├── .github └── workflow │ └── workflow.yml ├── .gitignore ├── .pdm-python ├── .pdmignore ├── DrawBridgeAPI ├── __init__.py ├── api_server.py ├── app.py ├── backend │ ├── FLUX_falai.py │ ├── FLUX_replicate.py │ ├── SD_A1111_webui.py │ ├── SD_civitai_API.py │ ├── __init__.py │ ├── base.py │ ├── comfyui.py │ ├── liblibai.py │ ├── midjourney.py │ ├── novelai.py │ ├── seaart.py │ ├── tusiart.py │ └── yunjie.py ├── base_config.py ├── comfyui_workflows │ ├── diaopony-hr.json │ ├── diaopony-hr_reflex.json │ ├── diaopony-tipo.json │ ├── diaopony-tipo_reflex.json │ ├── flux-dev.json │ ├── flux-dev_reflex.json │ ├── flux-schnell.json │ ├── flux-schnell_reflex.json │ ├── flux修手.json │ ├── flux修手_reflex.json │ ├── sd3.5_txt2img.json │ ├── sd3.5_txt2img_reflex.json │ ├── sdbase_img2img.json │ ├── sdbase_img2img_reflex.json │ ├── sdbase_txt2img.json │ ├── sdbase_txt2img_hr_fix.json │ ├── sdbase_txt2img_hr_fix_reflex.json │ ├── sdbase_txt2img_reflex.json │ └── 创意融字 工作流Jianan_创意融字海报.json ├── config_example.yaml ├── dbapi.py ├── locales │ ├── __init__.py │ └── zh_CN │ │ └── LC_MESSAGES │ │ ├── messages.mo │ │ └── messages.po ├── ui │ └── __init__.py └── utils │ ├── __init__.py │ ├── custom_class.py │ ├── exceptions.py │ ├── llm_caption_requirements.txt │ ├── llm_captions.py │ ├── request_model.py │ ├── shared.py │ ├── tagger-requirements.txt │ ├── tagger.py │ └── topaz.py ├── LICENSE ├── README.md ├── README_ZH.md ├── docs └── API.md ├── images ├── caption.png ├── cover.png ├── gradio.png ├── how_to_enable_backend1.png ├── how_to_enable_backend2.png ├── idle.png ├── posting.png └── working.png ├── pyproject.toml └── requirements.txt /.github/workflow/workflow.yml: -------------------------------------------------------------------------------- 1 | name: CI 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | pull_request: 8 | branches: 9 | - main 10 | 11 | jobs: 12 | test: 13 | runs-on: ubuntu-latest 14 | 15 | steps: 16 | - name: Check out the repository 17 | uses: actions/checkout@v3 18 | 19 | - name: Set up Python 20 | uses: actions/setup-python@v4 21 | with: 22 | python-version: '3.10' 23 | 24 | - name: Install dependencies 25 | run: | 26 | python -m pip install --upgrade pip 27 | pip install -r requirements.txt 28 | 29 | - name: Run tests 30 | run: | 31 | pytest 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | venv/ 2 | .idea/ 3 | __pycache__/ 4 | DrawBridgeAPI/__pycache__/ 5 | tmp/ 6 | .pdm-build/ 7 | dist/ 8 | 9 | *.log 10 | DrawBridgeAPI/config.yaml 11 | TODO 12 | 13 | DrawBridgeAPI/saved_images -------------------------------------------------------------------------------- /.pdm-python: -------------------------------------------------------------------------------- 1 | C:/Users/43701/github/diao/sd_api/venv/Scripts/python.exe -------------------------------------------------------------------------------- /.pdmignore: -------------------------------------------------------------------------------- 1 | *.log 2 | DrawBridgeAPI/config.yaml 3 | TODO 4 | 5 | DrawBridgeAPI/saved_images -------------------------------------------------------------------------------- /DrawBridgeAPI/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/DrawBridgeAPI/__init__.py -------------------------------------------------------------------------------- /DrawBridgeAPI/api_server.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import os 3 | import asyncio 4 | import time 5 | import traceback 6 | import json 7 | import itertools 8 | import argparse 9 | import uvicorn 10 | import logging 11 | import warnings 12 | import uuid 13 | import aiofiles 14 | 15 | os.environ['CIVITAI_API_TOKEN'] = 'kunkun' 16 | os.environ['FAL_KEY'] = 'Daisuki' 17 | path_env = os.getenv("CONF_PATH") 18 | 19 | from .utils import request_model, topaz, run_later 20 | from .base_config import setup_logger, init_instance 21 | 22 | from fastapi import FastAPI, Request 23 | from fastapi.responses import JSONResponse, RedirectResponse 24 | from fastapi.exceptions import HTTPException 25 | from pathlib import Path 26 | 27 | logger = setup_logger("[API]") 28 | logging.getLogger("uvicorn.access").disabled = True 29 | logging.getLogger("uvicorn.error").disabled = True 30 | logging.getLogger("fastapi").disabled = True 31 | 32 | app = FastAPI() 33 | 34 | parser = argparse.ArgumentParser(description='Run the FastAPI application.') 35 | parser.add_argument('--host', type=str, default='0.0.0.0', 36 | help='The host IP address to listen on (default: 0.0.0.0).') 37 | parser.add_argument('--port', type=int, default=8000, 38 | help='The port number to listen on (default: 8000).') 39 | parser.add_argument('--conf', '-c', type=str, default='./config.yaml', 40 | help='配置文件路径', dest='conf') 41 | parser.add_argument('--working-dir', '-wd', type=str, default='./', 42 | help='工作路径', dest='working_dir') 43 | parser.add_argument('--fastapi-log', action="store_true", help='工作路径', dest='log') 44 | 45 | args = parser.parse_args() 46 | port = args.port 47 | host = args.host 48 | config_file_path = path_env or args.conf 49 | 50 | from .locales import _ 51 | 52 | init_instance.init(config_file_path) 53 | config = init_instance.config 54 | redis_client = init_instance.redis_client 55 | os.chdir(args.working_dir) 56 | logger.info(_("Working directory: {0}").format(os.getcwd())) 57 | 58 | from .backend import TaskHandler, Backend, StaticHandler 59 | 60 | warnings.filterwarnings("ignore", category=DeprecationWarning) 61 | 62 | 63 | class Api: 64 | def __init__(self): 65 | self.app = app 66 | self.backend_instance = Backend() 67 | 68 | self.add_api_route( 69 | "/sdapi/v1/txt2img", 70 | self.txt2img_api, 71 | methods=["POST"], 72 | # response_model=request_model.Txt2ImgRequest 73 | ) 74 | self.add_api_route( 75 | "/sdapi/v1/img2img", 76 | self.img2img_api, 77 | methods=["POST"], 78 | # response_model=request_model.Img2ImgRequest 79 | ) 80 | self.add_api_route( 81 | "/sdapi/v1/sd-models", 82 | self.get_sd_models, 83 | methods=["GET"] 84 | ) 85 | self.add_api_route( 86 | "/sdapi/v1/progress", 87 | self.get_progress, 88 | methods=["GET"] 89 | ) 90 | self.add_api_route( 91 | "/sdapi/v1/memory", 92 | self.get_memory, 93 | methods=["GET"] 94 | ) 95 | self.add_api_route( 96 | "/sdapi/v1/options", 97 | self.get_options, 98 | methods=["GET"] 99 | ) 100 | self.add_api_route( 101 | "/sdapi/v1/options", 102 | self.set_options, 103 | methods=["POST"] 104 | ) 105 | self.add_api_route( 106 | "/sdapi/v1/prompt-styles", 107 | self.get_prompt_styles, 108 | methods=["GET"] 109 | ) 110 | 111 | if config.server_settings['build_in_tagger']: 112 | 113 | from .utils.tagger import wd_tagger_handler, wd_logger 114 | self.add_api_route( 115 | "/tagger/v1/interrogate", 116 | self.tagger, 117 | methods=["POST"], 118 | response_model=request_model.TaggerRequest 119 | ) 120 | 121 | if config.server_settings['llm_caption']['enable']: 122 | from .utils.llm_captions import llm_logger, joy_caption_handler 123 | self.add_api_route( 124 | "/llm/caption", 125 | self.llm_caption, 126 | methods=["POST"], 127 | response_model=request_model.TaggerRequest 128 | ) 129 | 130 | if config.server_settings['build_in_photoai']['exec_path']: 131 | self.add_api_route( 132 | "/topazai/image", 133 | self.topaz_ai, 134 | methods=["POST"] 135 | ) 136 | 137 | def add_api_route(self, path: str, endpoint, **kwargs): 138 | return self.app.add_api_route(path, endpoint, **kwargs) 139 | 140 | @staticmethod 141 | async def generate_handle(data) -> TaskHandler: 142 | 143 | model_to_backend = None 144 | if data['override_settings'].get("sd_model_checkpoint", None): 145 | model_to_backend = data['override_settings'].get("sd_model_checkpoint", None) 146 | 147 | styles = data.get('styles', []) 148 | selected_style = [] 149 | selected_comfyui_style = [] 150 | 151 | if styles: 152 | api_styles = StaticHandler.get_prompt_style() 153 | for index, i in enumerate(api_styles): 154 | for style in styles: 155 | if style.strip() == '': 156 | pass 157 | else: 158 | if style in i['name']: 159 | if 'comfyui' in i['name']: 160 | logger.info(f"{_('Selected ComfyUI style')} - {i['name']}") 161 | selected_comfyui_style.append(i['name']) 162 | else: 163 | selected_style.append(i['name']) 164 | 165 | if selected_style: 166 | for i in selected_style: 167 | data['prompt'] = data.get('prompt', '') + i['prompt'] 168 | data['negative_prompt'] = data.get('negative_prompt', '') + i['negative_prompt'] 169 | 170 | task_handler = TaskHandler( 171 | data, 172 | model_to_backend=model_to_backend, 173 | comfyui_json=selected_comfyui_style[0].replace('comfyui-work-flows-', '') if selected_comfyui_style else None 174 | ) 175 | 176 | return task_handler 177 | 178 | @staticmethod 179 | async def txt2img_api(request: request_model.Txt2ImgRequest, api: Request): 180 | 181 | data = request.model_dump() 182 | client_host = api.client.host 183 | 184 | task_handler = await Api.generate_handle(data) 185 | 186 | try: 187 | logger.info(f"{_('Exec TXT2IMG')} - {client_host}") 188 | result = await task_handler.txt2img() 189 | except Exception as e: 190 | logger.error(traceback.format_exc()) 191 | raise HTTPException(status_code=500, detail=str(e)) 192 | 193 | if result is None: 194 | raise HTTPException(500, detail='Result not found') 195 | 196 | return result 197 | 198 | @staticmethod 199 | async def img2img_api(request: request_model.Img2ImgRequest, api: Request): 200 | data = request.model_dump() 201 | client_host = api.client.host 202 | 203 | if len(data['init_images']) == 0: 204 | raise HTTPException(status_code=400, detail=_('IMG2IMG Requires image to start')) 205 | 206 | task_handler = await Api.generate_handle(data) 207 | 208 | try: 209 | logger.info(f"{_('Exec IMG2IMG')} - {client_host}") 210 | result = await task_handler.img2img() 211 | except Exception as e: 212 | logger.error(traceback.format_exc()) 213 | raise HTTPException(status_code=500, detail=str(e)) 214 | 215 | if result is None: 216 | raise HTTPException(500, detail='Result not found') 217 | 218 | return result 219 | 220 | @staticmethod 221 | async def get_sd_models(): 222 | 223 | task_list = [] 224 | path = '/sdapi/v1/sd-models' 225 | 226 | task_handler = TaskHandler({}, None, path, reutrn_instance=True, override_model_select=True) 227 | instance_list: list[Backend] = await task_handler.sd_api() 228 | 229 | for i in instance_list: 230 | task_list.append(i.get_models()) 231 | resp = await asyncio.gather(*task_list) 232 | 233 | models_dict = {} 234 | api_respond = [] 235 | for i in resp: 236 | models_dict = models_dict | i 237 | api_respond = api_respond + list(i.values()) 238 | 239 | api_respond = list(itertools.chain.from_iterable(api_respond)) 240 | 241 | redis_resp: bytes = redis_client.get('models') 242 | redis_resp: dict = json.loads(redis_resp.decode('utf-8')) 243 | redis_resp.update(models_dict) 244 | redis_client.set('models', json.dumps(redis_resp)) 245 | return api_respond 246 | 247 | async def tagger(self, request: request_model.TaggerRequest): 248 | from .utils.tagger import wd_tagger_handler, wd_logger 249 | 250 | data = request.model_dump() 251 | base64_image = await self.download_img_from_url(data) 252 | caption = await wd_tagger_handler.tagger_main(base64_image, data['threshold'], data['exclude_tags']) 253 | resp = {} 254 | 255 | resp['caption'] = caption 256 | wd_logger.info(f"{_('Caption Successful')}, {caption}") 257 | return JSONResponse(resp) 258 | 259 | async def llm_caption(self, request: request_model.TaggerRequest): 260 | 261 | from .utils.llm_captions import llm_logger, joy_caption_handler 262 | from .utils.tagger import wd_tagger_handler, wd_logger 263 | 264 | data = request.model_dump() 265 | base64_image = await self.download_img_from_url(data) 266 | 267 | try: 268 | caption = await joy_caption_handler.get_caption(base64_image, data['exclude_tags']) 269 | except Exception as e: 270 | traceback.print_exc() 271 | raise HTTPException(status_code=500, detail=str(e)) 272 | 273 | resp = {} 274 | 275 | resp['llm'] = caption 276 | llm_logger.info(f"{_('Caption Successful')}, {caption}") 277 | # caption = await wd_tagger_handler.tagger_main( 278 | # base64_image, 279 | # data['threshold'], 280 | # data['exclude_tags'] 281 | # ) 282 | # 283 | # resp['caption'] = caption 284 | # wd_logger.info(f"打标成功,{caption}") 285 | return JSONResponse(resp) 286 | 287 | async def get_progress(self): 288 | return JSONResponse(self.backend_instance.format_progress_api_resp(0.0, time.time())) 289 | 290 | async def get_memory(self): 291 | return JSONResponse(self.backend_instance.format_vram_api_resp()) 292 | 293 | @staticmethod 294 | async def get_options(): 295 | return JSONResponse(StaticHandler.get_backend_options()) 296 | 297 | @staticmethod 298 | async def set_options(request: request_model.SetConfigRequest): 299 | 300 | data = request.model_dump() 301 | if data.get('sd_model_checkpoint', None): 302 | logger.info(_("Lock to backend has configured")) 303 | StaticHandler.set_lock_to_backend(data.get('sd_model_checkpoint')) 304 | 305 | return 306 | 307 | @staticmethod 308 | async def topaz_ai(request: request_model.TopazAiRequest): 309 | data = request.model_dump() 310 | 311 | unique_id = str(uuid.uuid4()) 312 | save_dir = Path("saved_images") / unique_id 313 | processed_dir = save_dir / 'processed' 314 | save_dir.mkdir(parents=True, exist_ok=True) 315 | del data['output_folder'] 316 | 317 | try: 318 | 319 | if data['image']: 320 | base64_image = data['image'] 321 | input_image_path = save_dir / f"{unique_id}_image.png" 322 | async with aiofiles.open(input_image_path, "wb") as image_file: 323 | await image_file.write(base64.b64decode(base64_image)) 324 | output, error, return_code = await asyncio.get_running_loop().run_in_executor( 325 | None, topaz.run_tpai( 326 | input_folder=str(save_dir.resolve()), 327 | output_folder=str(processed_dir.resolve()), 328 | **data 329 | ) 330 | ) 331 | elif data['input_folder']: 332 | output, error, return_code = await asyncio.get_running_loop().run_in_executor( 333 | None, topaz.run_tpai( 334 | output_folder=str(processed_dir.resolve()), 335 | **data 336 | ) 337 | ) 338 | except: 339 | traceback.print_exc() 340 | raise HTTPException(status_code=500, detail="Error occurred while processing the image.") 341 | 342 | if return_code == 0: 343 | files = list(processed_dir.glob("*")) 344 | 345 | processed_image_path = files[0] 346 | if processed_image_path.exists(): 347 | async with aiofiles.open(processed_image_path, "rb") as img_file: 348 | encoded_image = base64.b64encode(await img_file.read()).decode('utf-8') 349 | processed_dir.rmdir() 350 | return {"status": "success", "image": encoded_image} 351 | else: 352 | raise HTTPException(status_code=500, detail="Processed image not found.") 353 | else: 354 | raise HTTPException(status_code=500, detail=f"Error: {error}") 355 | 356 | async def download_img_from_url(self, data): 357 | 358 | base64_image = data['image'] 359 | 360 | if data['image'].startswith("http"): 361 | image_url = data['image'] 362 | logger.info(f"{_('URL detected')}: {image_url}") 363 | response = await self.backend_instance.http_request( 364 | "GET", 365 | image_url, 366 | format=False 367 | ) 368 | 369 | if response.status_code != 200: 370 | logger.warning(_("Image download failed!")) 