Spaces:
Running
on
Zero
Running
on
Zero
File size: 22,255 Bytes
1d117d0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 |
import argparse
import os
import random
import sys
import numpy as np
import torch
from PIL import Image
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
from modules.AutoDetailer import SAM, SEGS, ADetailer, bbox
from modules.AutoEncoders import VariationalAE
from modules.clip import Clip
from modules.Device import Device
from modules.FileManaging import Downloader, ImageSaver, Loader
from modules.hidiffusion import msw_msa_attention
from modules.Model import LoRas
from modules.Quantize import Quantizer
from modules.sample import sampling
from modules.UltimateSDUpscale import UltimateSDUpscale, USDU_upscaler
from modules.Utilities import Enhancer, Latent, upscale
from modules.WaveSpeed import fbcache_nodes
last_seed = 0
Downloader.CheckAndDownload()
def pipeline(
prompt: str,
w: int,
h: int,
number: int = 1,
batch: int = 1,
hires_fix: bool = False,
adetailer: bool = False,
enhance_prompt: bool = False,
img2img: bool = False,
stable_fast: bool = False,
reuse_seed: bool = False,
flux_enabled: bool = False,
prio_speed: bool = False,
) -> None:
"""#### Run the LightDiffusion pipeline.
#### Args:
- `prompt` (str): The prompt for the pipeline.
- `w` (int): The width of the generated image.
- `h` (int): The height of the generated image.
- `hires_fix` (bool, optional): Enable high-resolution fix. Defaults to False.
- `adetailer` (bool, optional): Enable automatic face and body enhancing. Defaults to False.
- `enhance_prompt` (bool, optional): Enable Ollama prompt enhancement. Defaults to False.
- `img2img` (bool, optional): Use LightDiffusion in Image to Image mode, the prompt input becomes the path to the input image. Defaults to False.
- `stable_fast` (bool, optional): Enable Stable-Fast speedup offering a 70% speed improvement in return of a compilation time. Defaults to False.
- `reuse_seed` (bool, optional): Reuse the last used seed, if False the seed will be kept random. Default to False.
- `flux_enabled` (bool, optional): Enable the flux mode. Defaults to False.
- `prio_speed` (bool, optional): Prioritize speed over quality. Defaults to False.
"""
global last_seed
if reuse_seed:
seed = last_seed
else:
seed = random.randint(1, 2**64)
last_seed = seed
if enhance_prompt:
try:
prompt = Enhancer.enhance_prompt(prompt)
except:
pass
sampler_name = "dpmpp_sde" if not prio_speed else "dpmpp_2m"
ckpt = "./_internal/checkpoints/Meina V10 - baked VAE.safetensors"
with torch.inference_mode():
if not flux_enabled:
checkpointloadersimple = Loader.CheckpointLoaderSimple()
checkpointloadersimple_241 = checkpointloadersimple.load_checkpoint(
ckpt_name=ckpt
)
hidiffoptimizer = msw_msa_attention.ApplyMSWMSAAttentionSimple()
cliptextencode = Clip.CLIPTextEncode()
emptylatentimage = Latent.EmptyLatentImage()
ksampler_instance = sampling.KSampler2()
vaedecode = VariationalAE.VAEDecode()
saveimage = ImageSaver.SaveImage()
latent_upscale = upscale.LatentUpscale()
for _ in range(number):
if img2img:
img = Image.open(prompt)
img_array = np.array(img)
img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0
img_tensor = img_tensor.unsqueeze(0)
with torch.inference_mode():
ultimatesdupscale = UltimateSDUpscale.UltimateSDUpscale()
try:
loraloader = LoRas.LoraLoader()
loraloader_274 = loraloader.load_lora(
lora_name="add_detail.safetensors",
strength_model=2,
strength_clip=2,
model=checkpointloadersimple_241[0],
clip=checkpointloadersimple_241[1],
