File size: 28,578 Bytes
06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 c4b9efc 06d0fb4 c4b9efc 06d0fb4 3119b7f 06d0fb4 76aea54 e681c72 76aea54 06d0fb4 76aea54 06d0fb4 c4b9efc 06d0fb4 c4b9efc 06d0fb4 3119b7f 06d0fb4 76aea54 06d0fb4 76aea54 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 3119b7f 06d0fb4 79e1a03 06d0fb4 6f4ab03 3119b7f 6f4ab03 a01c15b 3119b7f a01c15b 449bc14 3119b7f 449bc14 48f38a9 3119b7f 48f38a9 bbc4ce7 3119b7f bbc4ce7 06d0fb4 76aea54 e681c72 76aea54 06d0fb4 3119b7f 06d0fb4 266f72f fd0b72b 266f72f fd0b72b 06d0fb4 79e1a03 6f4ab03 a01c15b 449bc14 48f38a9 bbc4ce7 06d0fb4 266f72f fd0b72b 06d0fb4 | 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 | import os
import random
import numpy as np
import gradio as gr
from PIL import Image, ImageChops
from typing import Dict, Any, List
from core.settings import (
INPUT_DIR, MULTIPLIERS_MAP, LORA_DIR, EMBEDDING_DIR, VAE_DIR,
FEATURES_CONFIG, TASK_FEATURES_CONFIG
)
from utils.app_utils import (
sanitize_filename,
get_lora_path,
get_embedding_path,
ensure_controlnet_model_downloaded,
ensure_ipadapter_models_downloaded,
_ensure_model_downloaded,
ensure_sd3_ipadapter_models_downloaded,
get_vae_path,
)
def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]:
task_type = ui_inputs['task_type']
temp_files_to_clean = []
arch_enabled_chains = FEATURES_CONFIG.get(workflow_model_type, {}).get('enabled_chains', [])
task_enabled_chains = TASK_FEATURES_CONFIG.get(task_type, {}).get('enabled_chains', [])
enabled_chains = set([c for c in arch_enabled_chains if c in task_enabled_chains])
multiplier = MULTIPLIERS_MAP.get(workflow_model_type, 8)
img_w, img_h = 0, 0
if task_type == 'txt2img':
img_w = int(ui_inputs.get('width', 0))
img_h = int(ui_inputs.get('height', 0))
elif task_type == 'img2img':
input_image_pil = ui_inputs.get('img2img_image')
if input_image_pil:
img_w, img_h = input_image_pil.width, input_image_pil.height
elif task_type == 'inpaint':
inpaint_img = ui_inputs.get('inpaint_image')
inpaint_dict = ui_inputs.get('inpaint_image_dict')
if inpaint_img:
img_w, img_h = inpaint_img.width, inpaint_img.height
elif inpaint_dict and inpaint_dict.get('background'):
img_w, img_h = inpaint_dict['background'].width, inpaint_dict['background'].height
elif task_type == 'outpaint':
input_image_pil = ui_inputs.get('outpaint_image')
if input_image_pil:
img_w, img_h = input_image_pil.width, input_image_pil.height
elif task_type == 'hires_fix':
input_image_pil = ui_inputs.get('hires_image')
if input_image_pil:
img_w, img_h = input_image_pil.width, input_image_pil.height
if img_w > 0 and img_h > 0:
if (img_w % multiplier != 0) or (img_h % multiplier != 0):
warning_msg = f"Width and height must be multiples of {multiplier} for this model."
raise gr.Error(warning_msg)
lora_data = ui_inputs.get('lora_data', [])
active_loras_for_gpu, active_loras_for_meta = [], []
if 'lora' in enabled_chains and lora_data:
sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
if scale > 0 and lora_id and lora_id.strip():
lora_filename = None
if source in ("Upload File", "File"):
raw_id = lora_id.strip().replace('\\', '/')
rel_name = raw_id if raw_id.startswith("upload_file/") else f"upload_file/{raw_id}"
local_path = os.path.join(LORA_DIR, rel_name)
if not os.path.exists(local_path):
if os.path.exists(os.path.join(LORA_DIR, raw_id)):
local_path = os.path.join(LORA_DIR, raw_id)
else:
raise gr.Error(f"Uploaded LoRA file '{lora_id}' no longer exists on server. Please re-upload it.")