371 | 372 | base64_image = base64.b64encode(response.read()) 373 | 374 | return base64_image 375 | 376 | @staticmethod 377 | async def get_prompt_styles(): 378 | 379 | task_list = [] 380 | path = '/sdapi/v1/prompt-styles' 381 | 382 | task_handler = TaskHandler({}, None, path, reutrn_instance=True, override_model_select=True) 383 | instance_list: list[Backend] = await task_handler.sd_api() 384 | 385 | for i in instance_list: 386 | task_list.append(i.get_all_prompt_style()) 387 | resp = await asyncio.gather(*task_list) 388 | 389 | api_respond = [] 390 | for i in resp: 391 | api_respond += i 392 | 393 | StaticHandler.set_prompt_style(api_respond) 394 | 395 | return api_respond 396 | 397 | async def init_api(self): 398 | await self.get_sd_models() 399 | await self.get_prompt_styles() 400 | 401 | 402 | api_instance = Api() 403 | 404 | 405 | @app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) 406 | async def proxy(path: str, request: Request): 407 | client_host = request.client.host 408 | 409 | task_handler = TaskHandler({}, request, path) 410 | 411 | try: 412 | logger.info(f"{_('Exec forwarding')} - {client_host}") 413 | result = await task_handler.sd_api() 414 | except Exception as e: 415 | logger.error(traceback.format_exc()) 416 | raise HTTPException(500, detail=str(e)) 417 | 418 | if result is None: 419 | raise HTTPException(500, detail='Result not found') 420 | 421 | return result 422 | 423 | 424 | @app.get("/backend-control") 425 | async def get_backend_control(backend: str, key: str, value: bool): 426 | pass 427 | 428 | 429 | @app.on_event("startup") 430 | async def startup_event(): 431 | logger.info(_('Waiting for API initialization')) 432 | await api_instance.init_api() 433 | logger.info(_('API initialization completed')) 434 | 435 | 436 | if __name__ == "__main__": 437 | 438 | # if config.server_settings['start_gradio']: 439 | # demo = create_gradio_interface(host, port) 440 | # app = gradio.mount_gradio_app(api_instance.app, demo, path="/") 441 | 442 | uvicorn.run(api_instance.app, host=host, port=port, log_level="critical" if not args.log else "info") 443 | -------------------------------------------------------------------------------- /DrawBridgeAPI/app.py: -------------------------------------------------------------------------------- 1 | import json 2 | import asyncio 3 | import subprocess 4 | 5 | import gradio as gr 6 | import os 7 | os.environ['CIVITAI_API_TOKEN'] = 'kunkun' 8 | os.environ['FAL_KEY'] = 'Daisuki' 9 | os.environ['CONF_PATH'] = './config.yaml' 10 | from PIL import Image 11 | 12 | import io 13 | import base64 14 | import httpx 15 | from .base_config import init_instance 16 | from .backend import TaskHandler 17 | from .locales import _ 18 | 19 | 20 | class Gradio: 21 | def __init__(self, host, port): 22 | self.host = '127.0.0.1' if host == '0.0.0.0' else host 23 | self.port = port 24 | 25 | def get_caption(self, image): 26 | caption = httpx.post( 27 | f"http://{self.host}:{self.port}/tagger/v1/interrogate", 28 | json=json.loads({"image": image}), timeout=600).json() 29 | return caption 30 | 31 | 32 | def format_caption_output(caption_result): 33 | llm_text = caption_result.get("llm", '') 34 | word_scores = "\n".join([f"{word}: {score}" for word, score in caption_result["caption"].items()]) 35 | word_ = ",".join([f"{word}" for word in caption_result["caption"].keys()]) 36 | return llm_text, word_scores, word_ 37 | 38 | 39 | async def create_gradio_interface(host, port): 40 | 41 | gradio_api = Gradio(host, port) 42 | from .api_server import api_instance 43 | all_models = [i['title'] for i in await api_instance.get_sd_models()] 44 | init_instance.logger.info(f"{_('Server is ready!')} Listen on {host}:{port}") 45 | 46 | async def get_image(model, prompt, negative_prompt, width, height, cfg_scale, steps): 47 | 48 | payload = { 49 | "prompt": prompt, 50 | "negative_prompt": negative_prompt, 51 | "width": width, 52 | "height": height, 53 | "steps": steps, 54 | "cfg_scale": cfg_scale 55 | } 56 | 57 | task_handler = TaskHandler(payload, model_to_backend=model) 58 | result = await task_handler.txt2img() 59 | image_data = result.get("images")[0] 60 | image = Image.open(io.BytesIO(base64.b64decode(image_data))) 61 | return image 62 | 63 | with gr.Blocks() as demo: 64 | with gr.Tab("txt2img"): 65 | with gr.Row(): 66 | with gr.Column(): 67 | model = gr.Dropdown(label="Model", choices=all_models) 68 | prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...") 69 | negative_prompt = gr.Textbox(label="Negative Prompt", 70 | placeholder="Enter your negative prompt here...") 71 | width = gr.Slider(label="Width", minimum=64, maximum=2048, step=1, value=512) 72 | height = gr.Slider(label="Height", minimum=64, maximum=2048, step=1, value=512) 73 | cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=30, step=0.1, value=7.5) 74 | steps = gr.Slider(label="Steps", minimum=1, maximum=200, step=1, value=20) 75 | generate_button = gr.Button("Generate Image") 76 | 77 | with gr.Column(): 78 | output_image = gr.Image(label="Generated Image") 79 | 80 | generate_button.click(get_image, [model, prompt, negative_prompt, width, height, cfg_scale, steps], 81 | output_image) 82 | 83 | with gr.Tab("Caption"): 84 | with gr.Row(): 85 | with gr.Column(): 86 | input_image = gr.Image(label="Input Image") 87 | caption_button = gr.Button("Get Caption") 88 | 89 | with gr.Column(): 90 | llm_output = gr.Textbox(label="Natural Language Description") 91 | word_output_ = gr.Textbox(label="Keywords", lines=6) 92 | word_output = gr.Textbox(label="Keywords with Scores", lines=6) 93 | 94 | caption_button.click( 95 | lambda image: format_caption_output(gradio_api.get_caption(image)), 96 | inputs=[input_image], 97 | outputs=[llm_output, word_output, word_output_] 98 | ) 99 | 100 | return demo 101 | 102 | 103 | async def run_gradio(host, port): 104 | interface = await create_gradio_interface(host, port) 105 | interface.launch(server_name=host, server_port=port+1) 106 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/FLUX_falai.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | import piexif 3 | import fal_client 4 | import os 5 | 6 | from io import BytesIO 7 | from .base import Backend 8 | 9 | 10 | class AIDRAW(Backend): 11 | 12 | def __init__(self, **kwargs): 13 | super().__init__(**kwargs) 14 | 15 | self.logger = self.setup_logger('[FLUX-FalAI]') 16 | 17 | async def get_shape(self): 18 | 19 | aspect_ratio = self.width / self.height 20 | tolerance = 0.05 21 | 22 | def is_close_to_ratio(ratio): 23 | return abs(aspect_ratio - ratio) < tolerance 24 | 25 | if self.width == self.height: 26 | return "square" 27 | elif is_close_to_ratio(4 / 3): 28 | return "portrait_4_3" if self.height > self.width else "landscape_4_3" 29 | elif is_close_to_ratio(16 / 9): 30 | return "portrait_16_9" if self.height > self.width else "landscape_16_9" 31 | else: 32 | return "portrait_4_3" 33 | 34 | async def update_progress(self): 35 | # 覆写函数 36 | pass 37 | 38 | async def get_img_comment(self): 39 | 40 | image_data = self.img_btyes[0] 41 | image_file = BytesIO(image_data) 42 | image_bytes = image_file.getvalue() 43 | exif_dict = piexif.load(image_bytes) 44 | try: 45 | user_comment = exif_dict['Exif'].get(piexif.ExifIFD.UserComment) 46 | except Exception: 47 | return 'No Raw Data' 48 | 49 | return user_comment.decode('utf-8', errors='ignore') 50 | 51 | async def check_backend_usability(self): 52 | pass 53 | 54 | async def err_formating_to_sd_style(self): 55 | 56 | await self.download_img() 57 | 58 | self.format_api_respond() 59 | 60 | self.result = self.build_respond 61 | 62 | async def posting(self): 63 | 64 | os.environ['FAL_KEY'] = self.backend_id 65 | image_shape = await self.get_shape() 66 | self.steps = int(self.steps / 3) 67 | 68 | handler = await fal_client.submit_async( 69 | "fal-ai/flux/schnell", 70 | arguments={ 71 | "prompt": self.prompt, 72 | "image_size": image_shape, 73 | "seed": self.seed, 74 | "num_inference_steps": self.steps, # FLUX不需要很高的步数 75 | "num_images": self.total_img_count, 76 | "enable_safety_checker": True 77 | }, 78 | ) 79 | 80 | response = await handler.get() 81 | 82 | try: 83 | if response['images']: 84 | images_list = response['images'] 85 | for i in images_list: 86 | self.img_url.append(i['url']) 87 | else: 88 | raise ValueError("图片没有被生成,可能是图片没有完成或者结果不可用") 89 | except Exception as e: 90 | self.fail_on_requesting = True 91 | self.logger.error(f"请求API失败: {e}\n{traceback.format_exc()}") 92 | 93 | await self.err_formating_to_sd_style() 94 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/FLUX_replicate.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | import piexif 3 | import os 4 | import replicate 5 | 6 | from io import BytesIO 7 | 8 | from .base import Backend 9 | 10 | 11 | class AIDRAW(Backend): 12 | 13 | def __init__(self, **kwargs): 14 | super().__init__(**kwargs) 15 | self.logger = self.setup_logger('[FLUX-Replicate]') 16 | 17 | async def get_shape(self): 18 | 19 | aspect_ratio = self.width / self.height 20 | tolerance = 0.05 21 | 22 | def is_close_to_ratio(ratio): 23 | return abs(aspect_ratio - ratio) < tolerance 24 | 25 | if self.width == self.height: 26 | return "1:1" 27 | elif is_close_to_ratio(16 / 9): 28 | return "16:9" 29 | elif is_close_to_ratio(21 / 9): 30 | return "21:9" 31 | elif is_close_to_ratio(2 / 3): 32 | return "2:3" 33 | elif is_close_to_ratio(3 / 2): 34 | return "3:2" 35 | elif is_close_to_ratio(4 / 5): 36 | return "4:5" 37 | elif is_close_to_ratio(5 / 4): 38 | return "5:4" 39 | elif is_close_to_ratio(9 / 16): 40 | return "9:16" 41 | elif is_close_to_ratio(9 / 21): 42 | return "9:21" 43 | else: 44 | return "2:3" 45 | 46 | async def update_progress(self): 47 | # 覆写函数 48 | pass 49 | 50 | async def get_img_comment(self): 51 | 52 | image_data = self.img_btyes[0] 53 | image_file = BytesIO(image_data) 54 | image_bytes = image_file.getvalue() 55 | exif_dict = piexif.load(image_bytes) 56 | try: 57 | user_comment = exif_dict['Exif'].get(piexif.ExifIFD.UserComment) 58 | except Exception: 59 | return 'No Raw Data' 60 | 61 | return user_comment.decode('utf-8', errors='ignore') 62 | 63 | async def check_backend_usability(self): 64 | pass 65 | 66 | async def err_formating_to_sd_style(self): 67 | 68 | await self.download_img() 69 | 70 | self.format_api_respond() 71 | 72 | self.result = self.build_respond 73 | 74 | async def posting(self): 75 | 76 | os.environ['REPLICATE_API_TOKEN'] = self.backend_id 77 | image_shape = await self.get_shape() 78 | 79 | input_ = { 80 | "prompt": self.prompt, 81 | "seed": self.seed, 82 | "num_outputs": self.total_img_count, 83 | "aspect_ratio": image_shape, 84 | "output_format": 'png', 85 | "output_quality": 90 86 | } 87 | 88 | output = await replicate.async_run( 89 | "black-forest-labs/flux-schnell", 90 | input=input_ 91 | ) 92 | 93 | try: 94 | if output: 95 | for i in output: 96 | self.img_url.append(i) 97 | else: 98 | raise ValueError("图片没有被生成,可能是图片没有完成或者结果不可用") 99 | except Exception as e: 100 | self.fail_on_requesting = True 101 | self.logger.error(f"请求API失败: {e}\n{traceback.format_exc()}") 102 | 103 | await self.err_formating_to_sd_style() 104 | 105 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/SD_A1111_webui.py: -------------------------------------------------------------------------------- 1 | from urllib.parse import urlencode 2 | 3 | from .base import Backend 4 | 5 | 6 | class AIDRAW(Backend): 7 | 8 | def __init__(self, **kwargs): 9 | super().__init__(**kwargs) 10 | 11 | self.logger = self.setup_logger('[SD-A1111]') 12 | 13 | async def exec_login(self): 14 | login_data = { 15 | 'username': self.current_config['username'][self.count], 16 | 'password': self.current_config['password'][self.count] 17 | } 18 | encoded_data = urlencode(login_data) 19 | 20 | response = await self.http_request( 21 | method="POST", 22 | target_url=f"{self.backend_id}/login", 23 | headers={ 24 | "Content-Type": "application/x-www-form-urlencoded", 25 | "accept": "application/json" 26 | }, 27 | content=encoded_data, 28 | ) 29 | if response.get('error') == "error": 30 | self.logger.warning(f"后端{self.backend_name}登录失败") 31 | self.fail_on_login = True 32 | return False, 500 33 | else: 34 | self.logger.info(f"后端{self.backend_name}登录成功") 35 | return True, 200 36 | 37 | async def check_backend_usability(self): 38 | 39 | if self.login: 40 | resp = await self.exec_login() 41 | if resp[0] is None: 42 | self.fail_on_login = True 43 | self.logger.warning(f"后端{self.backend_name}登陆失败") 44 | return False, resp 45 | 46 | async def get_backend_working_progress(self): 47 | """ 48 | 获取后端工作进度, 默认A1111 49 | :return: 50 | """ 51 | respond = await self.http_request( 52 | "GET", 53 | f"{self.backend_id}/sdapi/v1/options", 54 | verify=False, 55 | proxy=False, 56 | use_aiohttp=False 57 | ) 58 | 59 | self.model = respond['sd_model_checkpoint'] 60 | self.model_hash = respond 61 | 62 | if self.current_config['auth'][self.count]: 63 | self.login = True 64 | await self.exec_login() 65 | 66 | api_url = f"{self.backend_id}/sdapi/v1/progress" 67 | 68 | resp = await self.http_request( 69 | method="GET", 70 | target_url=api_url, 71 | format=False 72 | ) 73 | 74 | resp_json = resp.json() 75 | return resp_json, resp.status_code, self.backend_id, resp.status_code 76 | 77 | 78 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/SD_civitai_API.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | import piexif 3 | import os 4 | import civitai 5 | 6 | from io import BytesIO 7 | 8 | from .base import Backend 9 | 10 | class AIDRAW(Backend): 11 | 12 | def __init__(self, **kwargs): 13 | super().__init__(**kwargs) 14 | 15 | self.logger = self.setup_logger('[Civitai]') 16 | 17 | async def update_progress(self): 18 | # 覆写函数 19 | pass 20 | 21 | async def get_img_comment(self): 22 | 23 | image_data = self.img_btyes[0] 24 | image_file = BytesIO(image_data) 25 | image_bytes = image_file.getvalue() 26 | exif_dict = piexif.load(image_bytes) 27 | try: 28 | user_comment = exif_dict['Exif'].get(piexif.ExifIFD.UserComment) 29 | except KeyError: 30 | return 'No Raw Data' 31 | 32 | return user_comment.decode('utf-8', errors='ignore') 33 | 34 | async def check_backend_usability(self): 35 | 36 | self.headers['Authorization'] = f"Bearer {self.backend_id}" 37 | response = await self.http_request( 38 | method="GET", 39 | target_url='https://civitai.com/api/v1/models', 40 | headers=self.headers, 41 | params=None, 42 | format=True 43 | ) 44 | 45 | if isinstance(response, dict) and 'error' in response: 46 | self.fail_on_login = True 47 | return False 48 | else: 49 | resp_json = response 50 | return True, (resp_json, 200) 51 | 52 | async def err_formating_to_sd_style(self): 53 | 54 | await self.download_img() 55 | self.format_api_respond() 56 | self.result = self.build_respond 57 | 58 | async def posting(self): 59 | 60 | self.logger.info(f"开始使用{self.backend_id}获取图片") 61 | 62 | os.environ['CIVITAI_API_TOKEN'] = self.backend_id 63 | os.environ['HTTP_PROXY'] = self.config.server_settings['proxy'] 64 | os.environ['HTTPS_PROXY'] = self.config.server_settings['proxy'] 65 | await self.check_backend_usability() 66 | 67 | input_ = { 68 | "model": "urn:air:sd1:checkpoint:civitai:4201@130072", 69 | "params": { 70 | "prompt": self.prompt, 71 | "negativePrompt": self.negative_prompt, 72 | "scheduler": self.sampler, 73 | "steps": self.steps, 74 | "cfgScale": self.scale, 75 | "width": self.width, 76 | "height": self.height, 77 | "clipSkip": 2, 78 | "seed": self.seed 79 | } 80 | } 81 | 82 | self.logger.info(f"任务已经发送!本次生图{self.total_img_count}张") 83 | 84 | for i in range(self.total_img_count): 85 | 86 | try: 87 | response = await civitai.image.create(input_, wait=True) 88 | if response['jobs'][0]['result'].get('available'): 89 | self.img_url.append(response['jobs'][0]['result'].get('blobUrl')) 90 | else: 91 | raise ValueError("图片没有被生成,可能是图片没有完成或者结果不可用") 92 | except Exception as e: 93 | self.fail_on_requesting = True 94 | self.logger.error(f"请求API失败: {e}\n{traceback.format_exc()}") 95 | 96 | await self.err_formating_to_sd_style() 97 | 98 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/__init__.