)
except:
loraloader_274 = checkpointloadersimple_241
if stable_fast is True:
from modules.StableFast import StableFast
applystablefast = StableFast.ApplyStableFastUnet()
applystablefast_158 = applystablefast.apply_stable_fast(
enable_cuda_graph=False,
model=loraloader_274[0],
)
else:
applystablefast_158 = loraloader_274
clipsetlastlayer = Clip.CLIPSetLastLayer()
clipsetlastlayer_257 = clipsetlastlayer.set_last_layer(
stop_at_clip_layer=-2, clip=loraloader_274[1]
)
cliptextencode_242 = cliptextencode.encode(
text=prompt,
clip=clipsetlastlayer_257[0],
)
cliptextencode_243 = cliptextencode.encode(
text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)",
clip=clipsetlastlayer_257[0],
)
upscalemodelloader = USDU_upscaler.UpscaleModelLoader()
upscalemodelloader_244 = upscalemodelloader.load_model(
"RealESRGAN_x4plus.pth"
)
ultimatesdupscale_250 = ultimatesdupscale.upscale(
upscale_by=2,
seed=random.randint(1, 2**64),
steps=8,
cfg=6,
sampler_name=sampler_name,
scheduler="karras",
denoise=0.3,
mode_type="Linear",
tile_width=512,
tile_height=512,
mask_blur=16,
tile_padding=32,
seam_fix_mode="Half Tile",
seam_fix_denoise=0.2,
seam_fix_width=64,
seam_fix_mask_blur=16,
seam_fix_padding=32,
force_uniform_tiles="enable",
image=img_tensor,
model=applystablefast_158[0],
positive=cliptextencode_242[0],
negative=cliptextencode_243[0],
vae=checkpointloadersimple_241[2],
upscale_model=upscalemodelloader_244[0],
pipeline=True,
)
saveimage.save_images(
filename_prefix="LD-i2i",
images=ultimatesdupscale_250[0],
)
elif flux_enabled:
Downloader.CheckAndDownloadFlux()
with torch.inference_mode():
dualcliploadergguf = Quantizer.DualCLIPLoaderGGUF()
emptylatentimage = Latent.EmptyLatentImage()
vaeloader = VariationalAE.VAELoader()
unetloadergguf = Quantizer.UnetLoaderGGUF()
cliptextencodeflux = Quantizer.CLIPTextEncodeFlux()
conditioningzeroout = Quantizer.ConditioningZeroOut()
ksampler = sampling.KSampler2()
unetloadergguf_10 = unetloadergguf.load_unet(
unet_name="flux1-dev-Q8_0.gguf"
)
fb_cache = fbcache_nodes.ApplyFBCacheOnModel()
unetloadergguf_10 = fb_cache.patch(unetloadergguf_10, "diffusion_model", 0.120)
vaeloader_11 = vaeloader.load_vae(vae_name="ae.safetensors")
dualcliploadergguf_19 = dualcliploadergguf.load_clip(
clip_name1="clip_l.safetensors",
clip_name2="t5-v1_1-xxl-encoder-Q8_0.gguf",
type="flux",
)
emptylatentimage_5 = emptylatentimage.generate(
width=w, height=h, batch_size=batch
)
cliptextencodeflux_15 = cliptextencodeflux.encode(
clip_l=prompt,
t5xxl=prompt,
guidance=2.5,
clip=dualcliploadergguf_19[0],
flux_enabled=True,
)
conditioningzeroout_16 = conditioningzeroout.zero_out(
conditioning=cliptextencodeflux_15[0]
)
ksampler_3 = ksampler.sample(
seed=random.randint(1, 2**64),
steps=20,
cfg=1,
sampler_name="euler",
scheduler="beta",
denoise=1,
model=unetloadergguf_10[0],
positive=cliptextencodeflux_15[0],
negative=conditioningzeroout_16[0],
latent_image=emptylatentimage_5[0],
pipeline=True,
flux=True,
)
vaedecode_8 = vaedecode.decode(
samples=ksampler_3[0],
vae=vaeloader_11[0],
flux=True,
)
saveimage.save_images(
filename_prefix="Flux", images=vaedecode_8[0]
)
else:
while prompt is None:
pass
with torch.inference_mode():
try:
loraloader = LoRas.LoraLoader()
loraloader_274 = loraloader.load_lora(
lora_name="add_detail.safetensors",
strength_model=0.7,
strength_clip=0.7,
model=checkpointloadersimple_241[0],
clip=checkpointloadersimple_241[1],
)
print("loading add_detail.safetensors")
except:
loraloader_274 = checkpointloadersimple_241