lora_filename = os.path.relpath(local_path, LORA_DIR).replace('\\', '/')
elif source in ("Civitai", "Hugging Face"):
local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
if local_path: lora_filename = os.path.relpath(local_path, LORA_DIR).replace('\\', '/')
else: raise gr.Error(f"Failed to prepare LoRA {lora_id}: {status}")
if lora_filename:
active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
ui_inputs['denoise'] = 1.0
if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
elif task_type == 'inpaint': ui_inputs['denoise'] = ui_inputs.get('inpaint_denoise', 1.0)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
if task_type == 'img2img':
input_image_pil = ui_inputs.get('img2img_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Image-to-Image.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs['width'] = input_image_pil.width
ui_inputs['height'] = input_image_pil.height
elif task_type == 'inpaint':
inpaint_img = ui_inputs.get('inpaint_image')
inpaint_dict = ui_inputs.get('inpaint_image_dict')
if inpaint_img:
temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_{random.randint(1000, 9999)}.png")
inpaint_img.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs['width'] = inpaint_img.width
ui_inputs['height'] = inpaint_img.height
elif inpaint_dict and inpaint_dict.get('background') and inpaint_dict.get('layers'):
background_img = inpaint_dict['background'].convert("RGBA")
composite_mask_pil = Image.new('L', background_img.size, 0)
for layer in inpaint_dict['layers']:
if layer:
layer_alpha = layer.split()[-1]
composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
r, g, b, _ = background_img.split()
composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
temp_file_path = os.path.join(INPUT_DIR, f"temp_inpaint_composite_{random.randint(1000, 9999)}.png")
composite_image_with_mask.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs.pop('inpaint_mask', None)
else:
raise gr.Error("Inpainting requires an input image with a mask.")
elif task_type == 'outpaint':
input_image_pil = ui_inputs.get('outpaint_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Outpainting.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
ui_inputs['megapixels'] = 0.25
ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
elif task_type == 'hires_fix':
input_image_pil = ui_inputs.get('hires_image')
if not input_image_pil:
raise gr.Error("Please upload an image for Hires Fix.")
temp_file_path = os.path.join(INPUT_DIR, f"temp_input_{random.randint(1000, 9999)}.png")
input_image_pil.save(temp_file_path, "PNG")
ui_inputs['input_image'] = os.path.basename(temp_file_path)
temp_files_to_clean.append(temp_file_path)
embedding_data = ui_inputs.get('embedding_data', [])
if 'embedding' in enabled_chains and embedding_data:
emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
if emb_id and emb_id.strip():
if source in ("Civitai", "Hugging Face"):
local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
if not local_path:
raise gr.Error(f"Failed to prepare Embedding {emb_id}: {status}")
elif source in ("Upload File", "File"):
# Uploaded file is already stored in models/embeddings/upload_file/.
# Embeddings are triggered directly via prompt (e.g. embedding:upload_file/<filename>).
pass
controlnet_data = ui_inputs.get('controlnet_data', [])
active_controlnets = []
if 'controlnet' in enabled_chains and controlnet_data:
(cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
})
anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
active_anima_controlnets = []
if 'anima_controlnet_lllite' in enabled_chains and anima_controlnet_lllite_data:
(cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
_ensure_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_anima_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_anima_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
})
diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
active_diffsynth_controlnets = []
if 'diffsynth_controlnet' in enabled_chains and diffsynth_controlnet_data:
(cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_diffsynth_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_diffsynth_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"control_net_name": cn_filepaths[i]
})
krea2_controlnet_data = ui_inputs.get('krea2_controlnet_data', [])
active_krea2_controlnets = []
if 'krea2_controlnet' in enabled_chains and krea2_controlnet_data:
(cn_images, _, _, cn_strengths, cn_filepaths) = [krea2_controlnet_data[i::5] for i in range(5)]
for i in range(len(cn_images)):
if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
cn_temp_path = os.path.join(INPUT_DIR, f"temp_krea2_cn_{i}_{random.randint(1000, 9999)}.png")
cn_images[i].save(cn_temp_path, "PNG")
temp_files_to_clean.append(cn_temp_path)
active_krea2_controlnets.append({
"image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
"control_net_name": cn_filepaths[i]
})
ipadapter_data = ui_inputs.get('ipadapter_data', [])
active_ipadapters = []
if 'ipadapter' in enabled_chains and ipadapter_data:
num_ipa_units = (len(ipadapter_data) - 5) // 3
final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
all_presets_to_download = set()
for i in range(num_ipa_units):
if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
all_presets_to_download.add(final_preset)
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
ipa_temp_path = os.path.join(INPUT_DIR, f"temp_ipa_{i}_{random.randint(1000, 9999)}.png")
ipa_images[i].save(ipa_temp_path, "PNG")
temp_files_to_clean.append(ipa_temp_path)
active_ipadapters.append({
"image": os.path.basename(ipa_temp_path), "preset": final_preset,
"weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
})
if active_ipadapters and final_preset:
all_presets_to_download.add(final_preset)
for preset in all_presets_to_download:
ensure_ipadapter_models_downloaded(preset, progress)
model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
if active_ipadapters:
active_ipadapters.append({
'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
})
flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
active_flux1_ipadapters = []
if 'flux1_ipadapter' in enabled_chains and flux1_ipadapter_data:
num_units = len(flux1_ipadapter_data) // 4
f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
for i in range(len(f_images)):
if f_images[i] and f_weights[i] > 0:
for filename in ["ip-adapter.bin"]:
_ensure_model_downloaded(filename, progress)
from huggingface_hub import snapshot_download
progress(0.5, desc="Caching HF SigLIP model...")