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import random 3 | import json 4 | import time 5 | import traceback 6 | 7 | import aiofiles 8 | 9 | from tqdm import tqdm 10 | from pathlib import Path 11 | from fastapi import Request 12 | from fastapi.responses import JSONResponse 13 | from typing import Union 14 | from colorama import Fore, Style 15 | from colorama import init 16 | init() 17 | 18 | from ..base_config import setup_logger, init_instance 19 | from .base import Backend 20 | 21 | import_logger = setup_logger('[IMPORT_BACKEND]') 22 | 23 | from DrawBridgeAPI.locales import _ as i18n 24 | 25 | 26 | class BaseHandler: 27 | # 28 | selected_instance_list: list[Backend] = [] 29 | class_list: list[Backend] = [] 30 | class_dict: dict = {} 31 | selected_instance_dict: dict[list[Backend]] = {} 32 | init_parameters_list: list = [] 33 | init_parameters_dict: dict = {} 34 | enable_backend: dict = {} 35 | 36 | def __init__( 37 | self, 38 | payload, 39 | request: Request = None, 40 | path: str = None, 41 | comfyui_task=None, 42 | ): 43 | self.task_list = [] 44 | self.instance_list: list[Backend] = [] 45 | self.payload = payload 46 | self.request = request 47 | self.path = path 48 | self.config = init_instance.config 49 | self.all_task_list = None 50 | self.comfyui_task: str = comfyui_task 51 | self.task_type: str = "enable_txt2img_backends" 52 | 53 | async def get_enable_task( 54 | self, 55 | task_type, 56 | ): 57 | """ 58 | 此函数的作用是获取示例并且只保留选择了的后端 59 | :param enable_task: 60 | :return: 61 | """ 62 | 63 | self.init_parameters_list = [] 64 | self.selected_instance_list = [] 65 | self.class_list = [] 66 | update_dict = {} 67 | 68 | if self.selected_instance_dict.get(task_type, None): 69 | 70 | self.instance_list = self.selected_instance_dict[task_type] 71 | return 72 | 73 | enable_backend_list = self.config.enable_backends[task_type] 74 | 75 | for enable_backend, backend_setting in enable_backend_list.items(): 76 | 77 | def create_and_append_instances( 78 | enable_backend_type, 79 | AIDRAW_class, 80 | backend_setting, 81 | extra_args: dict=None 82 | ): 83 | 84 | enable_queue = False 85 | 86 | for counter in backend_setting: 87 | if isinstance(counter, int): 88 | enable_queue = False 89 | 90 | elif isinstance(counter, dict): 91 | operation = list(counter.values())[0] 92 | counter = int(list(counter.keys())[0]) 93 | enable_queue = True if operation == "queue" else False 94 | 95 | override = self.config.backends[enable_backend_type].get("override", None) 96 | try: 97 | if override is not None: 98 | override = override[counter] 99 | else: 100 | override = None 101 | except IndexError: 102 | override = None 103 | 104 | init_args = { 105 | "count": counter, 106 | "payload": self.payload, 107 | "enable_queue": enable_queue, 108 | "backend_type": enable_backend_type, 109 | "override_setting": override, 110 | 111 | } 112 | 113 | if extra_args: 114 | init_args.update(extra_args) 115 | 116 | self.init_parameters_list.append(init_args) 117 | 118 | aidraw_instance = AIDRAW_class( 119 | **init_args 120 | ) 121 | update_dict.update({ 122 | self.config.backends[enable_backend_type]["name"][counter]: 123 | self.config.backends[enable_backend_type]["api"][counter] 124 | } 125 | ) 126 | 127 | aidraw_instance.init_backend_info() 128 | import_logger.info("Backend {0} is enabled".format(self.config.backends[enable_backend_type]['name'][counter])) 129 | 130 | self.selected_instance_list.append(aidraw_instance) 131 | self.class_list.append(AIDRAW_class) 132 | 133 | if "civitai" in enable_backend: 134 | from .SD_civitai_API import AIDRAW 135 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 136 | 137 | elif "a1111webui" in enable_backend: 138 | from .SD_A1111_webui import AIDRAW 139 | 140 | create_and_append_instances(enable_backend, AIDRAW, backend_setting, 141 | extra_args={"request": self.request, "path": self.path}) 142 | 143 | elif "fal_ai" in enable_backend: 144 | from .FLUX_falai import AIDRAW 145 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 146 | 147 | elif "replicate" in enable_backend: 148 | from .FLUX_replicate import AIDRAW 149 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 150 | 151 | elif "liblibai" in enable_backend: 152 | from .liblibai import AIDRAW 153 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 154 | 155 | elif "tusiart" in enable_backend: 156 | from .tusiart import AIDRAW 157 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 158 | 159 | elif "seaart" in enable_backend: 160 | from .seaart import AIDRAW 161 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 162 | 163 | elif "yunjie" in enable_backend: 164 | from .yunjie import AIDRAW 165 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 166 | 167 | elif "comfyui" in enable_backend: 168 | from .comfyui import AIDRAW 169 | 170 | create_and_append_instances(enable_backend, AIDRAW, backend_setting, extra_args={ 171 | "request": self.request, 172 | "path": self.path 173 | }) 174 | 175 | elif "novelai" in enable_backend: 176 | from .novelai import AIDRAW 177 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 178 | 179 | elif "midjourney" in enable_backend: 180 | from .midjourney import AIDRAW 181 | create_and_append_instances(enable_backend, AIDRAW, backend_setting) 182 | 183 | self.enable_backend[task_type] = update_dict 184 | self.init_parameters_dict[task_type] = self.init_parameters_list 185 | self.selected_instance_dict[task_type] = self.selected_instance_list 186 | self.instance_list = self.selected_instance_list 187 | self.class_dict[task_type] = self.class_list 188 | 189 | 190 | class TXT2IMGHandler(BaseHandler): 191 | 192 | def __init__(self, payload, comfyui_task: str = None): 193 | super().__init__(comfyui_task=comfyui_task, payload=payload) 194 | 195 | async def get_all_instance(self) -> tuple[list[Backend], dict, dict, dict]: 196 | 197 | self.task_type = "enable_txt2img_backends" 198 | await self.get_enable_task(self.task_type) 199 | return self.instance_list, self.enable_backend, self.init_parameters_dict, self.class_dict 200 | 201 | 202 | class IMG2IMGHandler(BaseHandler): 203 | 204 | def __init__(self, payload, comfyui_task: str = None): 205 | super().__init__(comfyui_task=comfyui_task, payload=payload) 206 | 207 | async def get_all_instance(self) -> tuple[list[Backend], dict, dict, dict]: 208 | self.task_type = "enable_img2img_backends" 209 | await self.get_enable_task(self.task_type) 210 | return self.instance_list, self.enable_backend, self.init_parameters_dict, self.class_dict 211 | 212 | 213 | class A1111WebuiHandlerAPI(BaseHandler): 214 | async def get_all_instance(self) -> tuple[list[Backend], dict, dict, dict]: 215 | 216 | self.task_type = "enable_sdapi_backends" 217 | await self.get_enable_task(self.task_type) 218 | return self.instance_list, self.enable_backend, self.init_parameters_dict, self.class_dict 219 | 220 | 221 | class ComfyUIHandler(BaseHandler): 222 | 223 | async def get_all_instance(self) -> tuple[list[Backend], dict, dict, dict]: 224 | 225 | self.task_type = "enable_comfyui_backends" 226 | await self.get_enable_task(self.task_type) 227 | return self.instance_list, self.enable_backend, self.init_parameters_dict, self.class_dict 228 | 229 | 230 | class StaticHandler: 231 | lock_to_backend = None 232 | prompt_style: list = None 233 | 234 | @classmethod 235 | def set_lock_to_backend(cls, selected_model: str): 236 | cls.lock_to_backend = selected_model 237 | 238 | @classmethod 239 | def get_lock_to_backend(cls): 240 | return cls.lock_to_backend 241 | 242 | @classmethod 243 | def get_prompt_style(cls): 244 | return cls.prompt_style 245 | 246 | @classmethod 247 | def set_prompt_style(cls, prompt_style: list): 248 | cls.prompt_style = prompt_style 249 | 250 | @classmethod 251 | def get_backend_options(cls): 252 | build_resp = { 253 | "samples_save": True, 254 | "samples_format": "png", 255 | "samples_filename_pattern": "", 256 | "save_images_add_number": True, 257 | "grid_save": True, 258 | "grid_format": "png", 259 | "grid_extended_filename": False, 260 | "grid_only_if_multiple": True, 261 | "grid_prevent_empty_spots": False, 262 | "grid_zip_filename_pattern": "", 263 | "n_rows": -1.0, 264 | "font": "", 265 | "grid_text_active_color": "#000000", 266 | "grid_text_inactive_color": "#999999", 267 | "grid_background_color": "#ffffff", 268 | "enable_pnginfo": True, 269 | "save_txt": False, 270 | "save_images_before_face_restoration": False, 271 | "save_images_before_highres_fix": False, 272 | "save_images_before_color_correction": False, 273 | "save_mask": False, 274 | "save_mask_composite": False, 275 | "jpeg_quality": 80.0, 276 | "webp_lossless": False, 277 | "export_for_4chan": True, 278 | "img_downscale_threshold": 4.0, 279 | "target_side_length": 4000.0, 280 | "img_max_size_mp": 200.0, 281 | "use_original_name_batch": True, 282 | "use_upscaler_name_as_suffix": False, 283 | "save_selected_only": True, 284 | "save_init_img": False, 285 | "temp_dir": "", 286 | "clean_temp_dir_at_start": False, 287 | "save_incomplete_images": False, 288 | "outdir_samples": "", 289 | "outdir_txt2img_samples": "outputs/txt2img-images", 290 | "outdir_img2img_samples": "outputs/img2img-images", 291 | "outdir_extras_samples": "outputs/extras-images", 292 | "outdir_grids": "", 293 | "outdir_txt2img_grids": "outputs/txt2img-grids", 294 | "outdir_img2img_grids": "outputs/img2img-grids", 295 | "outdir_save": "log/images", 296 | "outdir_init_images": "outputs/init-images", 297 | "save_to_dirs": True, 298 | "grid_save_to_dirs": True, 299 | "use_save_to_dirs_for_ui": False, 300 | "directories_filename_pattern": "[date]", 301 | "directories_max_prompt_words": 8.0, 302 | "ESRGAN_tile": 192.0, 303 | "ESRGAN_tile_overlap": 8.0, 304 | "realesrgan_enabled_models": [ 305 | "R-ESRGAN 4x+", 306 | "R-ESRGAN 4x+ Anime6B" 307 | ], 308 | "upscaler_for_img2img": None, 309 | "face_restoration": False, 310 | "face_restoration_model": "CodeFormer", 311 | "code_former_weight": 0.5, 312 | "face_restoration_unload": False, 313 | "auto_launch_browser": "Local", 314 | "show_warnings": False, 315 | "show_gradio_deprecation_warnings": True, 316 | "memmon_poll_rate": 8.0, 317 | "samples_log_stdout": False, 318 | "multiple_tqdm": True, 319 | "print_hypernet_extra": False, 320 | "list_hidden_files": True, 321 | "disable_mmap_load_safetensors": False, 322 | "hide_ldm_prints": True, 323 | "api_enable_requests": True, 324 | "api_forbid_local_requests": True, 325 | "api_useragent": "", 326 | "unload_models_when_training": False, 327 | "pin_memory": False, 328 | "save_optimizer_state": False, 329 | "save_training_settings_to_txt": True, 330 | "dataset_filename_word_regex": "", 331 | "dataset_filename_join_string": " ", 332 | "training_image_repeats_per_epoch": 1.0, 333 | "training_write_csv_every": 500.0, 334 | "training_xattention_optimizations": False, 335 | "training_enable_tensorboard": False, 336 | "training_tensorboard_save_images": False, 337 | "training_tensorboard_flush_every": 120.0, 338 | "sd_model_checkpoint": cls.lock_to_backend if cls.lock_to_backend else 'DrawBridgeAPI-Auto-Select', 339 | "sd_checkpoints_limit": 1.0, 340 | "sd_checkpoints_keep_in_cpu": True, 341 | "sd_checkpoint_cache": 3, 342 | "sd_unet": "None", 343 | "enable_quantization": False, 344 | "enable_emphasis": True, 345 | "enable_batch_seeds": True, 346 | "comma_padding_backtrack": 20.0, 347 | "CLIP_stop_at_last_layers": 3.0, 348 | "upcast_attn": False, 349 | "randn_source": "GPU", 350 | "tiling": False, 351 | "hires_fix_refiner_pass": "second pass", 352 | "sdxl_crop_top": 0.0, 353 | "sdxl_crop_left": 0.0, 354 | "sdxl_refiner_low_aesthetic_score": 2.5, 355 | "sdxl_refiner_high_aesthetic_score": 6.0, 356 | "sd_vae_explanation": "VAE is a neural network that transforms a standard RGB\nimage into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling\n(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.\nFor img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.", 357 | "sd_vae_checkpoint_cache": 0, 358 | "sd_vae": "None", 359 | "sd_vae_overrides_per_model_preferences": False, 360 | "auto_vae_precision": True, 361 | "sd_vae_encode_method": "Full", 362 | "sd_vae_decode_method": "Full", 363 | "inpainting_mask_weight": 1.0, 364 | "initial_noise_multiplier": 1.0, 365 | "img2img_extra_noise": 0, 366 | "img2img_color_correction": False, 367 | "img2img_fix_steps": False, 368 | "img2img_background_color": "#ffffff", 369 | "img2img_editor_height": 720.0, 370 | "img2img_sketch_default_brush_color": "#ffffff", 371 | "img2img_inpaint_mask_brush_color": "#ffffff", 372 | "img2img_inpaint_sketch_default_brush_color": "#ffffff", 373 | "return_mask": False, 374 | "return_mask_composite": False, 375 | "cross_attention_optimization": "Automatic", 376 | "s_min_uncond": 0.0, 377 | "token_merging_ratio": 0.0, 378 | "token_merging_ratio_img2img": 0.0, 379 | "token_merging_ratio_hr": 0.0, 380 | "pad_cond_uncond": False, 381 | "persistent_cond_cache": True, 382 | "batch_cond_uncond": True, 383 | "use_old_emphasis_implementation": False, 384 | "use_old_karras_scheduler_sigmas": False, 385 | "no_dpmpp_sde_batch_determinism": False, 386 | "use_old_hires_fix_width_height": False, 387 | "dont_fix_second_order_samplers_schedule": False, 388 | "hires_fix_use_firstpass_conds": False, 389 | "use_old_scheduling": False, 390 | "interrogate_keep_models_in_memory": False, 391 | "interrogate_return_ranks": False, 392 | "interrogate_clip_num_beams": 1.0, 393 | "interrogate_clip_min_length": 24.0, 394 | "interrogate_clip_max_length": 48.0, 395 | "interrogate_clip_dict_limit": 1500.0, 396 | "interrogate_clip_skip_categories": [], 397 | "interrogate_deepbooru_score_threshold": 0.5, 398 | "deepbooru_sort_alpha": True, 399 | "deepbooru_use_spaces": True, 400 | "deepbooru_escape": True, 401 | "deepbooru_filter_tags": "", 402 | "extra_networks_show_hidden_directories": True, 403 | "extra_networks_hidden_models": "When searched", 404 | "extra_networks_default_multiplier": 1.0, 405 | "extra_networks_card_width": 0, 406 | "extra_networks_card_height": 0, 407 | "extra_networks_card_text_scale": 1.0, 408 | "extra_networks_card_show_desc": True, 409 | "extra_networks_add_text_separator": " ", 410 | "ui_extra_networks_tab_reorder": "", 411 | "textual_inversion_print_at_load": False, 412 | "textual_inversion_add_hashes_to_infotext": True, 413 | "sd_hypernetwork": "None", 414 | "localization": "None", 415 | "gradio_theme": "Default", 416 | "gradio_themes_cache": True, 417 | "gallery_height": "", 418 | "return_grid": True, 419 | "do_not_show_images": False, 420 | "send_seed": True, 421 | "send_size": True, 422 | "js_modal_lightbox": True, 423 | "js_modal_lightbox_initially_zoomed": True, 424 | "js_modal_lightbox_gamepad": False, 425 | "js_modal_lightbox_gamepad_repeat": 250.0, 426 | "show_progress_in_title": True, 427 | "samplers_in_dropdown": True, 428 | "dimensions_and_batch_together": True, 429 | "keyedit_precision_attention": 0.1, 430 | "keyedit_precision_extra": 0.05, 431 | "keyedit_delimiters": ".,\\/!?