clipsetlastlayer = Clip.CLIPSetLastLayer()
clipsetlastlayer_257 = clipsetlastlayer.set_last_layer(
stop_at_clip_layer=-2, clip=loraloader_274[1]
)
applystablefast_158 = loraloader_274
cliptextencode_242 = cliptextencode.encode(
text=prompt,
clip=clipsetlastlayer_257[0],
)
cliptextencode_243 = cliptextencode.encode(
text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)",
clip=clipsetlastlayer_257[0],
)
emptylatentimage_244 = emptylatentimage.generate(
width=w, height=h, batch_size=batch
)
if stable_fast is True:
from modules.StableFast import StableFast
try:
self.title("LightDiffusion - Generating StableFast model")
except:
pass
applystablefast = StableFast.ApplyStableFastUnet()
applystablefast_158 = applystablefast.apply_stable_fast(
enable_cuda_graph=False,
model=loraloader_274[0],
)
else:
applystablefast_158 = loraloader_274
fb_cache = fbcache_nodes.ApplyFBCacheOnModel()
applystablefast_158 = fb_cache.patch(applystablefast_158, "diffusion_model", 0.120)
ksampler_239 = ksampler_instance.sample(
seed=seed,
steps=20,
cfg=7,
sampler_name=sampler_name,
scheduler="karras",
denoise=1,
pipeline=True,
model=hidiffoptimizer.go(
model_type="auto", model=applystablefast_158[0]
)[0],
positive=cliptextencode_242[0],
negative=cliptextencode_243[0],
latent_image=emptylatentimage_244[0],
)
if hires_fix:
latentupscale_254 = latent_upscale.upscale(
width=w * 2,
height=h * 2,
samples=ksampler_239[0],
)
ksampler_253 = ksampler_instance.sample(
seed=random.randint(1, 2**64),
steps=10,
cfg=8,
sampler_name="euler_ancestral",
scheduler="normal",
denoise=0.45,
model=hidiffoptimizer.go(
model_type="auto", model=applystablefast_158[0]
)[0],
positive=cliptextencode_242[0],
negative=cliptextencode_243[0],
latent_image=latentupscale_254[0],
pipeline=True,
)
else:
ksampler_253 = ksampler_239
vaedecode_240 = vaedecode.decode(
samples=ksampler_253[0],
vae=checkpointloadersimple_241[2],
)
if adetailer:
with torch.inference_mode():
samloader = SAM.SAMLoader()
samloader_87 = samloader.load_model(
model_name="sam_vit_b_01ec64.pth", device_mode="AUTO"
)
cliptextencode_124 = cliptextencode.encode(
text="royal, detailed, magnificient, beautiful, seducing",
clip=loraloader_274[1],
)
ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider()
ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit(
# model_name="face_yolov8m.pt"
model_name="person_yolov8m-seg.pt"
)
bboxdetectorsegs = bbox.BboxDetectorForEach()
samdetectorcombined = SAM.SAMDetectorCombined()
impactsegsandmask = SEGS.SegsBitwiseAndMask()
detailerforeachdebug = ADetailer.DetailerForEachTest()
bboxdetectorsegs_132 = bboxdetectorsegs.doit(
threshold=0.5,
dilation=10,
crop_factor=2,
drop_size=10,
labels="all",
bbox_detector=ultralyticsdetectorprovider_151[0],
image=vaedecode_240[0],
)
samdetectorcombined_139 = samdetectorcombined.doit(
detection_hint="center-1",
dilation=0,
threshold=0.93,
bbox_expansion=0,
mask_hint_threshold=0.7,
mask_hint_use_negative="False",
sam_model=samloader_87[0],
segs=bboxdetectorsegs_132,
image=vaedecode_240[0],
)
if samdetectorcombined_139 is None:
return
impactsegsandmask_152 = impactsegsandmask.doit(
segs=bboxdetectorsegs_132,
mask=samdetectorcombined_139[0],
)
detailerforeachdebug_145 = detailerforeachdebug.doit(
guide_size=512,
guide_size_for=False,
max_size=768,
seed=random.randint(1, 2**64),
steps=20,
cfg=6.5,
sampler_name=sampler_name,
scheduler="karras",
denoise=0.5,
feather=5,
noise_mask=True,
force_inpaint=True,
wildcard="",
cycle=1,
inpaint_model=False,
noise_mask_feather=20,
image=vaedecode_240[0],
