gc()
def load_tile_upscaler(self):
if self.__current_task_name == "tile_upscaler":
return
tile_upscaler = ControlNetModel.from_pretrained(
"lllyasviel/control_v11f1e_sd15_tile",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
self.__current_task_name = "tile_upscaler"
self.controlnet = tile_upscaler
self.load()
if hasattr(self, "pipe"):
self.pipe.controlnet = tile_upscaler
if hasattr(self, "pipe2"):
self.pipe2.controlnet = tile_upscaler
clear_cuda_and_gc()
def load_scribble(self):
if self.__current_task_name == "scribble":
return
scribble = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_scribble",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
self.__current_task_name = "scribble"
self.controlnet = scribble
self.load()
if hasattr(self, "pipe"):
self.pipe.controlnet = scribble
if hasattr(self, "pipe2"):
self.pipe2.controlnet = scribble
clear_cuda_and_gc()
def load_linearart(self):
if self.__current_task_name == "linearart":
return
linearart = ControlNetModel.from_pretrained(
"ControlNet-1-1-preview/control_v11p_sd15_lineart",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
self.__current_task_name = "linearart"
self.controlnet = linearart
self.load()
if hasattr(self, "pipe"):
self.pipe.controlnet = linearart
if hasattr(self, "pipe2"):
self.pipe2.controlnet = linearart
clear_cuda_and_gc()
def cleanup(self):
if hasattr(self, "pipe"):
self.pipe.controlnet = None
if hasattr(self, "pipe2"):
self.pipe2.controlnet = None
self.controlnet = None
del self.controlnet
self.__current_task_name = ""
clear_cuda_and_gc()
@torch.inference_mode()
def process_canny(
self,
prompt: List[str],
imageUrl: str,
seed: int,
steps: int,
negative_prompt: List[str],
guidance_scale: float,
height: int,
width: int,
):
if self.__current_task_name != "canny":
raise Exception("ControlNet is not loaded with canny model")
torch.manual_seed(seed)
init_image = download_image(imageUrl).resize((width, height))
init_image = self.__canny_detect_edge(init_image)
result = self.pipe2.__call__(
prompt=prompt,
image=init_image,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
negative_prompt=negative_prompt,
num_inference_steps=steps,
height=height,
width=width,
)
return Result.from_result(result)
@torch.inference_mode()
def process_pose(
self,
prompt: List[str],
image: List[Image.Image],
seed: int,
steps: int,
guidance_scale: float,
negative_prompt: List[str],
height: int,
width: int,
):
if self.__current_task_name != "pose":
raise Exception("ControlNet is not loaded with pose model")
torch.manual_seed(seed)
result = self.pipe2.__call__(
prompt=prompt,
image=image,
num_images_per_prompt=1,
num_inference_steps=steps,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
height=height,
width=width,
)
return Result.from_result(result)
@torch.inference_mode()
def process_tile_upscaler(
self,
imageUrl: str,
prompt: str,
negative_prompt: str,
steps: int,
seed: int,
height: int,
width: int,
resize_dimension: int,
guidance_scale: float,
):
if self.__current_task_name != "tile_upscaler":
raise Exception("ControlNet is not loaded with tile_upscaler model")
torch.manual_seed(seed)
init_image = download_image(imageUrl).resize((width, height))
condition_image = self.__resize_for_condition_image(
init_image, resize_dimension
)
result = self.pipe.__call__(
image=condition_image,
prompt=prompt,
controlnet_conditioning_image=condition_image,
num_inference_steps=steps,
negative_prompt=negative_prompt,
height=condition_image.size[1],
width=condition_image.size[0],
guidance_scale=guidance_scale,
)
return Result.from_result(result)
@torch.inference_mode()
def process_scribble(
self,
imageUrl: Union[str, Image.Image],
prompt: Union[str, List[str]],
negative_prompt: Union[str, List[str]],
steps: int,
seed: int,
height: int,
width: int,
guidance_scale: float = 7.5,
):
if self.__current_task_name != "scribble":
raise Exception("ControlNet is not loaded with scribble model")
torch.manual_seed(seed)
if isinstance(imageUrl, Image.Image):
init_image = imageUrl.resize((width, height))
else:
init_image = download_image(imageUrl).resize((width, height))
condition_image = self.__scribble_condition_image(init_image)
result = self.pipe2.__call__(
image=condition_image,
prompt=prompt,
num_inference_steps=steps,
negative_prompt=negative_prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
)
return Result.from_result(result)
@torch.inference_mode()
def process_linearart(
self,
imageUrl: str,
prompt: Union[str, List[str]],
negative_prompt: Union[str, List[str]],
steps: int,
seed: int,
height: int,
width: int,
guidance_scale: float = 7.5,
):
if self.__current_task_name != "linearart":
raise Exception("ControlNet is not loaded with linearart model")
torch.manual_seed(seed)
init_image = download_image(imageUrl).resize((width, height))
condition_image = ControlNet.linearart_condition_image(init_image)
result = self.pipe2.__call__(
image=condition_image,
prompt=prompt,
num_inference_steps=steps,
negative_prompt=negative_prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
)
return Result.from_result(result)
def detect_pose(self, imageUrl: str) -> Image.Image:
detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
image = download_image(imageUrl)
image = detector.__call__(image)
return image
def __scribble_condition_image(self, image: Image.Image) -> Image.Image:
processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
image = processor.__call__(input_image=image, scribble=True)
return image
@staticmethod
def linearart_condition_image(image: Image.Image) -> Image.Image:
processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
image = processor.__call__(input_image=image)
return image
def __canny_detect_edge(self, image: Image.Image) -> Image.Image:
image_array = np.array(image)
low_threshold = 100
high_threshold = 200
image_array = cv2.Canny(image_array, low_threshold, high_threshold)
image_array = image_array[:, :, None]
image_array = np.concatenate([image_array, image_array, image_array], axis=2)
canny_image = Image.fromarray(image_array)
return canny_image
def __resize_for_condition_image(self, image: Image.Image, resolution: int):
input_image = image.convert("RGB")
W, H = input_image.size
k = float(resolution) / max(W, H)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
|