%^*;:{}=`~()", 432 | "keyedit_move": True, 433 | "quicksettings_list": [ 434 | "sd_model_checkpoint", 435 | "sd_unet", 436 | "sd_vae", 437 | "CLIP_stop_at_last_layers" 438 | ], 439 | "ui_tab_order": [], 440 | "hidden_tabs": [], 441 | "ui_reorder_list": [], 442 | "hires_fix_show_sampler": False, 443 | "hires_fix_show_prompts": False, 444 | "disable_token_counters": False, 445 | "add_model_hash_to_info": True, 446 | "add_model_name_to_info": True, 447 | "add_user_name_to_info": False, 448 | "add_version_to_infotext": True, 449 | "disable_weights_auto_swap": True, 450 | "infotext_styles": "Apply if any", 451 | "show_progressbar": True, 452 | "live_previews_enable": True, 453 | "live_previews_image_format": "png", 454 | "show_progress_grid": True, 455 | "show_progress_every_n_steps": 10.0, 456 | "show_progress_type": "Approx NN", 457 | "live_preview_allow_lowvram_full": False, 458 | "live_preview_content": "Prompt", 459 | "live_preview_refresh_period": 1000.0, 460 | "live_preview_fast_interrupt": False, 461 | "hide_samplers": [], 462 | "eta_ddim": 0.0, 463 | "eta_ancestral": 1.0, 464 | "ddim_discretize": "uniform", 465 | "s_churn": 0.0, 466 | "s_tmin": 0.0, 467 | "s_tmax": 0, 468 | "s_noise": 1.0, 469 | "k_sched_type": "Automatic", 470 | "sigma_min": 0.0, 471 | "sigma_max": 0.0, 472 | "rho": 0.0, 473 | "eta_noise_seed_delta": 0, 474 | "always_discard_next_to_last_sigma": False, 475 | "sgm_noise_multiplier": False, 476 | "uni_pc_variant": "bh1", 477 | "uni_pc_skip_type": "time_uniform", 478 | "uni_pc_order": 3.0, 479 | "uni_pc_lower_order_final": True, 480 | "postprocessing_enable_in_main_ui": [], 481 | "postprocessing_operation_order": [], 482 | "upscaling_max_images_in_cache": 5.0, 483 | "disabled_extensions": [], 484 | "disable_all_extensions": "none", 485 | "restore_config_state_file": "", 486 | "sd_checkpoint_hash": "91e0f7cbaf70676153810c231e8703bf26b3208c116a3d1f2481cbc666905471" 487 | } 488 | 489 | return build_resp 490 | 491 | 492 | class TaskHandler(StaticHandler): 493 | 494 | backend_avg_dict: dict = {} 495 | write_count: dict = {} 496 | backend_images: dict = {} 497 | 498 | backend_site_list = None 499 | load_balance_logger = setup_logger('[AvgTimeCalculator]') 500 | load_balance_sample = 10 501 | 502 | redis_client = None 503 | backend_status = None 504 | 505 | @classmethod 506 | def update_backend_status(cls): 507 | cls.backend_status = json.loads(cls.redis_client.get("workload")) 508 | 509 | @classmethod 510 | def get_redis_client(cls): 511 | cls.redis_client = init_instance.redis_client 512 | 513 | @classmethod 514 | async def get_backend_avg_work_time(cls) -> dict: 515 | backend_sites = cls.backend_site_list 516 | 517 | avg_time_key = "" 518 | 519 | avg_time_data = cls.redis_client.get("backend_avg_time") 520 | if avg_time_data is None: 521 | cls.redis_client.set(avg_time_key, json.dumps(cls.backend_avg_dict)) 522 | else: 523 | new_data = json.loads(avg_time_data) 524 | for key, values in new_data.items(): 525 | if key in cls.backend_avg_dict: 526 | cls.backend_avg_dict[key].extend( 527 | values[-cls.load_balance_sample:] if len(values) >= cls.load_balance_sample else 528 | values 529 | ) 530 | else: 531 | cls.backend_avg_dict[key] = (values[-cls.load_balance_sample:] if 532 | len(values) >= cls.load_balance_sample else values) 533 | 534 | cls.backend_avg_dict[key] = cls.backend_avg_dict[key][-10:] 535 | 536 | avg_time_dict = {} 537 | for backend_site in backend_sites: 538 | spend_time_list = cls.backend_avg_dict.get(backend_site, []) 539 | if spend_time_list and len(spend_time_list) >= cls.load_balance_sample: 540 | sorted_list = sorted(spend_time_list) 541 | trimmed_list = sorted_list[1:-1] 542 | avg_time = sum(trimmed_list) / len(trimmed_list) if trimmed_list else None 543 | avg_time_dict[backend_site] = avg_time 544 | else: 545 | avg_time_dict[backend_site] = None 546 | 547 | return avg_time_dict 548 | 549 | @classmethod 550 | async def set_backend_work_time(cls, spend_time, backend_site, total_images=1): 551 | spend_time_list = cls.backend_avg_dict.get(backend_site, []) 552 | spend_time_list.append(int(spend_time/total_images)) 553 | 554 | if len(spend_time_list) >= cls.load_balance_sample: 555 | spend_time_list = spend_time_list[-cls.load_balance_sample:] 556 | 557 | cls.backend_avg_dict[backend_site] = spend_time_list 558 | 559 | cls.write_count[backend_site] = cls.write_count.get(backend_site, 0) + 1 560 | 561 | if cls.write_count.get(backend_site, 0) >= cls.load_balance_sample: 562 | cls.redis_client.set("backend_avg_time", json.dumps(cls.backend_avg_dict)) 563 | cls.write_count[backend_site] = 0 564 | 565 | # info_str = '' 566 | 567 | # for key, values in cls.backend_avg_dict.items(): 568 | # info_str += f"{key}: 最近10次生成时间{values}\n" 569 | # 570 | # cls.load_balance_logger.info(info_str) 571 | 572 | @classmethod 573 | def set_backend_image(cls, num=0, backend_site=None, get=False) -> Union[None, dict]: 574 | all_backend_dict = {} 575 | 576 | if backend_site: 577 | working_images = cls.backend_images.get(backend_site, 1) 578 | working_images += num 579 | cls.backend_images[backend_site] = working_images 580 | 581 | if get: 582 | for site in cls.backend_site_list: 583 | all_backend_dict[site] = cls.backend_images.get(site, 1) 584 | return all_backend_dict 585 | 586 | @classmethod 587 | def set_backend_list(cls, backend_dict): 588 | cls.backend_site_list = list(backend_dict.values()) 589 | 590 | def __init__( 591 | self, 592 | payload=None, 593 | request: Request = None, 594 | path: str = None, 595 | select_backend: int = None, 596 | reutrn_instance: bool = False, 597 | model_to_backend: str = None, 598 | disable_loadbalance: bool = False, 599 | comfyui_json: str = "", 600 | override_model_select: bool = False, 601 | ): 602 | self.payload = payload 603 | self.instance_list: list[Backend] = [] 604 | self.class_dict: dict = {} 605 | self.parameters_list = {} 606 | self.result = None 607 | self.request = request 608 | self.path = path 609 | self.enable_backend: dict[str: dict] = None 610 | self.reutrn_instance = reutrn_instance 611 | self.select_backend = select_backend 612 | self.model_to_backend = model_to_backend # 模型的名称 613 | self.disable_loadbalance = disable_loadbalance 614 | self.lock_to_backend = self.get_lock_to_backend() if override_model_select is False else None 615 | self.comfyui_json: str = comfyui_json 616 | 617 | self.total_images = (self.payload.get("batch_size", 1) * self.payload.get("n_iter", 1)) or 1 618 | 619 | self.ava_backend_url = None 620 | self.ava_backend_index = None 621 | 622 | self.task_type: str = None 623 | 624 | @staticmethod 625 | def get_backend_name(model_name) -> str: 626 | all_model: bytes = init_instance.redis_client.get('models') 627 | all_model: dict = json.loads(all_model.decode('utf-8')) 628 | for key, models in all_model.items(): 629 | if isinstance(models, list): 630 | for model in models: 631 | if model.get("title") == model_name or model.get("model_name") == model_name: 632 | return key 633 | 634 | @staticmethod 635 | def get_backend_index(mapping_dict, key_to_find) -> int: 636 | keys = list(mapping_dict.keys()) 637 | if key_to_find in keys: 638 | return keys.index(key_to_find) 639 | return None 640 | 641 | async def txt2img(self): 642 | 643 | self.instance_list, self.enable_backend, self.parameters_list, self.class_dict = await TXT2IMGHandler( 644 | self.payload, 645 | comfyui_task=self.comfyui_json 646 | ).get_all_instance() 647 | 648 | await self.choice_backend(task_type="enable_txt2img_backends") 649 | return self.result 650 | 651 | async def img2img(self): 652 | 653 | self.instance_list, self.enable_backend, self.parameters_list, self.class_dict = await IMG2IMGHandler( 654 | self.payload, 655 | comfyui_task=self.comfyui_json 656 | ).get_all_instance() 657 | 658 | await self.choice_backend(task_type="enable_img2img_backends") 659 | return self.result 660 | 661 | async def sd_api(self) -> JSONResponse | list[Backend]: 662 | 663 | self.instance_list, self.enable_backend, self.parameters_list, self.class_dict = await A1111WebuiHandlerAPI( 664 | self.payload, 665 | self.request, 666 | self.path 667 | ).get_all_instance() 668 | 669 | await self.choice_backend(task_type="enable_sdapi_backends") 670 | return self.result 671 | 672 | async def comfyui_api(self) -> JSONResponse | list[Backend]: 673 | 674 | self.instance_list, self.enable_backend, self.parameters_list, self.class_dict = await ComfyUIHandler( 675 | self.payload, 676 | self.request, 677 | self.path 678 | ).get_all_instance() 679 | 680 | await self.choice_backend(task_type="enable_comfyui_backends") 681 | return self.result 682 | 683 | async def choice_backend(self, task_type: str): 684 | 685 | self.task_type = task_type 686 | 687 | from DrawBridgeAPI.locales import _ as i18n 688 | 689 | if self.disable_loadbalance: 690 | return 691 | 692 | backend_url_dict = self.enable_backend[task_type] 693 | 694 | self.set_backend_list(backend_url_dict) 695 | self.get_redis_client() 696 | 697 | tasks = [] 698 | is_avaiable = 0 699 | status_dict = {} 700 | ava_url = None 701 | n = -1 702 | e = -1 703 | normal_backend = [] 704 | idle_backend = [] 705 | 706 | logger = setup_logger(custom_prefix='[LOAD_BALANCE]') 707 | 708 | if self.reutrn_instance: 709 | self.result = self.instance_list 710 | return 711 | for i in self.instance_list: 712 | task = i.get_backend_working_progress() 713 | tasks.append(task) 714 | # 获取api队列状态 715 | key = self.get_backend_name(self.model_to_backend or self.lock_to_backend) 716 | if self.model_to_backend and key is not None: 717 | 718 | backend_index = self.get_backend_index(backend_url_dict, key) 719 | logger.info(f"{i18n('Manually select model')}: {self.model_to_backend}, {i18n('Backend select')}{key[:24]}") 720 | 721 | self.ava_backend_url = backend_url_dict[key] 722 | self.ava_backend_index = backend_index 723 | 724 | await self.exec_generate() 725 | 726 | elif self.lock_to_backend: 727 | if self.lock_to_backend and key is not None: 728 | backend_index = self.get_backend_index(backend_url_dict, key) 729 | logger.info(f"{i18n('Backend locked')}: {key[:24]}") 730 | 731 | self.ava_backend_url = backend_url_dict[key] 732 | self.ava_backend_index = backend_index 733 | 734 | await self.exec_generate() 735 | 736 | else: 737 | all_resp = await asyncio.gather(*tasks, return_exceptions=True) 738 | logger.info(i18n('Starting backend selection')) 739 | for resp_tuple in all_resp: 740 | e += 1 741 | if isinstance(resp_tuple, None or Exception): 742 | logger.warning(i18n('Backend {0} is down').format(self.instance_list[e].workload_name)) 743 | else: 744 | try: 745 | if resp_tuple[3] in [200, 201]: 746 | n += 1 747 | status_dict[resp_tuple[2]] = resp_tuple[0]["eta_relative"] 748 | normal_backend = (list(status_dict.keys())) 749 | else: 750 | raise RuntimeError 751 | except (RuntimeError, TypeError): 752 | logger.warning(i18n('Backend {0} is failed or locked').format(self.instance_list[e].workload_name)) 753 | continue 754 | else: 755 | # 更改判断逻辑 756 | if resp_tuple[0]["progress"] in [0, 0.0]: 757 | is_avaiable += 1 758 | idle_backend.append(normal_backend[n]) 759 | else: 760 | pass 761 | # 显示进度 762 | total = 100 763 | progress = int(resp_tuple[0]["progress"] * 100) 764 | show_str = f"{self.instance_list[e].workload_name}" 765 | show_str = show_str.ljust(50, "-") 766 | 767 | bar_format = f"{Fore.CYAN}[Progress] {{l_bar}}{{bar}}|{Style.RESET_ALL}" 768 | 769 | with tqdm( 770 | total=total, 771 | desc=show_str + "-->", 772 | bar_format=bar_format 773 | ) as pbar: 774 | pbar.update(progress) 775 | if len(normal_backend) == 0: 776 | logger.error(i18n('No available backend')) 777 | raise RuntimeError(i18n('No available backend')) 778 | 779 | backend_total_work_time = {} 780 | avg_time_dict = await self.get_backend_avg_work_time() 781 | backend_image = self.set_backend_image(get=True) 782 | 783 | eta = 0 784 | 785 | for (site, time_), (_, image_count) in zip(avg_time_dict.items(), backend_image.items()): 786 | self.load_balance_logger.info( 787 | i18n('Backend: {0} Average work time: {1} seconds, Current tasks: {2}').format(site, time_, image_count - 1) 788 | ) 789 | 790 | if site in normal_backend: 791 | self.update_backend_status() 792 | for key in self.backend_status: 793 | if site in key: 794 | end_time = self.backend_status[key].get('end_time', None) 795 | start_time = self.backend_status[key].get('start_time', None) 796 | if start_time: 797 | if end_time: 798 | eta = 0 799 | else: 800 | current_time = time.time() 801 | eta = int(current_time - start_time) 802 | 803 | effective_time = 1 if time_ is None else time_ 804 | total_work_time = effective_time * int(image_count) 805 | 806 | eta = eta if time_ else 0 807 | self.load_balance_logger.info(f"{i18n('Extra time weight')} {eta}") 808 | 809 | backend_total_work_time[site] = total_work_time - eta if (total_work_time - eta) >= 0 else total_work_time 810 | 811 | total_time_dict = list(backend_total_work_time.values()) 812 | rev_dict = {} 813 | for key, value in backend_total_work_time.items(): 814 | if value in rev_dict: 815 | rev_dict[(value, key)] = value 816 | else: 817 | rev_dict[value] = key 818 | 819 | sorted_list = sorted(total_time_dict) 820 | fastest_backend = sorted_list[0] 821 | ava_url = rev_dict[fastest_backend] 822 | self.load_balance_logger.info(i18n('Backend: {0} is the fastest, has been selected').format(ava_url[:35])) 823 | 824 | ava_url_index = list(backend_url_dict.values()).index(ava_url) 825 | 826 | self.ava_backend_url = ava_url 827 | self.ava_backend_index = ava_url_index 828 | 829 | await self.exec_generate() 830 | # ava_url_tuple = (ava_url, reverse_dict[ava_url], all_resp, len(normal_backend), vram_dict[ava_url]) 831 | 832 | async def exec_generate(self): 833 | fifo = None 834 | try: 835 | self.set_backend_image(self.total_images, self.ava_backend_url) 836 | backend_class = self.class_dict[self.task_type][self.ava_backend_index] 837 | 838 | self.parameters_list[self.task_type][self.ava_backend_index]['payload'] = self.payload 839 | select_instance = backend_class(**self.parameters_list[self.task_type][self.ava_backend_index]) 840 | 841 | select_instance.init_backend_info() 842 | fifo = await select_instance.send_result_to_api() 843 | except: 844 | traceback.print_exc() 845 | finally: 846 | self.set_backend_image(-self.total_images, self.ava_backend_url) 847 | await self.set_backend_work_time(fifo.spend_time, self.ava_backend_url, fifo.total_img_count) 848 | self.result = fifo.result 849 | 850 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/comfyui.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import copy 3 | import json 4 | import random 5 | import time 6 | import traceback 7 | import uuid 8 | from pathlib import Path 9 | from tqdm import tqdm 10 | import os 11 | import base64 12 | import aiohttp 13 | 14 | from .base import Backend 15 | from ..utils import run_later 16 | 17 | global __ALL_SUPPORT_NODE__ 18 | MAX_SEED = 2 ** 32 19 | 20 | 21 | class AIDRAW(Backend): 22 | 23 | def __init__(self, **kwargs): 24 | super().__init__(**kwargs) 25 | # 需要更改 26 | self.logger = self.setup_logger('[Comfyui]') 27 | 28 | self.comfyui_api_json = None 29 | self.comfyui_api_json_reflex = None 30 | 31 | self.reflex_dict['sampler'] = { 32 | "DPM++ 2M": "dpmpp_2m", 33 | "DPM++ SDE": "dpmpp_sde", 34 | "DPM++ 2M SDE": "dpmpp_2m_sde", 35 | "DPM++ 2M SDE Heun": "dpmpp_2m_sde", 36 | "DPM++ 2S a": "dpmpp_2s_ancestral", 37 | "DPM++ 3M SDE": "dpmpp_3m_sde", 38 | "Euler a": "euler_ancestral", 39 | "Euler": "euler", 40 | "LMS": "lms", 41 | "Heun": "heun", 42 | "DPM2": "dpm_2", 43 | "DPM2 a": "dpm_2_ancestral", 44 | "DPM fast": "dpm_fast", 45 | "DPM adaptive": "dpm_adaptive", 46 | "Restart": "restart", 47 | "HeunPP2": "heunpp2", 48 | "IPNDM": "ipndm", 49 | "IPNDM_V": "ipndm_v", 50 | "DEIS": "deis", 51 | "DDIM": "ddim", 52 | "DDIM CFG++": "ddim", 53 | "PLMS": "plms", 54 | "UniPC": "uni_pc", 55 | "LCM": "lcm", 56 | "DDPM": "ddpm", 57 | # "[Forge] Flux Realistic": None, 58 | # "[Forge] Flux Realistic (Slow)": None, 59 | } 60 | self.reflex_dict['scheduler'] = { 61 | "Automatic": "normal", 62 | "Karras": "karras", 63 | "Exponential": "exponential", 64 | "SGM Uniform": "sgm_uniform", 65 | "Simple": "simple", 66 | "Normal": "normal", 67 | "DDIM": "ddim_uniform", 68 | "Beta": "beta" 69 | } 70 | 71 | self.reflex_dict['parameters'] = {} 72 | 73 | self.scheduler = self.reflex_dict['scheduler'].get(self.scheduler, "normal") 74 | self.sampler = self.reflex_dict['sampler'].get(self.sampler, "euler") 75 | 76 | async def heart_beat(self, id_): 77 | self.logger.info(f"{id_} 开始请求") 78 | 79 | async def get_images(): 80 | 81 | response = await self.http_request( 82 | method="GET", 83 | target_url=f"{self.backend_id}/history/{id_}", 84 | ) 85 | 86 | if response: 87 | for img in response[id_]['outputs'][str(self.comfyui_api_json_reflex.get('output', 9))]['images']: 88 | if img['subfolder'] == "": 89 | img_url = f"{self.backend_id}/view?filename={img['filename']}" 90 | else: 91 | img_url = f"{self.backend_id}/view?filename={img['filename']}&subfolder={img['subfolder']}" 92 | self.img_url.append(img_url) 93 | 94 | async with aiohttp.ClientSession() as session: 95 | ws_url = f'{self.backend_id}/ws?clientId={self.client_id}' 96 | async with session.ws_connect(ws_url) as ws: 97 | 98 | self.logger.info(f"WS连接成功: {ws_url}") 99 | progress_bar = None 100 | 101 | async for msg in ws: 102 | if msg.type == aiohttp.WSMsgType.TEXT: 103 | ws_msg = json.loads(msg.data) 104 | # 105 | # current_node = ws_msg['data']['node'] 106 | 107 | if ws_msg['type'] == 'progress': 108 | value = ws_msg['data']['value'] 109 | max_value = ws_msg['data']['max'] 110 | 111 | if progress_bar is None: 112 | progress_bar = await asyncio.to_thread( 113 | tqdm, total=max_value, 114 | desc=f"Prompt ID: {ws_msg['data']['prompt_id']}", 115 | unit="steps" 116 | ) 117 | 118 | delta = value - progress_bar.n 119 | await asyncio.to_thread(progress_bar.update, delta) 120 | 121 | if ws_msg['type'] == 'executing': 122 | if ws_msg['data']['node'] is None: 123 | self.logger.info(f"{id_}绘画完成!") 124 | await get_images() 125 | await ws.close() 126 | # 127 | # elif msg.type == aiohttp.WSMsgType.BINARY: 128 | # if current_node == 'save_image_websocket_node': 129 | # bytes_msg = msg.data 130 | # images_output = output_images.get(current_node, []) 131 | # images_output.append(bytes_msg[8:]) 132 | # output_images[current_node] = images_output 133 | 134 | elif msg.type == aiohttp.WSMsgType.ERROR: 135 | self.logger.error(f"Error: {msg.data}") 136 | await ws.close() 137 | break 138 | 139 | if progress_bar is not None: 140 | await asyncio.to_thread(progress_bar.close) 141 | 142 | async def update_progress(self): 143 | # 覆写函数 144 | pass 145 | 146 | async def get_backend_working_progress(self): 147 | 148 | try: 149 | response = await self.http_request( 150 | method="GET", 151 | target_url=f"{self.backend_id}/queue", 152 | ) 153 | if response.get("error", None): 154 | available = False 155 | else: 156 | available = True 157 | 158 | if len(response.get("queue_running", [])) == 0: 159 | progress = 0 160 | else: 161 | progress = 0.99 162 | 163 | build_resp = self.format_progress_api_resp(progress, self.start_time) 164 | 165 | sc = 200 if available is True else 500 166 | except: 167 | traceback.print_exc() 168 | finally: 169 | return build_resp, sc, self.backend_id, sc 170 | 171 | async def check_backend_usability(self): 172 | pass 173 | 174 | async def err_formating_to_sd_style(self): 175 | 176 | await self.download_img() 177 | self.format_api_respond() 178 | self.result = self.build_respond 179 | 180 | async def posting(self): 181 | 182 | self.logger.info(f"选择工作流{self.comfyui_api_json}") 183 | path_to_json = self.comfyui_api_json 184 | if self.comfyui_api_json: 185 | 186 | with open( 187 | Path(f"{os.path.dirname(os.path.abspath(__file__))}/../comfyui_workflows/{self.comfyui_api_json}.json").resolve(), 'r', encoding='utf-8') as f: 188 | self.comfyui_api_json = json.load(f) 189 | with open( 190 | Path(f"{os.path.dirname(os.path.abspath(__file__))}/../comfyui_workflows/{path_to_json}_reflex.json").resolve(), 'r', encoding='utf-8') as f: 191 | self.comfyui_api_json_reflex = json.load(f) 192 | 193 | upload_img_resp_list = [] 194 | 195 | if self.init_images: 196 | for image in self.init_images: 197 | resp = await self.upload_base64_image(image, uuid.uuid4().hex) 198 | upload_img_resp_list.append(resp) 199 | 200 | await self.update_api_json(upload_img_resp_list) 201 | 202 | input_ = { 203 | "client_id": self.client_id, 204 | "prompt": self.comfyui_api_json 205 | } 206 | 207 | respone = await self.http_request( 208 | method="POST", 209 | target_url=f"{self.backend_id}/prompt", 210 | headers=self.headers, 211 | content=json.dumps(input_) 212 | ) 213 | 214 | if respone.get("error", None): 215 | self.logger.error(respone) 216 | raise RuntimeError(respone["status_code"]) 217 | 218 | self.task_id = respone['prompt_id'] 219 | 220 | await self.heart_beat(self.task_id) 221 | await self.err_formating_to_sd_style() 222 | 223 | async def update_api_json(self, init_images): 224 | api_json = copy.deepcopy(self.comfyui_api_json) 225 | raw_api_json = copy.deepcopy(self.comfyui_api_json) 226 | 227 | update_mapping = { 228 | "sampler": { 229 | "seed": self.seed, 230 | "steps": self.steps, 231 | "cfg": self.scale, 232 | "sampler_name": self.sampler, 233 | "scheduler": self.scheduler, 234 | "denoise": self.denoising_strength 235 | }, 236 | "seed": { 237 | "seed": self.seed, 238 | "noise_seed": self.seed 239 | }, 240 | "image_size": { 241 | "width": self.width, 242 | "height": self.height, 243 | "batch_size": self.batch_size 244 | }, 245 | "prompt": { 246 | "text": self.prompt 247 | }, 248 | "negative_prompt": { 249 | "text": self.negative_prompt 250 | }, 251 | "checkpoint": { 252 | "ckpt_name": self.model_path if self.model_path else None 253 | }, 254 | "latentupscale": { 255 | "width": int(self.width*self.hr_scale) if not self.hr_resize_x else self.hr_resize_x, 256 | "height": int(self.height*self.hr_scale) if not self.hr_resize_y else self.hr_resize_y, 257 | }, 258 | "load_image": { 259 | "image": init_images[0]['name'] if self.init_images else None 260 | }, 261 | "resize": { 262 | "width": int(self.width*self.hr_scale) if not self.hr_resize_x else self.hr_resize_x, 263 | "height": int(self.height*self.hr_scale) if not self.hr_resize_y else self.hr_resize_y, 264 | }, 265 | "hr_steps": { 266 | "seed": self.seed, 267 | "steps": self.hr_second_pass_steps, 268 | "cfg": self.hr_scale, 269 | "sampler_name": self.sampler, 270 | "scheduler": self.scheduler, 271 | "denoise": self.denoising_strength, 272 | }, 273 | "hr_prompt": { 274 | "text": self.hr_prompt 275 | }, 276 | "hr_negative_prompt": { 277 | "text": self.hr_negative_prompt 278 | }, 279 | "tipo": { 280 | "width": self.width, 281 | "height": self.height, 282 | "seed": self.seed, 283 | "prompt": self.prompt, 284 | }, 285 | "append_prompt": { 286 | 287 | } 288 | } 289 | 290 | __OVERRIDE_SUPPORT_KEYS__ = { 291 | 'keep', 292 | 'value', 293 | 'append_prompt', 294 | 'append_negative_prompt', 295 | 'remove', 296 | "randint", 297 | "get_text", 298 | "upscale", 299 | 'image' 300 | 301 | } 302 | __ALL_SUPPORT_NODE__ = set(update_mapping.keys()) 303 | 304 | for item, node_id in self.comfyui_api_json_reflex.items(): 305 | 306 | if node_id and item not in ("override", "note"): 307 | 308 | org_node_id = node_id 309 | 310 | if isinstance(node_id, list): 311 | node_id = node_id 312 | elif isinstance(node_id, (int, str)): 313 | node_id = [node_id] 314 | elif isinstance(node_id, dict): 315 | node_id = list(node_id.keys()) 316 | 317 | for id_ in node_id: 318 | id_ = str(id_) 319 | update_dict = api_json.get(id_, None) 320 | if update_dict and item in update_mapping: 321 | api_json[id_]['inputs'].update(update_mapping[item]) 322 | 323 | if isinstance(org_node_id, dict): 324 | for node, override_dict in org_node_id.items(): 325 | single_node_or = override_dict.get("override", {}) 326 | 327 | if single_node_or: 328 | for key, override_action in single_node_or.items(): 329 | 330 | if override_action == "randint": 331 | api_json[node]['inputs'][key] = random.randint(0, MAX_SEED) 332 | 333 | elif override_action == "keep": 334 | org_cons = raw_api_json[node]['inputs'][key] 335 | 336 | elif override_action == "append_prompt": 337 | prompt = raw_api_json[node]['inputs'][key] 338 | prompt = self.prompt + prompt 339 | api_json[node]['inputs'][key] = prompt 340 | 341 | elif override_action == "append_negative_prompt": 342 | prompt = raw_api_json[node]['inputs'][key] 343 | prompt = self.negative_prompt + prompt 344 | api_json[node]['inputs'][key] = prompt 345 | 346 | elif "upscale" in override_action: 347 | scale = 1.5 348 | if "_" in override_action: 349 | scale = override_action.split("_")[1] 350 | 351 | if key == 'width': 352 | res = self.width 353 | elif key == 'height': 354 | res = self.height 355 | 356 | upscale_size = int(res * scale) 357 | api_json[node]['inputs'][key] = upscale_size 358 | 359 | elif "value" in override_action: 360 | override_value = raw_api_json[node]['inputs'][key] 361 | if "_" in override_action: 362 | override_value = override_action.split("_")[1] 363 | override_type = override_action.split("_")[2] 364 | if override_type == "int": 365 | override_value = int(override_value) 366 | elif override_type == "float": 367 | override_value = float(override_value) 368 | elif override_type == "str": 369 | override_value = str(override_value) 370 | 371 | api_json[node]['inputs'][key] = override_value 372 | 373 | elif "image" in override_action: 374 | image_id = int(override_action.split("_")[1]) 375 | api_json[node]['inputs'][key] = init_images[image_id]['name'] 376 | 377 | else: 378 | update_dict = api_json.get(node, None) 379 | if update_dict and item in update_mapping: 380 | api_json[node]['inputs'].update(update_mapping[item]) 381 | 382 | await run_later(self.compare_dicts(api_json, self.comfyui_api_json), 0.5) 383 | self.comfyui_api_json = api_json 384 | 385 | async def compare_dicts(self, dict1, dict2): 386 | 387 | modified_keys = {k for k in dict1.keys() & dict2.keys() if dict1[k] != dict2[k]} 388 | build_info = "节点映射情况: \n" 389 | for key in modified_keys: 390 | build_info += f"节点ID: {key} -> \n" 391 | for (key1, value1), (key2, value2) in zip(dict1[key].items(), dict2[key].items()): 392 | if value1 == value2: 393 | pass 394 | else: 395 | build_info += f"新的值: {key1} -> {value1}\n旧的值: {key2} -> {value2}\n" 396 | 397 | self.logger.info(build_info) 398 | 399 | async def upload_base64_image(self, b64_image, name, image_type="input", overwrite=False): 400 | 401 | if b64_image.startswith("data:image"): 402 | header, b64_image = b64_image.split(",", 1) 403 | file_type = header.split(";")[0].split(":")[1].split("/")[1] 404 | else: 405 | raise ValueError("Invalid base64 image format.") 406 | 407 | image_data = base64.b64decode(b64_image) 408 | 409 | data = aiohttp.FormData() 410 | data.add_field('image', image_data, filename=f"{name}.{file_type}", content_type=f'image/{file_type}') 411 | data.add_field('type', image_type) 412 | data.add_field('overwrite', str(overwrite).lower()) 413 | 414 | async with aiohttp.ClientSession() as session: 415 | async with session.post(f"{self.backend_id}/upload/image", data=data) as response: 416 | return json.loads(await response.read()) 417 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/liblibai.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import traceback 4 | 5 | from .base import Backend 6 | 7 | 8 | class AIDRAW(Backend): 9 | 10 | def __init__(self, **kwargs): 11 | super().__init__(**kwargs) 12 | self.logger = self.setup_logger('[LiblibAI]') 13 | 14 | async def heart_beat(self, id_): 15 | self.logger.info(f"{id_}开始请求") 16 | for i in range(60): 17 | 18 | response = await self.http_request( 19 | method="POST", 20 | target_url=f"https://liblib-api.vibrou.com/gateway/sd-api/generate/progress/msg/v3/{id_}", 21 | headers=self.headers, 22 | content=json.dumps({"flag": 0}), 23 | verify=False 24 | ) 25 | 26 | # 检查请求结果并处理 27 | if response.get('error') == "error": 28 | self.logger.warning(f"Failed to request: {response}") 29 | raise RuntimeError('服务器返回错误') 30 | if response['code'] != 0 or response['data']['statusMsg'] == '执行异常': 31 | raise RuntimeError('服务器返回错误') 32 | 33 | images = response['data']['images'] 34 | 35 | if images is None: 36 | self.logger.info(f"第{i+1}次心跳，未返回结果") 37 | await asyncio.sleep(5) 38 | continue 39 | else: 40 | # await self.set_backend_working_status(available=True) 41 | for i in images: 42 | if 'porn' in i['previewPath']: 43 | self.nsfw_detected = True 44 | self.logger.warning("API侧检测到NSFW图片") 45 | else: 46 | self.logger.img(f"图片url: {i['previewPath']}") 47 | self.img_url.append(i['previewPath']) 48 | self.comment = i['imageInfo'] 49 | break 50 | 51 | async def update_progress(self): 52 | # 覆写函数 53 | pass 54 | 55 | async def check_backend_usability(self): 56 | pass 57 | 58 | async def err_formating_to_sd_style(self): 59 | 60 | if self.nsfw_detected: 61 | await self.return_build_image() 62 | else: 63 | await self.download_img() 64 | 65 | self.format_api_respond() 66 | 67 | self.result = self.build_respond 68 | 69 | async def posting(self): 70 | 71 | if self.flux: 72 | input_ = { 73 | "checkpointId": 2295774, 74 | "generateType": 17, 75 | "frontCustomerReq": { 76 | "windowId": "", 77 | "tabType": "txt2img", 78 | "conAndSegAndGen": "gen" 79 | }, 80 | "adetailerEnable": 0, 81 | "text2imgV3": { 82 | "clipSkip": 2, 83 | "checkPointName": 2295774, 84 | "prompt": self.prompt, 85 | "negPrompt": self.negative_prompt, 86 | "seed": self.seed, 87 | "randnSource": 0, 88 | "samplingMethod": 31, 89 | "imgCount": self.batch_size, 90 | "samplingStep": self.steps, 91 | "cfgScale": self.scale, 92 | "width": self.width, 93 | "height": self.height 94 | }, 95 | "taskQueuePriority": 1 96 | } 97 | 98 | else: 99 | input_ = { 100 | "checkpointId": self.model_path, 101 | "generateType": 1, 102 | "frontCustomerReq": { 103 | # "frontId": "f46f8e35-5728-4ded-b163-832c3b85009d", 104 | "windowId": "", 105 | "tabType": "txt2img", 106 | "conAndSegAndGen": "gen" 107 | } 108 | , 109 | "adetailerEnable": 0, 110 | "text2img": { 111 | "prompt": self.prompt, 112 | "negativePrompt": self.negative_prompt, 113 | "extraNetwork": "", 114 | "samplingMethod": 0, 115 | "samplingStep": self.steps, 116 | "width": self.width, 117 | "height": self.height, 118 | "imgCount": self.batch_size, 119 | "cfgScale": self.scale, 120 | "seed": self.seed, 121 | "seedExtra": 0, 122 | "hiResFix": 0, 123 | "restoreFaces": 0, 124 | "tiling": 0, 125 | "clipSkip": 2, 126 | "randnSource": 0, 127 | "tileDiffusion": None 128 | } 129 | , 130 | "taskQueuePriority": 1 131 | } 132 | 133 | if self.enable_hr: 134 | 135 | hr_payload = { 136 | "hiresSteps": self.hr_second_pass_steps, 137 | "denoisingStrength": self.denoising_strength, 138 | "hiResFix": 1 if self.enable_hr else 0, 139 | "hiResFixInfo": { 140 | "upscaler": 6, 141 | "upscaleBy": self.hr_scale, 142 | "resizeWidth": int(self.width * self.hr_scale), 143 | "resizeHeight": int(self.height * self.hr_scale) 144 | } 145 | } 146 | 147 | input_['text2img'].update(hr_payload) 148 | 149 | new_headers = { 150 | "Accept": "application/json, text/plain, */*", 151 | "Token": self.backend_id 152 | } 153 | self.headers.update(new_headers) 154 | 155 | response = await self.http_request( 156 | method="POST", 157 | target_url="https://liblib-api.vibrou.com/gateway/sd-api/generate/image", 158 | headers=self.headers, 159 | content=json.dumps(input_), 160 | verify=False 161 | ) 162 | 163 | # 检查请求结果 164 | if response.get('error') == "error": 165 | self.logger.warning(f"Failed to request: {response}") 166 | else: 167 | task = response 168 | if task.get('msg') == 'Insufficient power': 169 | self.logger.warning('费用不足!') 170 | self.logger.info(f"API返回{task}") 171 | task_id = task['data'] 172 | await self.heart_beat(task_id) 173 | 174 | await self.err_formating_to_sd_style() 175 | 176 | 177 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/midjourney.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import aiohttp 4 | 5 | from .base import Backend 6 | from PIL import Image 7 | import asyncio 8 | import json 9 | import traceback 10 | import math 11 | import zipfile 12 | import io 13 | import os 14 | import aiofiles 15 | import base64 16 | 17 | from pathlib import Path 18 | 19 | class AIDRAW(Backend): 20 | 21 | def __init__(self, **kwargs): 22 | super().__init__(**kwargs) 23 | 24 | self.logger = self.setup_logger('[MidJourney]') 25 | 26 | async def heart_beat(self, id_): 27 | task_url = f"{self.backend_id}/mj/task/{id_}/fetch" 28 | 29 | while True: 30 | try: 31 | resp = await self.http_request("GET", task_url, format=True) 32 | status = resp.get('status') 33 | content = '' 34 | 35 | if status == "SUCCESS": 36 | content = resp['imageUrl'] 37 | self.img_url.append(resp['imageUrl']) 38 | self.logger.img(f"任务{id_}成功完成，图片URL：{resp['imageUrl']}") 39 | return content 40 | 41 | elif status == "FAILED": 42 | content = resp.get('failReason') or '未知原因' 43 | self.logger.error(f"任务处理失败，原因：{content}") 44 | 45 | raise Exception(f"任务处理失败，原因：{content}") 46 | 47 | elif status == "NOT_START": 48 | content = '任务未开始' 49 | 50 | elif status == "IN_PROGRESS": 51 | content = '任务正在运行' 52 | if resp.get('progress'): 53 | content += f"，进度：{resp['progress']}" 54 | 55 | elif status == "SUBMITTED": 56 | content = '任务已提交处理' 57 | 58 | elif status == "FAILURE": 59 | fail_reason = resp.get('failReason') or '未知原因' 60 | self.logger.error(f"任务处理失败，原因：{fail_reason}") 61 | if "Banned prompt detected" in fail_reason: 62 | await self.return_build_image("NSFW Prompt Detected") 63 | return 64 | else: 65 | raise Exception(f"任务处理失败，原因：{content}") 66 | 67 | else: 68 | content = status 69 | 70 | self.logger.info(f"任务{id_}状态：{content}") 71 | 72 | await asyncio.sleep(5) 73 | 74 | except Exception as e: 75 | self.logger.error(f"任务{id_}心跳监控出错: {str(e)}") 76 | raise 77 | 78 | async def update_progress(self): 79 | # 覆写函数 80 | pass 81 | 82 | async def get_shape(self): 83 | 84 | gcd = math.gcd(self.width, self.height) 85 | 86 | simplified_width = self.width // gcd 87 | simplified_height = self.height // gcd 88 | 89 | ar = f"{simplified_width}:{simplified_height}" 90 | 91 | return ar 92 | 93 | async def check_backend_usability(self): 94 | pass 95 | 96 | async def split_image(self): 97 | img = Image.open(io.BytesIO(self.img_btyes[0])) 98 | width, height = img.size 99 | 100 | half_width = width // 2 101 | half_height = height // 2 102 | 103 | coordinates = [(0, 0, half_width, half_height), 104 | (half_width, 0, width, half_height), 105 | (0, half_height, half_width, height), 106 | (half_width, half_height, width, height)] 107 | 108 | images = [img.crop(c) for c in coordinates] 109 | 110 | images_bytes = [io.BytesIO() for _ in range(4)] 111 | base64_images = [] 112 | 113 | for i in range(4): 114 | images[i].save(images_bytes[i], format='PNG') 115 | 116 | images_bytes[i].seek(0) 117 | base64_image = base64.b64encode(images_bytes[i].getvalue()).decode('utf-8') 118 | 119 | base64_images.append(base64_image) 120 | 121 | self.img_btyes += images_bytes 122 | self.img += base64_images 123 | 124 | # async def formating_to_sd_style(self): 125 | # 126 | # await self.download_img() 127 | # await self.split_image() 128 | # 129 | # self.format_api_respond() 130 | # self.result = self.build_respond 131 | 132 | async def posting(self): 133 | 134 | accept_ratio = await self.get_shape() 135 | 136 | ntags = f"--no {self.negative_prompt}" if self.negative_prompt else "" 137 | 138 | build_prompt = f"{self.prompt} --ar {accept_ratio} --seed {self.seed}" + ' ' + ntags + ' ' 139 | 140 | payload = { 141 | "prompt": build_prompt 142 | } 143 | 144 | if self.config.midjourney['auth_toekn'][self.count]: 145 | self.headers.update({"mj-api-secret": self.config.midjourney['auth_toekn'][self.count]}) 146 | 147 | resp = await self.http_request( 148 | "POST", 149 | f"{self.backend_url}/mj/submit/imagine", 150 | headers=self.headers, 151 | content=json.dumps(payload), 152 | format=True 153 | ) 154 | 155 | if resp.get('code') == 24: 156 | await self.return_build_image(text="NSFW Prompt Detected") 157 | 158 | elif resp.get('code') == 1: 159 | task_id = resp.get('result') 160 | self.task_id = task_id 161 | self.logger.info(f"任务提交成功，任务id: {task_id}") 162 | 163 | await self.heart_beat(task_id) 164 | await self.download_img() 165 | await self.split_image() 166 | 167 | self.format_api_respond() 168 | self.result = self.build_respond 169 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/novelai.