segs=impactsegsandmask_152[0],
model=applystablefast_158[0],
clip=checkpointloadersimple_241[1],
vae=checkpointloadersimple_241[2],
positive=cliptextencode_124[0],
negative=cliptextencode_243[0],
pipeline=True,
)
saveimage.save_images(
filename_prefix="LD-refined",
images=detailerforeachdebug_145[0],
)
ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider()
ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit(
model_name="face_yolov9c.pt"
)
bboxdetectorsegs_132 = bboxdetectorsegs.doit(
threshold=0.5,
dilation=10,
crop_factor=2,
drop_size=10,
labels="all",
bbox_detector=ultralyticsdetectorprovider_151[0],
image=detailerforeachdebug_145[0],
)
samdetectorcombined_139 = samdetectorcombined.doit(
detection_hint="center-1",
dilation=0,
threshold=0.93,
bbox_expansion=0,
mask_hint_threshold=0.7,
mask_hint_use_negative="False",
sam_model=samloader_87[0],
segs=bboxdetectorsegs_132,
image=detailerforeachdebug_145[0],
)
impactsegsandmask_152 = impactsegsandmask.doit(
segs=bboxdetectorsegs_132,
mask=samdetectorcombined_139[0],
)
detailerforeachdebug_145 = detailerforeachdebug.doit(
guide_size=512,
guide_size_for=False,
max_size=768,
seed=random.randint(1, 2**64),
steps=20,
cfg=6.5,
sampler_name=sampler_name,
scheduler="karras",
denoise=0.5,
feather=5,
noise_mask=True,
force_inpaint=True,
wildcard="",
cycle=1,
inpaint_model=False,
noise_mask_feather=20,
image=detailerforeachdebug_145[0],
segs=impactsegsandmask_152[0],
model=applystablefast_158[0],
clip=checkpointloadersimple_241[1],
vae=checkpointloadersimple_241[2],
positive=cliptextencode_124[0],
negative=cliptextencode_243[0],
pipeline=True,
)
saveimage.save_images(
filename_prefix="lD-2ndrefined",
images=detailerforeachdebug_145[0],
)
else:
saveimage.save_images(filename_prefix="LD", images=vaedecode_240[0])
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the LightDiffusion pipeline.")
parser.add_argument("prompt", type=str, help="The prompt for the pipeline.")
parser.add_argument("width", type=int, help="The width of the generated image.")
parser.add_argument("height", type=int, help="The height of the generated image.")
parser.add_argument("number", type=int, help="The number of images to generate.")
parser.add_argument("batch", type=int, help="The batch size. aka the number of images to generate at once.")
parser.add_argument(
"--hires-fix", action="store_true", help="Enable high-resolution fix."
)
parser.add_argument(
"--adetailer",
action="store_true",
help="Enable automatic face and body enhancin.g",
)
parser.add_argument(
"--enhance-prompt",
action="store_true",
help="Enable Ollama prompt enhancement. Make sure to have ollama with Ollama installed.",
)
parser.add_argument(
"--img2img",
action="store_true",
help="Enable image-to-image mode. This will use the image as the prompt.",
)
parser.add_argument(
"--stable-fast",
action="store_true",
help="Enable StableFast mode. This will compile the model for faster inference.",
)
parser.add_argument(
"--reuse-seed",
action="store_true",
help="Enable to reuse last used seed for sampling, default for False is a random seed at every use.",
)
parser.add_argument(
"--flux",
action="store_true",
help="Enable the flux mode.",
)
parser.add_argument(
"--prio-speed",
action="store_true",
help="Prioritize speed over quality.",
)
args = parser.parse_args()
pipeline(
args.prompt,
args.width,
args.height,
args.number,
args.batch,
args.hires_fix,
args.adetailer,
args.enhance_prompt,
args.img2img,
args.stable_fast,
args.reuse_seed,
args.flux,
args.prio_speed,
)
|