snapshot_download(
repo_id="google/siglip-so400m-patch14-384",
allow_patterns=["*.json", "*.safetensors", "*.txt"],
ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
)
temp_path = os.path.join(INPUT_DIR, f"temp_fipa_{i}_{random.randint(1000, 9999)}.png")
f_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_flux1_ipadapters.append({
"image": os.path.basename(temp_path),
"weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
})
sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
active_sd3_ipadapters = []
if 'sd3_ipadapter' in enabled_chains and sd3_ipadapter_data:
num_units = len(sd3_ipadapter_data) // 4
s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
sd3_ipa_downloaded = False
for i in range(len(s_images)):
if s_images[i] and s_weights[i] > 0:
if not sd3_ipa_downloaded:
ensure_sd3_ipadapter_models_downloaded(progress)
sd3_ipa_downloaded = True
temp_path = os.path.join(INPUT_DIR, f"temp_s3ipa_{i}_{random.randint(1000, 9999)}.png")
s_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_sd3_ipadapters.append({
"image": os.path.basename(temp_path),
"weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
})
style_data = ui_inputs.get('style_data', [])
active_styles = []
if 'style' in enabled_chains and style_data:
num_units = len(style_data) // 2
st_images = style_data[0*num_units : 1*num_units]
st_strengths = style_data[1*num_units : 2*num_units]
style_models_downloaded = False
for i in range(len(st_images)):
if st_images[i] and st_strengths[i] > 0:
if not style_models_downloaded:
_ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
_ensure_model_downloaded("flux1-redux-dev.safetensors", progress)
style_models_downloaded = True
temp_path = os.path.join(INPUT_DIR, f"temp_style_{i}_{random.randint(1000, 9999)}.png")
st_images[i].save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_styles.append({
"image": os.path.basename(temp_path), "strength": st_strengths[i]
})
reference_latent_data = ui_inputs.get('reference_latent_data', [])
active_reference_latents = []
if 'reference_latent' in enabled_chains and reference_latent_data:
for img in reference_latent_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_reference_latents.append(os.path.basename(temp_path))
hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', [])
active_hidream_o1_reference = []
if 'hidream_o1_reference' in enabled_chains and hidream_o1_reference_data:
for img in hidream_o1_reference_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_ho1_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_hidream_o1_reference.append(os.path.basename(temp_path))
sensenova_reference_data = ui_inputs.get('sensenova_reference_data', [])
active_sensenova_reference = []
if 'sensenova_reference' in enabled_chains and sensenova_reference_data:
for img in sensenova_reference_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_sn_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_sensenova_reference.append(os.path.basename(temp_path))
joyai_reference_data = ui_inputs.get('joyai_reference_data', [])
active_joyai_reference = []
if 'joyai_image' in enabled_chains and joyai_reference_data:
for img in joyai_reference_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_joyai_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_joyai_reference.append(os.path.basename(temp_path))
krea2_identity_edit_data = ui_inputs.get('krea2_identity_edit_data', [])
active_krea2_identity_edit = []
if 'krea2_identity_edit' in enabled_chains and krea2_identity_edit_data:
for img in krea2_identity_edit_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_krea2_id_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_krea2_identity_edit.append(os.path.basename(temp_path))
krea2_reference_edit_data = ui_inputs.get('krea2_reference_edit_data', [])
active_krea2_reference_edit = []
if 'krea2_style_reference' in enabled_chains and krea2_reference_edit_data:
for img in krea2_reference_edit_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_krea2_reference_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_krea2_reference_edit.append(os.path.basename(temp_path))
qwen_image_edit_data = ui_inputs.get('qwen_image_edit_data', [])
active_qwen_image_edit = []
if 'qwen_image_edit' in enabled_chains and qwen_image_edit_data:
for img in qwen_image_edit_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_qwen_edit_ref_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_qwen_image_edit.append(os.path.basename(temp_path))
reference_image_data = ui_inputs.get('reference_image_data', [])
active_reference_images = []
if 'reference_image' in enabled_chains and reference_image_data:
for img in reference_image_data:
if img:
if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
temp_path = os.path.join(INPUT_DIR, f"temp_ref_img_{random.randint(1000, 9999)}.png")
img.save(temp_path, "PNG")
temp_files_to_clean.append(temp_path)
active_reference_images.append(os.path.basename(temp_path))
vae_source = ui_inputs.get('vae_source')
vae_id = ui_inputs.get('vae_id')
vae_name_override = None
if 'vae' in enabled_chains and vae_source and vae_id and str(vae_id).strip():
vae_id_clean = str(vae_id).strip()
if vae_source in ("Upload File", "File"):
raw_id = vae_id_clean.replace('\\', '/')
rel_name = raw_id if raw_id.startswith("upload_file/") else f"upload_file/{raw_id}"
local_path = os.path.join(VAE_DIR, rel_name)
if not os.path.exists(local_path):
if os.path.exists(os.path.join(VAE_DIR, raw_id)):
local_path = os.path.join(VAE_DIR, raw_id)
else:
raise gr.Error(f"Uploaded VAE file '{vae_id_clean}' no longer exists on server. Please re-upload it.")