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import aiohttp 4 | 5 | from .base import Backend 6 | import asyncio 7 | import json 8 | import traceback 9 | import zipfile 10 | import io 11 | import os 12 | import aiofiles 13 | import base64 14 | 15 | from pathlib import Path 16 | 17 | class AIDRAW(Backend): 18 | 19 | def __init__(self, **kwargs): 20 | super().__init__(**kwargs) 21 | 22 | self.logger = self.setup_logger('[NovelAI]') 23 | 24 | self.reflex_dict['sampler'] = { 25 | "DPM++ 2M": "k_dpmpp_2m", 26 | "DPM++ SDE": "k_dpmpp_sde", 27 | "DPM++ 2M SDE": "k_dpmpp_2m_sde", 28 | "DPM++ 2S a": "k_dpmpp_2s_ancestral", 29 | "Euler a": "k_euler_ancestral", 30 | "Euler": "k_euler", 31 | "DDIM": "ddim_v3" 32 | } 33 | 34 | async def update_progress(self): 35 | # 覆写函数 36 | pass 37 | 38 | async def get_shape(self): 39 | aspect_ratio = self.width / self.height 40 | 41 | resolutions = { 42 | "832x1216": (832, 1216), 43 | "1216x832": (1216, 832), 44 | "1024x1024": (1024, 1024), 45 | } 46 | 47 | closest_resolution = min(resolutions.keys(), 48 | key=lambda r: abs((resolutions[r][0] / resolutions[r][1]) - aspect_ratio)) 49 | 50 | self.width, self.height = resolutions[closest_resolution] 51 | 52 | return closest_resolution 53 | 54 | async def check_backend_usability(self): 55 | pass 56 | 57 | async def err_formating_to_sd_style(self): 58 | 59 | if self.nsfw_detected: 60 | await self.return_build_image() 61 | 62 | self.format_api_respond() 63 | 64 | self.result = self.build_respond 65 | 66 | async def posting(self): 67 | 68 | self.sampler = self.reflex_dict['sampler'].get(self.sampler, "k_euler_ancestral") 69 | 70 | header = { 71 | "authorization": "Bearer " + self.backend_id, 72 | ":authority": "https://api.novelai.net", 73 | ":path": "/ai/generate-image", 74 | "content-type": "application/json", 75 | "referer": "https://novelai.net", 76 | "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36", 77 | } 78 | 79 | post_api = "https://image.novelai.net/ai/generate-image" 80 | 81 | await self.get_shape() 82 | 83 | parameters = { 84 | "width": self.width, 85 | "height": self.height, 86 | "qualityToggle": False, 87 | "scale": self.scale, 88 | "sampler": self.sampler, 89 | "steps": self.steps, 90 | "seed": self.seed, 91 | "n_samples": 1, 92 | "ucPreset": 0, 93 | "negative_prompt": self.negative_prompt, 94 | } 95 | 96 | json_data = { 97 | "input": self.prompt, 98 | "model": self.model_path, 99 | "parameters": parameters 100 | } 101 | 102 | async def send_request(): 103 | 104 | async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=300)) as session: 105 | while True: 106 | async with session.post( 107 | post_api, 108 | headers=header, 109 | json=json_data, 110 | ssl=False, 111 | proxy=self.config.server_settings['proxy'] 112 | ) as response: 113 | 114 | if response.status == 429: 115 | resp_text = await response.json() 116 | if resp_text['message'] == 'Rate limited': 117 | raise Exception("触发频率限制") 118 | self.logger.warning(f"token繁忙中..., {resp_text}") 119 | wait_time = 5 120 | await asyncio.sleep(wait_time) 121 | else: 122 | response_data = await response.read() 123 | try: 124 | with zipfile.ZipFile(io.BytesIO(response_data)) as z: 125 | z.extractall(self.save_path) 126 | except: 127 | try: 128 | resp_text = await response.json() 129 | except: 130 | if resp_text['statusCode'] == 402: 131 | self.logger.warning(f"token余额不足, {resp_text}") 132 | return 133 | 134 | await send_request() 135 | 136 | await self.images_to_base64(self.save_path) 137 | await self.err_formating_to_sd_style() 138 | 139 | async def images_to_base64(self, save_path): 140 | 141 | for filename in os.listdir(save_path): 142 | if filename.endswith('.png'): 143 | file_path = os.path.join(save_path, filename) 144 | async with aiofiles.open(file_path, "rb") as image_file: 145 | image_data = await image_file.read() 146 | encoded_string = base64.b64encode(image_data).decode('utf-8') 147 | self.img.append(encoded_string) 148 | self.img_btyes.append(image_data) 149 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/seaart.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import traceback 4 | from ..locales import _ 5 | 6 | from .base import Backend 7 | 8 | 9 | class AIDRAW(Backend): 10 | 11 | def __init__(self, **kwargs): 12 | super().__init__(**kwargs) 13 | 14 | self.logger = self.setup_logger('[SeaArt]') 15 | 16 | async def heart_beat(self, id_): 17 | self.logger.info(f"{id_} 开始请求") 18 | self.logger.info(f"{_()}") 19 | data = json.dumps({"task_ids": [id_]}) 20 | for i in range(60): 21 | response = await self.http_request( 22 | method="POST", 23 | target_url="https://www.seaart.me/api/v1/task/batch-progress", 24 | headers=self.headers, 25 | content=data 26 | ) 27 | 28 | if isinstance(response, dict) and 'error' in response: 29 | raise RuntimeError(f"请求失败，错误信息: {response.get('details')}") 30 | else: 31 | items = response.get('data', {}).get('items', []) 32 | 33 | if not items: 34 | self.logger.info(f"第{i + 1}次心跳，未返回结果") 35 | await asyncio.sleep(5) 36 | continue 37 | 38 | for item in items: 39 | urls = item.get("img_uris") 40 | 41 | if urls is None: 42 | self.logger.info(f"第{i + 1}次心跳，未返回结果") 43 | await asyncio.sleep(5) 44 | continue 45 | 46 | elif isinstance(urls, list): 47 | for url in urls: 48 | self.logger.img(f"图片url: {url['url']}") 49 | self.img_url.append(url['url']) 50 | return 51 | 52 | raise RuntimeError(f"任务 {id_} 在60次心跳后仍未完成") 53 | 54 | 55 | async def update_progress(self): 56 | # 覆写函数 57 | pass 58 | 59 | async def check_backend_usability(self): 60 | pass 61 | 62 | async def err_formating_to_sd_style(self): 63 | 64 | await self.download_img() 65 | 66 | self.format_api_respond() 67 | 68 | self.result = self.build_respond 69 | 70 | async def posting(self): 71 | 72 | input_ = { 73 | "action": 1, 74 | "art_model_no": self.model_path or "1a486c58c2aa0601b57ddc263fc350d0", 75 | "category": 1, 76 | "speed_type": 1, 77 | "meta": 78 | { 79 | "prompt": self.prompt, 80 | "negative_prompt": self.negative_prompt, 81 | "restore_faces": self.restore_faces, 82 | "seed": self.seed, 83 | "sampler_name": self.sampler, 84 | "width": self.width, 85 | "height": self.height, 86 | "steps": self.steps, 87 | "cfg_scale": self.scale, 88 | "lora_models": [], 89 | "vae": "vae-ft-mse-840000-ema-pruned", 90 | "clip_skip": 1, 91 | "hr_second_pass_steps": 20, 92 | "lcm_mode": 0, 93 | "n_iter": 1, 94 | "embeddings": [] 95 | } 96 | } 97 | 98 | if self.enable_hr: 99 | 100 | hr_payload = { 101 | "hr_second_pass_steps": self.hr_second_pass_steps, 102 | "enable_hr": True, 103 | "hr_upscaler": "4x-UltraSharp", 104 | "hr_scale": self.hr_scale, 105 | } 106 | 107 | input_['meta'].update(hr_payload) 108 | 109 | new_headers = { 110 | "Accept": "application/json, text/plain, */*", 111 | "Token": self.backend_id 112 | } 113 | 114 | self.headers.update(new_headers) 115 | 116 | data = json.dumps(input_) 117 | response = await self.http_request( 118 | method="POST", 119 | target_url="https://www.seaart.me/api/v1/task/create", 120 | headers=self.headers, 121 | content=data 122 | ) 123 | 124 | if isinstance(response, dict) and 'error' in response: 125 | self.logger.warning(f"{response.get('details')}") 126 | else: 127 | task = response 128 | task_id = task.get('data', {}).get('id') 129 | 130 | if task_id: 131 | await self.heart_beat(task_id) 132 | 133 | await self.err_formating_to_sd_style() 134 | 135 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/tusiart.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import traceback 4 | 5 | from .base import Backend 6 | 7 | 8 | class AIDRAW(Backend): 9 | 10 | def __init__(self, **kwargs): 11 | super().__init__(**kwargs) 12 | 13 | self.logger = self.setup_logger('[TusiArt]') 14 | 15 | async def heart_beat(self, id_): 16 | self.logger.info(f"{id_}开始请求") 17 | self.headers['referer'] = "https://tusiart.com/models" 18 | del self.headers['sec-ch-ua'] 19 | 20 | for i in range(60): 21 | await asyncio.sleep(5) 22 | self.logger.info(f"第{i + 1}次心跳") 23 | response = await self.http_request( 24 | method="GET", 25 | target_url='https://api.tusiart.cn/works/v1/works/tasks?size=20&cursor=0&returnAllTask=true', 26 | headers=self.headers 27 | ) 28 | 29 | if isinstance(response, dict) and 'error' in response: 30 | raise RuntimeError(f"Request failed with error: {response.get('details')}") 31 | else: 32 | resp_json = response 33 | all_tasks = resp_json['data']['tasks'] 34 | task_found = False 35 | for task in all_tasks: 36 | if task['taskId'] == id_: 37 | task_found = True 38 | if task['status'] == 'WAITING': 39 | break 40 | elif task['status'] == 'FINISH': 41 | matched = False 42 | for img in task['items']: 43 | if 'workspace.tusiassets.com' in img['url']: 44 | self.logger.img(f"图片url: {img['url']}") 45 | self.img_url.append(img['url']) 46 | matched = True 47 | 48 | if matched: 49 | return 50 | else: 51 | self.logger.info(f"第{i + 1}次心跳，FINISH状态下未找到符合条件的URL") 52 | await asyncio.sleep(5) 53 | break 54 | if not task_found: 55 | self.logger.info(f"任务 {id_} 未找到") 56 | await asyncio.sleep(5) 57 | continue 58 | 59 | raise RuntimeError(f"任务 {id_} 在 {60} 次轮询后仍未完成") 60 | 61 | async def update_progress(self): 62 | # 覆写函数 63 | pass 64 | 65 | async def check_backend_usability(self): 66 | pass 67 | 68 | async def err_formating_to_sd_style(self): 69 | 70 | await self.download_img() 71 | 72 | self.format_api_respond() 73 | 74 | self.result = self.build_respond 75 | 76 | async def posting(self): 77 | 78 | self.sampler = "Euler a" 79 | 80 | input_ = { 81 | "params": 82 | { 83 | "baseModel": 84 | { 85 | "modelId": self.model_path or "758751795863586176", 86 | "modelFileId": "708770380970509676" 87 | }, 88 | "sdxl": 89 | {"refiner": False}, 90 | "models": [], 91 | "embeddingModels": [], 92 | "sdVae": "Automatic", 93 | "prompt": self.prompt, 94 | "negativePrompt": self.negative_prompt, 95 | "height": self.height, 96 | "width": self.width, 97 | "imageCount": self.total_img_count, 98 | "steps": self.steps, 99 | "images": [], 100 | "cfgScale": self.scale, 101 | "seed": str(self.seed), 102 | "clipSkip": 2, 103 | "etaNoiseSeedDelta": 31337, 104 | "v1Clip": False, 105 | "samplerName": self.sampler 106 | }, 107 | "taskType": "TXT2IMG", 108 | "isRemix": False, 109 | "captchaType": "CLOUDFLARE_TURNSTILE" 110 | } 111 | 112 | if self.enable_hr: 113 | 114 | hr_payload = { 115 | "enableHr": True, 116 | "hrUpscaler": "R-ESRGAN 4x+ Anime6B", 117 | "hrSecondPassSteps": self.hr_second_pass_steps, 118 | "denoisingStrength": self.denoising_strength, 119 | "hrResizeX": int(self.width*self.hr_scale), 120 | "hrResizeY": int(self.height*self.hr_scale) 121 | } 122 | 123 | input_['params'].update(hr_payload) 124 | 125 | new_headers = { 126 | "Authorization": f"Bearer {self.backend_id}", 127 | "Token": self.backend_id, 128 | "referer": self.config.backends[self.backend_type]['referer'][self.count], 129 | "sec-ch-ua": 'Not)A;Brand";v="99", "Microsoft Edge";v="127", "Chromium";v="127' 130 | } 131 | self.headers.update(new_headers) 132 | 133 | data = json.dumps(input_) 134 | 135 | response = await self.http_request( 136 | method="POST", 137 | target_url="https://api.tusiart.cn/works/v1/works/task", 138 | headers=self.headers, 139 | content=data 140 | ) 141 | 142 | if isinstance(response, dict) and 'error' in response: 143 | pass 144 | else: 145 | task = response 146 | if task['code'] == '1300100': 147 | error_text = f""" 148 | 后端：{self.config.tusiart_setting['note'][self.count]} 遇到人机验证，需到验证。 149 | 请前往https://tusiart.com/使用一次生图来触发验证码。 150 | 后端已被标记为不可使用,如需继续使用请重启API" 151 | """ 152 | self.logger.warning("遇到人机验证！") 153 | raise RuntimeError(error_text) 154 | task_id = task['data']['task']['taskId'] 155 | await self.heart_beat(task_id) 156 | 157 | await self.err_formating_to_sd_style() 158 | 159 | 160 | -------------------------------------------------------------------------------- /DrawBridgeAPI/backend/yunjie.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import traceback 4 | 5 | from .base import Backend 6 | 7 | 8 | class AIDRAW(Backend): 9 | 10 | def __init__(self, **kwargs): 11 | super().__init__(**kwargs) 12 | 13 | self.logger = self.setup_logger('[YunJie]') 14 | 15 | async def heart_beat(self, id_): 16 | self.logger.info(f"{id_} 开始请求") 17 | for i in range(60): 18 | await asyncio.sleep(5) 19 | 20 | data = json.dumps({"taskId": id_}) 21 | response = await self.http_request( 22 | method="POST", 23 | target_url="https://www.yunjie.art/rayvision/aigc/customer/task/progress", 24 | headers=self.headers, 25 | content=data 26 | ) 27 | 28 | if isinstance(response, dict) and 'error' in response: 29 | raise RuntimeError(f"请求失败，错误信息: {response.get('details')}") 30 | else: 31 | resp_json = response 32 | if resp_json['code'] == "Account.Token.Expired": 33 | error_text = f""" 34 | 后端：{self.config.yunjie_setting['note'][self.count]} token过期。 35 | 请前往https://www.yunjie.art/ 登录重新获取token 36 | """ 37 | self.logger.warning("token过期") 38 | raise RuntimeError(error_text) 39 | items = resp_json.get('data', {}).get('data', []) 40 | self.logger.info(f"第{i + 1}次心跳，未返回结果") 41 | 42 | if not items: 43 | continue 44 | 45 | for item in items: 46 | url = item.get("url") 47 | 48 | if url: 49 | self.logger.img(f"图片url: {url}") 50 | self.img_url.append(url) 51 | return 52 | 53 | raise RuntimeError(f"任务 {id_} 在60次心跳后仍未完成") 54 | 55 | async def update_progress(self): 56 | # 覆写函数 57 | pass 58 | 59 | async def check_backend_usability(self): 60 | pass 61 | 62 | async def err_formating_to_sd_style(self): 63 | 64 | await self.download_img() 65 | 66 | self.format_api_respond() 67 | 68 | self.result = self.build_respond 69 | 70 | async def posting(self): 71 | 72 | input_ = { 73 | "genModel": "advance", 74 | "initImage": "", 75 | "modelUuid": self.model_path or "MGC-17d172ee37c1b000", 76 | "samplingMethod": 77 | self.sampler, 78 | "cfgScale": self.scale, 79 | "samplingSteps": self.steps, 80 | "plugins": [], 81 | "clipSkip": 2, 82 | "etaNoiseSeedDelta": 31337, 83 | "prompt": self.prompt, 84 | "negativePrompt": self.negative_prompt, 85 | "resolutionX": self.width, 86 | "resolutionY": self.height, 87 | "genCount": self.total_img_count, 88 | "seed": self.seed, 89 | "prompt": [] 90 | } 91 | 92 | if self.enable_hr: 93 | 94 | hr_payload = { 95 | "hires": 96 | {"hrSecondPassSteps": self.hr_second_pass_steps, 97 | "denoisingStrength": self.denoising_strength, 98 | "hrScale": self.hr_scale, 99 | "hrUpscaler": "R-ESRGAN 4x+" 100 | } 101 | } 102 | 103 | input_.update(hr_payload) 104 | 105 | new_headers = { 106 | "Token": self.backend_id 107 | } 108 | self.headers.update(new_headers) 109 | data = json.dumps(input_) 110 | 111 | response = await self.http_request( 112 | method="POST", 113 | target_url="https://www.yunjie.art/rayvision/aigc/customer/task/imageGen", 114 | headers=self.headers, 115 | content=data 116 | ) 117 | 118 | if response.get("error", None): 119 | self.logger.error(f"请求失败，错误信息: {response.get('details')}") 120 | else: 121 | task = response 122 | task_id = task['data']['taskId'] 123 | await self.heart_beat(task_id) 124 | await self.err_formating_to_sd_style() 125 | 126 | -------------------------------------------------------------------------------- /DrawBridgeAPI/base_config.py: -------------------------------------------------------------------------------- 1 | import yaml as yaml_ 2 | import shutil 3 | import redis 4 | import json 5 | import logging 6 | import os 7 | 8 | from pydantic import BaseModel 9 | from typing import Dict, List 10 | from pathlib import Path 11 | 12 | from .locales import _ 13 | 14 | 15 | redis_client = None 16 | 17 | api_current_dir = os.path.dirname(os.path.abspath(__file__)) 18 | 19 | 20 | class CustomFormatter(logging.Formatter): 21 | def __init__(self, fmt=None, datefmt=None, style='%', prefix="[MAIN]"): 22 | super().