vae_name_override = os.path.relpath(local_path, VAE_DIR).replace('\\', '/')
elif vae_source in ("Civitai", "Hugging Face"):
local_path, status = get_vae_path(vae_source, vae_id_clean, os.environ.get("CIVITAI_API_KEY", ""), progress)
if local_path: vae_name_override = os.path.relpath(local_path, VAE_DIR).replace('\\', '/')
else: raise gr.Error(f"Failed to prepare VAE {vae_id_clean}: {status}")
if vae_name_override:
ui_inputs['vae_name'] = vae_name_override
conditioning_data = ui_inputs.get('conditioning_data', [])
active_conditioning = []
if 'conditioning' in enabled_chains and conditioning_data:
num_units = len(conditioning_data) // 6
prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
for i in range(num_units):
if prompts[i] and prompts[i].strip():
active_conditioning.append({
"prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
"x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
})
pe_enable = ui_inputs.get('qwen_image_2_1_prompt_enhancer_enable', False)
active_qwen_image_2_1_prompt_enhancer = []
if 'qwen_image_2_1_prompt_enhancer' in enabled_chains and pe_enable:
active_qwen_image_2_1_prompt_enhancer.append({
"enable": True,
"thinking": bool(ui_inputs.get('qwen_image_2_1_prompt_enhancer_thinking', False)),
"max_length": int(ui_inputs.get('qwen_image_2_1_prompt_enhancer_max_length', 4096))
})
ming_pe_enable = ui_inputs.get('ming_image_prompt_enhancer_enable', False)
active_ming_image_prompt_enhancer = []
if 'ming_image_prompt_enhancer' in enabled_chains and ming_pe_enable:
item = {
"enable": True,
"thinking": bool(ui_inputs.get('ming_image_prompt_enhancer_thinking', False)),
"max_length": int(ui_inputs.get('ming_image_prompt_enhancer_max_length', 4096))
}
sys_p = ui_inputs.get('ming_image_prompt_enhancer_system_prompt')
if sys_p:
item["system_prompt"] = str(sys_p)
active_ming_image_prompt_enhancer.append(item)
return {
"active_loras_for_gpu": active_loras_for_gpu,
"active_loras_for_meta": active_loras_for_meta,
"active_controlnets": active_controlnets,
"active_anima_controlnets": active_anima_controlnets,
"active_diffsynth_controlnets": active_diffsynth_controlnets,
"active_krea2_controlnets": active_krea2_controlnets,
"active_ipadapters": active_ipadapters,
"active_flux1_ipadapters": active_flux1_ipadapters,
"active_sd3_ipadapters": active_sd3_ipadapters,
"active_styles": active_styles,
"active_reference_latents": active_reference_latents,
"active_hidream_o1_reference": active_hidream_o1_reference,
"active_sensenova_reference": active_sensenova_reference,
"active_joyai_reference": active_joyai_reference,
"active_krea2_identity_edit": active_krea2_identity_edit,
"active_krea2_reference_edit": active_krea2_reference_edit,
"active_qwen_image_edit": active_qwen_image_edit,
"active_reference_images": active_reference_images,
"active_conditioning": active_conditioning,
"active_qwen_image_2_1_prompt_enhancer": active_qwen_image_2_1_prompt_enhancer,
"active_ming_image_prompt_enhancer": active_ming_image_prompt_enhancer,
"temp_files_to_clean": temp_files_to_clean
} |