__init__(fmt, datefmt, style) 23 | self.prefix = prefix 24 | 25 | def format(self, record): 26 | original_msg = record.msg 27 | record.msg = f"{self.prefix} {original_msg}" 28 | formatted_msg = super().format(record) 29 | record.msg = original_msg # 恢复原始消息 30 | return formatted_msg 31 | 32 | 33 | # 字典用于跟踪已创建的日志记录器 34 | 35 | empty_dict = {"token": None} 36 | 37 | import logging 38 | 39 | 40 | class CustomFormatter(logging.Formatter): 41 | """Custom formatter to add a fixed color for the prefix and variable colors for the log levels.""" 42 | 43 | def __init__(self, prefix="", img_prefix="", *args, **kwargs): 44 | super().__init__(*args, **kwargs) 45 | self.prefix = f"\033[94m{prefix}\033[0m" # 固定蓝色前缀 46 | self.img_prefix = f"\033[93m{img_prefix}\033[0m" # 固定黄色前缀 47 | self.FORMATS = { 48 | logging.DEBUG: f"{self.prefix} \033[94m[DEBUG]\033[0m %(message)s", 49 | logging.INFO: f"{self.prefix} \033[92m[INFO]\033[0m %(message)s", 50 | logging.WARNING: f"{self.prefix} \033[93m[WARNING]\033[0m %(message)s", 51 | logging.ERROR: f"{self.prefix} \033[91m[ERROR]\033[0m %(message)s", 52 | logging.CRITICAL: f"{self.prefix} \033[95m[CRITICAL]\033[0m %(message)s", 53 | "IMG": f"{self.img_prefix} \033[93m[IMG]\033[0m %(message)s" # 黄色前缀的 IMG 日志 54 | } 55 | 56 | def format(self, record): 57 | log_fmt = self.FORMATS.get(record.levelno, self.FORMATS.get("IMG")) 58 | formatter = logging.Formatter(log_fmt) 59 | return formatter.format(record) 60 | 61 | 62 | class CustomLogger(logging.Logger): 63 | """Custom logger class to add an img method.""" 64 | 65 | def __init__(self, name, level=logging.DEBUG): 66 | super().__init__(name, level) 67 | self.img_level = 25 # 自定义日志等级 68 | logging.addLevelName(self.img_level, "IMG") 69 | 70 | def img(self, msg, *args, **kwargs): 71 | if self.isEnabledFor(self.img_level): 72 | self._log(self.img_level, msg, args, **kwargs) 73 | 74 | 75 | loggers = {} 76 | 77 | 78 | def setup_logger(custom_prefix="[MAIN]"): 79 | # 检查是否已经存在具有相同前缀的 logger 80 | if custom_prefix in loggers: 81 | return loggers[custom_prefix] 82 | 83 | # 使用自定义的 Logger 类 84 | logger = CustomLogger(custom_prefix) 85 | logger.setLevel(logging.DEBUG) 86 | 87 | # 创建一个控制台处理器并设置日志级别为DEBUG 88 | console_handler = logging.StreamHandler() 89 | console_handler.setLevel(logging.DEBUG) 90 | 91 | # 创建一个文件处理器来保存所有日志到 log.txt 92 | file_handler = logging.FileHandler('log.log') 93 | file_handler.setLevel(logging.DEBUG) 94 | 95 | # 创建一个错误文件处理器来保存错误日志到 log_error.txt 96 | error_file_handler = logging.FileHandler('log_error.log') 97 | error_file_handler.setLevel(logging.ERROR) 98 | 99 | # 创建一个文件处理器来保存IMG日志到 log_img.log 100 | img_file_handler = logging.FileHandler('log_img.log') 101 | img_file_handler.setLevel(logger.img_level) 102 | 103 | # 创建格式器并将其添加到处理器 104 | formatter = CustomFormatter(prefix=custom_prefix, img_prefix=custom_prefix) 105 | console_handler.setFormatter(formatter) 106 | file_handler.setFormatter(formatter) 107 | error_file_handler.setFormatter(formatter) 108 | img_file_handler.setFormatter(formatter) 109 | 110 | # 将处理器添加到日志记录器 111 | logger.addHandler(console_handler) 112 | logger.addHandler(file_handler) 113 | logger.addHandler(error_file_handler) 114 | logger.addHandler(img_file_handler) 115 | 116 | # 将创建的 logger 存储在字典中 117 | loggers[custom_prefix] = logger 118 | 119 | return logger 120 | 121 | 122 | class Config(BaseModel): 123 | 124 | backend_name_list: list = [] 125 | 126 | server_settings: dict = {} 127 | enable_backends: Dict[str, Dict[str, List[int | Dict]]] = {} 128 | backends: dict = {} 129 | 130 | retry_times: int = 3 131 | proxy: str = '' 132 | 133 | workload_dict: dict = {} 134 | 135 | base_workload_dict: dict = { 136 | "start_time": None, 137 | "end_time": None, 138 | "idle": True, 139 | "available": True, 140 | "fault": False 141 | } 142 | 143 | models_list: list = [] 144 | 145 | name_url: dict = {} 146 | 147 | 148 | def package_import(copy_to_config_path): 149 | current_dir = os.path.dirname(os.path.abspath(__file__)) 150 | source_template = Path(os.path.join(current_dir, "config_example.yaml")).resolve() 151 | shutil.copy(source_template, copy_to_config_path) 152 | 153 | 154 | class ConfigInit: 155 | 156 | def __init__(self): 157 | self.config = None 158 | self.config_file_path = None 159 | self.logger = setup_logger(custom_prefix="[INIT]") 160 | self.redis_client = None 161 | 162 | def load_config(self): 163 | 164 | with open(self.config_file_path, "r", encoding="utf-8") as f: 165 | yaml_config = yaml_.load(f, Loader=yaml_.FullLoader) 166 | config = Config(**yaml_config) 167 | self.logger.info(_('Loading config file completed')) 168 | 169 | return config 170 | 171 | def init(self, config_file_path): 172 | 173 | self.config_file_path = config_file_path 174 | config = self.load_config() 175 | 176 | welcome_txt = ''' 177 | 欢迎使用 178 | _____ ____ _ _ _____ _____ 179 | | __ \ | _ \ (_) | | /\ | __ \ |_ _| 180 | | | | | _ __ __ _ __ __ | |_) | _ __ _ __| | __ _ ___ / \ | |__) | | | 181 | | | | | | '__| / _` | \ \ /\ / / | _ < | '__| | | / _` | / _` | / _ \ / /\ \ | ___/ | | 182 | | |__| | | | | (_| | \ V V / | |_) | | | | | | (_| | | (_| | | __/ / ____ \ | | _| |_ 183 | |_____/ |_| \__,_| \_/\_/ |____/ |_| |_| \__,_| \__, | \___| /_/ \_\ |_| |_____| 184 | __/ | 185 | |___/ 186 | 关注雕雕, 关注雕雕喵 187 | 项目地址/Project Re: https://github.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI 188 | ''' 189 | 190 | print(welcome_txt) 191 | 192 | for backend_type, api in config.backends.items(): 193 | if api: 194 | for name in api['name']: 195 | key = f"{backend_type}-{name}" 196 | config.workload_dict[key] = config.base_workload_dict 197 | 198 | models_dict = {} 199 | models_dict['is_loaded'] = False 200 | for back_name in list(config.workload_dict.keys()): 201 | models_dict[back_name] = config.models_list 202 | 203 | try: 204 | db_index = config.server_settings['redis_server'][3] 205 | except IndexError: 206 | db_index = 15 207 | 208 | self.redis_client = redis.Redis( 209 | host=config.server_settings['redis_server'][0], 210 | port=config.server_settings['redis_server'][1], 211 | password=config.server_settings['redis_server'][2], 212 | db=db_index 213 | ) 214 | 215 | self.logger.info(_('Redis connection successful')) 216 | 217 | workload_json = json.dumps(config.workload_dict) 218 | 219 | rp = self.redis_client.pipeline() 220 | rp.set('workload', workload_json) 221 | rp.set('models', json.dumps(models_dict)) 222 | rp.set('styles', json.dumps([])) 223 | rp.execute() 224 | 225 | self.config = config 226 | 227 | 228 | init_instance = ConfigInit() 229 | -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/diaopony-hr.json: -------------------------------------------------------------------------------- 1 | { 2 | "4": { 3 | "inputs": { 4 | "ckpt_name": "models\\DiaoDaiaPony - 100 Artists - testing.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Load Checkpoint" 9 | } 10 | }, 11 | "7": { 12 | "inputs": { 13 | "text": "score_3,poorly drawn,bad anatomy,bad proportions, watercolor painting, brush strokes,3d,2.5d,signature,watermark,bad face,distorted face,messed up eyes,deformed,(low quality, bad quality, worst quality:1.2),bad hand", 14 | "clip": [ 15 | "4", 16 | 1 17 | ] 18 | }, 19 | "class_type": "CLIPTextEncode", 20 | "_meta": { 21 | "title": "CLIP Text Encode (Negative Prompt)" 22 | } 23 | }, 24 | "53": { 25 | "inputs": { 26 | "width": 768, 27 | "height": 1152, 28 | "batch_size": 1 29 | }, 30 | "class_type": "EmptyLatentImage", 31 | "_meta": { 32 | "title": "Empty Latent Image" 33 | } 34 | }, 35 | "79": { 36 | "inputs": { 37 | "seed": 657283391776279, 38 | "steps": 30, 39 | "cfg": 8, 40 | "sampler_name": "euler", 41 | "scheduler": "karras", 42 | "denoise": 1, 43 | "model": [ 44 | "4", 45 | 0 46 | ], 47 | "positive": [ 48 | "103", 49 | 0 50 | ], 51 | "negative": [ 52 | "7", 53 | 0 54 | ], 55 | "latent_image": [ 56 | "53", 57 | 0 58 | ] 59 | }, 60 | "class_type": "KSampler", 61 | "_meta": { 62 | "title": "KSampler" 63 | } 64 | }, 65 | "88": { 66 | "inputs": { 67 | "filename_prefix": "ComfyUI", 68 | "images": [ 69 | "91", 70 | 0 71 | ] 72 | }, 73 | "class_type": "SaveImage", 74 | "_meta": { 75 | "title": "Save Image" 76 | } 77 | }, 78 | "91": { 79 | "inputs": { 80 | "upscale_by": 2, 81 | "seed": 291655160144038, 82 | "steps": 12, 83 | "cfg": 8, 84 | "sampler_name": "dpmpp_2m", 85 | "scheduler": "karras", 86 | "denoise": 0.2, 87 | "mode_type": "Linear", 88 | "tile_width": 1024, 89 | "tile_height": 1024, 90 | "mask_blur": 8, 91 | "tile_padding": 32, 92 | "seam_fix_mode": "None", 93 | "seam_fix_denoise": 1, 94 | "seam_fix_width": 64, 95 | "seam_fix_mask_blur": 8, 96 | "seam_fix_padding": 16, 97 | "force_uniform_tiles": true, 98 | "tiled_decode": false, 99 | "image": [ 100 | "92", 101 | 0 102 | ], 103 | "model": [ 104 | "4", 105 | 0 106 | ], 107 | "positive": [ 108 | "103", 109 | 0 110 | ], 111 | "negative": [ 112 | "7", 113 | 0 114 | ], 115 | "vae": [ 116 | "4", 117 | 2 118 | ], 119 | "upscale_model": [ 120 | "93", 121 | 0 122 | ] 123 | }, 124 | "class_type": "UltimateSDUpscale", 125 | "_meta": { 126 | "title": "Ultimate SD Upscale" 127 | } 128 | }, 129 | "92": { 130 | "inputs": { 131 | "samples": [ 132 | "99", 133 | 0 134 | ], 135 | "vae": [ 136 | "4", 137 | 2 138 | ] 139 | }, 140 | "class_type": "VAEDecode", 141 | "_meta": { 142 | "title": "VAE Decode" 143 | } 144 | }, 145 | "93": { 146 | "inputs": { 147 | "model_name": "4x-UltraSharp.pth" 148 | }, 149 | "class_type": "UpscaleModelLoader", 150 | "_meta": { 151 | "title": "Load Upscale Model" 152 | } 153 | }, 154 | "98": { 155 | "inputs": { 156 | "upscale_method": "nearest-exact", 157 | "width": 1152, 158 | "height": 1536, 159 | "crop": "disabled", 160 | "samples": [ 161 | "79", 162 | 0 163 | ] 164 | }, 165 | "class_type": "LatentUpscale", 166 | "_meta": { 167 | "title": "Upscale Latent" 168 | } 169 | }, 170 | "99": { 171 | "inputs": { 172 | "seed": 641400482051274, 173 | "steps": 20, 174 | "cfg": 8, 175 | "sampler_name": "euler", 176 | "scheduler": "normal", 177 | "denoise": 1, 178 | "model": [ 179 | "4", 180 | 0 181 | ], 182 | "positive": [ 183 | "103", 184 | 0 185 | ], 186 | "negative": [ 187 | "7", 188 | 0 189 | ], 190 | "latent_image": [ 191 | "98", 192 | 0 193 | ] 194 | }, 195 | "class_type": "KSampler", 196 | "_meta": { 197 | "title": "KSampler" 198 | } 199 | }, 200 | "103": { 201 | "inputs": { 202 | "text": ",(qianqianjie:1.1),(shinyo yukino:1),roku 6,(miyu (miy u1308):1.1),momoko (momopoco), score_9,score_8_up,score_7_up,score_anime,amazing quality,very aesthetic,absurdres,", 203 | "clip": [ 204 | "4", 205 | 1 206 | ] 207 | }, 208 | "class_type": "CLIPTextEncode", 209 | "_meta": { 210 | "title": "CLIP Text Encode (Prompt)" 211 | } 212 | } 213 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/diaopony-hr_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "prompt": {"103": {"override": {"text": "append_prompt"}}}, 3 | "negative_prompt": {"7": {"override": {"text": "append_negative_prompt"}}}, 4 | "sampler": ["79", "99"], 5 | "image_size": {"53": {}, "98": {"override": {"width": "upscale", "height": "upscale"}}}, 6 | "output": 88 7 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/diaopony-tipo.json: -------------------------------------------------------------------------------- 1 | { 2 | "4": { 3 | "inputs": { 4 | "ckpt_name": "models\\DiaoDaiaPony - 100 Artists - testing.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Load Checkpoint" 9 | } 10 | }, 11 | "6": { 12 | "inputs": { 13 | "text": [ 14 | "50", 15 | 0 16 | ], 17 | "clip": [ 18 | "4", 19 | 1 20 | ] 21 | }, 22 | "class_type": "CLIPTextEncode", 23 | "_meta": { 24 | "title": "CLIP Text Encode (TIPO Prompt)" 25 | } 26 | }, 27 | "7": { 28 | "inputs": { 29 | "text": "score_3,poorly drawn,bad anatomy,bad proportions, watercolor painting, brush strokes,3d,2.5d,signature,watermark,bad face,distorted face,messed up eyes,deformed,(low quality, bad quality, worst quality:1.2),bad hand", 30 | "clip": [ 31 | "4", 32 | 1 33 | ] 34 | }, 35 | "class_type": "CLIPTextEncode", 36 | "_meta": { 37 | "title": "CLIP Text Encode (Negative Prompt)" 38 | } 39 | }, 40 | "8": { 41 | "inputs": { 42 | "samples": [ 43 | "52", 44 | 0 45 | ], 46 | "vae": [ 47 | "4", 48 | 2 49 | ] 50 | }, 51 | "class_type": "VAEDecode", 52 | "_meta": { 53 | "title": "VAE Decode" 54 | } 55 | }, 56 | "50": { 57 | "inputs": { 58 | "tags": "\n\nscore_9,score_8_up,score_7_up,score_anime,amazing quality,very aesthetic,absurdres", 59 | "nl_prompt": "An illustration of", 60 | "ban_tags": "text, censor, speech, say, illustrations, doll", 61 | "tipo_model": "KBlueLeaf/TIPO-500M", 62 | "format": "<|special|>, \n<|characters|>, <|copyrights|>, \n<|artist|>, \n\n<|general|>,\n\n<|extended|>.\n\n<|quality|>, <|meta|>, <|rating|>", 63 | "width": 1024, 64 | "height": 1024, 65 | "temperature": 0.5, 66 | "top_p": 0.95, 67 | "min_p": 0.05, 68 | "top_k": 80, 69 | "tag_length": "long", 70 | "nl_length": "long", 71 | "seed": 1763 72 | }, 73 | "class_type": "TIPO", 74 | "_meta": { 75 | "title": "TIPO" 76 | } 77 | }, 78 | "52": { 79 | "inputs": { 80 | "seed": 11451, 81 | "steps": 20, 82 | "cfg": 8, 83 | "sampler_name": "euler", 84 | "scheduler": "normal", 85 | "denoise": 1, 86 | "model": [ 87 | "4", 88 | 0 89 | ], 90 | "positive": [ 91 | "6", 92 | 0 93 | ], 94 | "negative": [ 95 | "7", 96 | 0 97 | ], 98 | "latent_image": [ 99 | "53", 100 | 0 101 | ] 102 | }, 103 | "class_type": "KSampler", 104 | "_meta": { 105 | "title": "KSampler" 106 | } 107 | }, 108 | "53": { 109 | "inputs": { 110 | "width": 1152, 111 | "height": 1536, 112 | "batch_size": 1 113 | }, 114 | "class_type": "EmptyLatentImage", 115 | "_meta": { 116 | "title": "Empty Latent Image" 117 | } 118 | }, 119 | "72": { 120 | "inputs": { 121 | "filename_prefix": "ComfyUI", 122 | "images": [ 123 | "8", 124 | 0 125 | ] 126 | }, 127 | "class_type": "SaveImage", 128 | "_meta": { 129 | "title": "Save Image" 130 | } 131 | } 132 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/diaopony-tipo_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "tipo": {"50": {"override": {"tags": "append_prompt"}}}, 3 | "sampler": 52, 4 | "image_size": 53, 5 | "output": 72 6 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux-dev.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "models\\flux1-dev-bnb-nf4-v2.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderNF4", 7 | "_meta": { 8 | "title": "CheckpointLoaderNF4" 9 | } 10 | }, 11 | "2": { 12 | "inputs": { 13 | "text": "a tank", 14 | "clip": [ 15 | "1", 16 | 1 17 | ] 18 | }, 19 | "class_type": "CLIPTextEncode", 20 | "_meta": { 21 | "title": "CLIP Text Encode (Prompt)" 22 | } 23 | }, 24 | "3": { 25 | "inputs": { 26 | "seed": 861133332082627, 27 | "steps": 20, 28 | "cfg": 1, 29 | "sampler_name": "euler", 30 | "scheduler": "simple", 31 | "denoise": 1, 32 | "model": [ 33 | "1", 34 | 0 35 | ], 36 | "positive": [ 37 | "2", 38 | 0 39 | ], 40 | "negative": [ 41 | "2", 42 | 0 43 | ], 44 | "latent_image": [ 45 | "4", 46 | 0 47 | ] 48 | }, 49 | "class_type": "KSampler", 50 | "_meta": { 51 | "title": "KSampler" 52 | } 53 | }, 54 | "4": { 55 | "inputs": { 56 | "width": 512, 57 | "height": 768, 58 | "batch_size": 1 59 | }, 60 | "class_type": "EmptyLatentImage", 61 | "_meta": { 62 | "title": "Empty Latent Image" 63 | } 64 | }, 65 | "5": { 66 | "inputs": { 67 | "samples": [ 68 | "3", 69 | 0 70 | ], 71 | "vae": [ 72 | "1", 73 | 2 74 | ] 75 | }, 76 | "class_type": "VAEDecode", 77 | "_meta": { 78 | "title": "VAE Decode" 79 | } 80 | }, 81 | "6": { 82 | "inputs": { 83 | "filename_prefix": "ComfyUI", 84 | "images": [ 85 | "5", 86 | 0 87 | ] 88 | }, 89 | "class_type": "SaveImage", 90 | "_meta": { 91 | "title": "Save Image" 92 | } 93 | } 94 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux-dev_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "prompt": 2, 3 | "image_size": 4, 4 | "output": 6, 5 | "seed": 3 6 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux-schnell.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "models\\flux1-schnell-bnb-nf4.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderNF4", 7 | "_meta": { 8 | "title": "CheckpointLoaderNF4" 9 | } 10 | }, 11 | "2": { 12 | "inputs": { 13 | "text": "a tank", 14 | "clip": [ 15 | "1", 16 | 1 17 | ] 18 | }, 19 | "class_type": "CLIPTextEncode", 20 | "_meta": { 21 | "title": "CLIP Text Encode (Prompt)" 22 | } 23 | }, 24 | "3": { 25 | "inputs": { 26 | "seed": 0, 27 | "steps": 4, 28 | "cfg": 1, 29 | "sampler_name": "euler", 30 | "scheduler": "simple", 31 | "denoise": 1, 32 | "model": [ 33 | "1", 34 | 0 35 | ], 36 | "positive": [ 37 | "2", 38 | 0 39 | ], 40 | "negative": [ 41 | "2", 42 | 0 43 | ], 44 | "latent_image": [ 45 | "4", 46 | 0 47 | ] 48 | }, 49 | "class_type": "KSampler", 50 | "_meta": { 51 | "title": "KSampler" 52 | } 53 | }, 54 | "4": { 55 | "inputs": { 56 | "width": 512, 57 | "height": 768, 58 | "batch_size": 1 59 | }, 60 | "class_type": "EmptyLatentImage", 61 | "_meta": { 62 | "title": "Empty Latent Image" 63 | } 64 | }, 65 | "5": { 66 | "inputs": { 67 | "samples": [ 68 | "3", 69 | 0 70 | ], 71 | "vae": [ 72 | "1", 73 | 2 74 | ] 75 | }, 76 | "class_type": "VAEDecode", 77 | "_meta": { 78 | "title": "VAE Decode" 79 | } 80 | }, 81 | "6": { 82 | "inputs": { 83 | "filename_prefix": "ComfyUI", 84 | "images": [ 85 | "5", 86 | 0 87 | ] 88 | }, 89 | "class_type": "SaveImage", 90 | "_meta": { 91 | "title": "Save Image" 92 | } 93 | } 94 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux-schnell_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "prompt": 2, 3 | "image_size": 4, 4 | "output": 6, 5 | "seed": 3 6 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux修手.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "context_expand_pixels": 100, 5 | "context_expand_factor": 1, 6 | "fill_mask_holes": true, 7 | "blur_mask_pixels": 16, 8 | "invert_mask": false, 9 | "blend_pixels": 16, 10 | "rescale_algorithm": "bicubic", 11 | "mode": "ranged size", 12 | "force_width": 1024, 13 | "force_height": 1024, 14 | "rescale_factor": 1, 15 | "min_width": 512, 16 | "min_height": 512, 17 | "max_width": 768, 18 | "max_height": 768, 19 | "padding": 32, 20 | "image": [ 21 | "47", 22 | 0 23 | ], 24 | "mask": [ 25 | "50", 26 | 0 27 | ] 28 | }, 29 | "class_type": "InpaintCrop", 30 | "_meta": { 31 | "title": "✂️ Inpaint Crop" 32 | } 33 | }, 34 | "2": { 35 | "inputs": { 36 | "rescale_algorithm": "bislerp", 37 | "stitch": [ 38 | "1", 39 | 0 40 | ], 41 | "inpainted_image": [ 42 | "15", 43 | 0 44 | ] 45 | }, 46 | "class_type": "InpaintStitch", 47 | "_meta": { 48 | "title": "✂️ Inpaint Stitch" 49 | } 50 | }, 51 | "3": { 52 | "inputs": { 53 | "image": "a87ed50d8e69b8bfb62df848bac69d12.png", 54 | "upload": "image" 55 | }, 56 | "class_type": "LoadImage", 57 | "_meta": { 58 | "title": "Load Image" 59 | } 60 | }, 61 | "15": { 62 | "inputs": { 63 | "samples": [ 64 | "100", 65 | 0 66 | ], 67 | "vae": [ 68 | "99", 69 | 2 70 | ] 71 | }, 72 | "class_type": "VAEDecode", 73 | "_meta": { 74 | "title": "VAE Decode" 75 | } 76 | }, 77 | "19": { 78 | "inputs": { 79 | "positive": [ 80 | "32", 81 | 0 82 | ], 83 | "negative": [ 84 | "32", 85 | 0 86 | ], 87 | "vae": [ 88 | "99", 89 | 2 90 | ], 91 | "pixels": [ 92 | "1", 93 | 1 94 | ], 95 | "mask": [ 96 | "1", 97 | 2 98 | ] 99 | }, 100 | "class_type": "InpaintModelConditioning", 101 | "_meta": { 102 | "title": "InpaintModelConditioning" 103 | } 104 | }, 105 | "25": { 106 | "inputs": { 107 | "rescale_algorithm": "bicubic", 108 | "mode": "ensure minimum size", 109 | "min_width": 0, 110 | "min_height": 1536, 111 | "rescale_factor": 1, 112 | "image": [ 113 | "26", 114 | 0 115 | ], 116 | "mask": [ 117 | "26", 118 | 1 119 | ] 120 | }, 121 | "class_type": "InpaintResize", 122 | "_meta": { 123 | "title": "✂️ Resize Image Before Inpainting" 124 | } 125 | }, 126 | "26": { 127 | "inputs": { 128 | "sam_model": "sam_vit_h (2.56GB)", 129 | "grounding_dino_model": "GroundingDINO_SwinB (938MB)", 130 | "threshold": 0.3, 131 | "detail_method": "VITMatte", 132 | "detail_erode": 6, 133 | "detail_dilate": 6, 134 | "black_point": 0.15, 135 | "white_point": 0.99, 136 | "process_detail": false, 137 | "prompt": "hand", 138 | "device": "cuda", 139 | "max_megapixels": 2, 140 | "cache_model": false, 141 | "image": [ 142 | "3", 143 | 0 144 | ] 145 | }, 146 | "class_type": "LayerMask: SegmentAnythingUltra V2", 147 | "_meta": { 148 | "title": "LayerMask: SegmentAnythingUltra V2" 149 | } 150 | }, 151 | "32": { 152 | "inputs": { 153 | "text": "Masterpiece, High Definition, Real Person Portrait, 5 Fingers, Girl's Hand", 154 | "clip": [ 155 | "99", 156 | 1 157 | ] 158 | }, 159 | "class_type": "CLIPTextEncode", 160 | "_meta": { 161 | "title": "CLIP Text Encode (Prompt)" 162 | } 163 | }, 164 | "47": { 165 | "inputs": { 166 | "fill_background": false, 167 | "background_color": "#000000", 168 | "RGBA_image": [ 169 | "25", 170 | 0 171 | ], 172 | "mask": [ 173 | "25", 174 | 1 175 | ] 176 | }, 177 | "class_type": "LayerUtility: ImageRemoveAlpha", 178 | "_meta": { 179 | "title": "LayerUtility: ImageRemoveAlpha" 180 | } 181 | }, 182 | "50": { 183 | "inputs": { 184 | "expand": 30, 185 | "incremental_expandrate": 0.1, 186 | "tapered_corners": false, 187 | "flip_input": false, 188 | "blur_radius": 10, 189 | "lerp_alpha": 1, 190 | "decay_factor": 1, 191 | "fill_holes": false, 192 | "mask": [ 193 | "25", 194 | 1 195 | ] 196 | }, 197 | "class_type": "GrowMaskWithBlur", 198 | "_meta": { 199 | "title": "Grow Mask With Blur" 200 | } 201 | }, 202 | "94": { 203 | "inputs": { 204 | "filename_prefix": "hand_fix", 205 | "images": [ 206 | "2", 207 | 0 208 | ] 209 | }, 210 | "class_type": "SaveImage", 211 | "_meta": { 212 | "title": "Save Image" 213 | } 214 | }, 215 | "99": { 216 | "inputs": { 217 | "ckpt_name": "models\\flux1-dev-bnb-nf4-v2.safetensors" 218 | }, 219 | "class_type": "CheckpointLoaderNF4", 220 | "_meta": { 221 | "title": "CheckpointLoaderNF4" 222 | } 223 | }, 224 | "100": { 225 | "inputs": { 226 | "seed": 266696528873091, 227 | "steps": 20, 228 | "cfg": 1, 229 | "sampler_name": "euler", 230 | "scheduler": "simple", 231 | "denoise": 0.5, 232 | "model": [ 233 | "99", 234 | 0 235 | ], 236 | "positive": [ 237 | "19", 238 | 0 239 | ], 240 | "negative": [ 241 | "19", 242 | 1 243 | ], 244 | "latent_image": [ 245 | "19", 246 | 2 247 | ] 248 | }, 249 | "class_type": "KSampler", 250 | "_meta": { 251 | "title": "KSampler" 252 | } 253 | } 254 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/flux修手_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "prompt": 32, 3 | "output": 94, 4 | "load_image":3 5 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sd3.5_txt2img.json: -------------------------------------------------------------------------------- 1 | { 2 | "4": { 3 | "inputs": { 4 | "ckpt_name": "models\\sd3.5_large.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Load Checkpoint" 9 | } 10 | }, 11 | "6": { 12 | "inputs": { 13 | "text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle,", 14 | "clip": [ 15 | "11", 16 | 0 17 | ] 18 | }, 19 | "class_type": "CLIPTextEncode", 20 | "_meta": { 21 | "title": "CLIP Text Encode (Prompt)" 22 | } 23 | }, 24 | "8": { 25 | "inputs": { 26 | "samples": [ 27 | "294", 28 | 0 29 | ], 30 | "vae": [ 31 | "4", 32 | 2 33 | ] 34 | }, 35 | "class_type": "VAEDecode", 36 | "_meta": { 37 | "title": "VAE Decode" 38 | } 39 | }, 40 | "11": { 41 | "inputs": { 42 | "clip_name1": "clip_g.pth", 43 | "clip_name2": "clip_l.safetensors", 44 | "clip_name3": "t5xxl_fp16.safetensors" 45 | }, 46 | "class_type": "TripleCLIPLoader", 47 | "_meta": { 48 | "title": "TripleCLIPLoader" 49 | } 50 | }, 51 | "13": { 52 | "inputs": { 53 | "shift": 3, 54 | "model": [ 55 | "4", 56 | 0 57 | ] 58 | }, 59 | "class_type": "ModelSamplingSD3", 60 | "_meta": { 61 | "title": "ModelSamplingSD3" 62 | } 63 | }, 64 | "67": { 65 | "inputs": { 66 | "conditioning": [ 67 | "71", 68 | 0 69 | ] 70 | }, 71 | "class_type": "ConditioningZeroOut", 72 | "_meta": { 73 | "title": "ConditioningZeroOut" 74 | } 75 | }, 76 | "68": { 77 | "inputs": { 78 | "start": 0.1, 79 | "end": 1, 80 | "conditioning": [ 81 | "67", 82 | 0 83 | ] 84 | }, 85 | "class_type": "ConditioningSetTimestepRange", 86 | "_meta": { 87 | "title": "ConditioningSetTimestepRange" 88 | } 89 | }, 90 | "69": { 91 | "inputs": { 92 | "conditioning_1": [ 93 | "68", 94 | 0 95 | ], 96 | "conditioning_2": [ 97 | "70", 98 | 0 99 | ] 100 | }, 101 | "class_type": "ConditioningCombine", 102 | "_meta": { 103 | "title": "Conditioning (Combine)" 104 | } 105 | }, 106 | "70": { 107 | "inputs": { 108 | "start": 0, 109 | "end": 0.1, 110 | "conditioning": [ 111 | "71", 112 | 0 113 | ] 114 | }, 115 | "class_type": "ConditioningSetTimestepRange", 116 | "_meta": { 117 | "title": "ConditioningSetTimestepRange" 118 | } 119 | }, 120 | "71": { 121 | "inputs": { 122 | "text": "", 123 | "clip": [ 124 | "11", 125 | 0 126 | ] 127 | }, 128 | "class_type": "CLIPTextEncode", 129 | "_meta": { 130 | "title": "CLIP Text Encode (Prompt)" 131 | } 132 | }, 133 | "135": { 134 | "inputs": { 135 | "width": 1024, 136 | "height": 1024, 137 | "batch_size": 1 138 | }, 139 | "class_type": "EmptySD3LatentImage", 140 | "_meta": { 141 | "title": "EmptySD3LatentImage" 142 | } 143 | }, 144 | "294": { 145 | "inputs": { 146 | "seed": 143084108695924, 147 | "steps": 20, 148 | "cfg": 4.5, 149 | "sampler_name": "dpmpp_2m", 150 | "scheduler": "sgm_uniform", 151 | "denoise": 1, 152 | "model": [ 153 | "13", 154 | 0 155 | ], 156 | "positive": [ 157 | "6", 158 | 0 159 | ], 160 | "negative": [ 161 | "69", 162 | 0 163 | ], 164 | "latent_image": [ 165 | "135", 166 | 0 167 | ] 168 | }, 169 | "class_type": "KSampler", 170 | "_meta": { 171 | "title": "KSampler" 172 | } 173 | }, 174 | "302": { 175 | "inputs": { 176 | "filename_prefix": "ComfyUI", 177 | "images": [ 178 | "8", 179 | 0 180 | ] 181 | }, 182 | "class_type": "SaveImage", 183 | "_meta": { 184 | "title": "Save Image" 185 | } 186 | } 187 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sd3.5_txt2img_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "prompt": 6, 3 | "negative_prompt": 71, 4 | "image_size": 135, 5 | "output": 302, 6 | "seed": 294 7 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_img2img.json: -------------------------------------------------------------------------------- 1 | { 2 | "3": { 3 | "inputs": { 4 | "seed": 280823642470253, 5 | "steps": 20, 6 | "cfg": 8, 7 | "sampler_name": "dpmpp_2m", 8 | "scheduler": "normal", 9 | "denoise": 0.8700000000000001, 10 | "model": [ 11 | "14", 12 | 0 13 | ], 14 | "positive": [ 15 | "6", 16 | 0 17 | ], 18 | "negative": [ 19 | "7", 20 | 0 21 | ], 22 | "latent_image": [ 23 | "12", 24 | 0 25 | ] 26 | }, 27 | "class_type": "KSampler", 28 | "_meta": { 29 | "title": "KSampler" 30 | } 31 | }, 32 | "6": { 33 | "inputs": { 34 | "text": "photograph of victorian woman with wings, sky clouds, meadow grass\n", 35 | "clip": [ 36 | "14", 37 | 1 38 | ] 39 | }, 40 | "class_type": "CLIPTextEncode", 41 | "_meta": { 42 | "title": "CLIP Text Encode (Prompt)" 43 | } 44 | }, 45 | "7": { 46 | "inputs": { 47 | "text": "watermark, text\n", 48 | "clip": [ 49 | "14", 50 | 1 51 | ] 52 | }, 53 | "class_type": "CLIPTextEncode", 54 | "_meta": { 55 | "title": "CLIP Text Encode (Prompt)" 56 | } 57 | }, 58 | "8": { 59 | "inputs": { 60 | "samples": [ 61 | "3", 62 | 0 63 | ], 64 | "vae": [ 65 | "14", 66 | 2 67 | ] 68 | }, 69 | "class_type": "VAEDecode", 70 | "_meta": { 71 | "title": "VAE Decode" 72 | } 73 | }, 74 | "9": { 75 | "inputs": { 76 | "filename_prefix": "ComfyUI", 77 | "images": [ 78 | "8", 79 | 0 80 | ] 81 | }, 82 | "class_type": "SaveImage", 83 | "_meta": { 84 | "title": "Save Image" 85 | } 86 | }, 87 | "10": { 88 | "inputs": { 89 | "image": "example.png", 90 | "upload": "image" 91 | }, 92 | "class_type": "LoadImage", 93 | "_meta": { 94 | "title": "Load Image" 95 | } 96 | }, 97 | "12": { 98 | "inputs": { 99 | "pixels": [ 100 | "10", 101 | 0 102 | ], 103 | "vae": [ 104 | "14", 105 | 2 106 | ] 107 | }, 108 | "class_type": "VAEEncode", 109 | "_meta": { 110 | "title": "VAE Encode" 111 | } 112 | }, 113 | "14": { 114 | "inputs": { 115 | "ckpt_name": "v1-5-pruned-emaonly.ckpt" 116 | }, 117 | "class_type": "CheckpointLoaderSimple", 118 | "_meta": { 119 | "title": "Load Checkpoint" 120 | } 121 | } 122 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_img2img_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "sampler": 3, 3 | "prompt": 6, 4 | "image_size": 5, 5 | "negative_prompt": 7, 6 | "checkpoint": 14, 7 | "output": 9, 8 | "load_image":10 9 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_txt2img.json: -------------------------------------------------------------------------------- 1 | { 2 | "3": { 3 | "inputs": { 4 | "seed": 567570346829551, 5 | "steps": 20, 6 | "cfg": 8, 7 | "sampler_name": "euler", 8 | "scheduler": "normal", 9 | "denoise": 1, 10 | "model": [ 11 | "4", 12 | 0 13 | ], 14 | "positive": [ 15 | "6", 16 | 0 17 | ], 18 | "negative": [ 19 | "7", 20 | 0 21 | ], 22 | "latent_image": [ 23 | "5", 24 | 0 25 | ] 26 | }, 27 | "class_type": "KSampler", 28 | "_meta": { 29 | "title": "KSampler" 30 | } 31 | }, 32 | "4": { 33 | "inputs": { 34 | "ckpt_name": "models\\DiaoDaia_mix_4.5.ckpt" 35 | }, 36 | "class_type": "CheckpointLoaderSimple", 37 | "_meta": { 38 | "title": "Load Checkpoint" 39 | } 40 | }, 41 | "5": { 42 | "inputs": { 43 | "width": 512, 44 | "height": 512, 45 | "batch_size": 1 46 | }, 47 | "class_type": "EmptyLatentImage", 48 | "_meta": { 49 | "title": "Empty Latent Image" 50 | } 51 | }, 52 | "6": { 53 | "inputs": { 54 | "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,", 55 | "clip": [ 56 | "4", 57 | 1 58 | ] 59 | }, 60 | "class_type": "CLIPTextEncode", 61 | "_meta": { 62 | "title": "CLIP Text Encode (Prompt)" 63 | } 64 | }, 65 | "7": { 66 | "inputs": { 67 | "text": "text, watermark", 68 | "clip": [ 69 | "4", 70 | 1 71 | ] 72 | }, 73 | "class_type": "CLIPTextEncode", 74 | "_meta": { 75 | "title": "CLIP Text Encode (Prompt)" 76 | } 77 | }, 78 | "8": { 79 | "inputs": { 80 | "samples": [ 81 | "3", 82 | 0 83 | ], 84 | "vae": [ 85 | "4", 86 | 2 87 | ] 88 | }, 89 | "class_type": "VAEDecode", 90 | "_meta": { 91 | "title": "VAE Decode" 92 | } 93 | }, 94 | "9": { 95 | "inputs": { 96 | "filename_prefix": "ComfyUI", 97 | "images": [ 98 | "8", 99 | 0 100 | ] 101 | }, 102 | "class_type": "SaveImage", 103 | "_meta": { 104 | "title": "Save Image" 105 | } 106 | } 107 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_txt2img_hr_fix.json: -------------------------------------------------------------------------------- 1 | { 2 | "3": { 3 | "inputs": { 4 | "seed": 213416933995644, 5 | "steps": 20, 6 | "cfg": 8, 7 | "sampler_name": "euler_ancestral", 8 | "scheduler": "normal", 9 | "denoise": 1, 10 | "model": [ 11 | "4", 12 | 0 13 | ], 14 | "positive": [ 15 | "6", 16 | 0 17 | ], 18 | "negative": [ 19 | "7", 20 | 0 21 | ], 22 | "latent_image": [ 23 | "5", 24 | 0 25 | ] 26 | }, 27 | "class_type": "KSampler", 28 | "_meta": { 29 | "title": "KSampler" 30 | } 31 | }, 32 | "4": { 33 | "inputs": { 34 | "ckpt_name": "models\\1053-S.ckpt" 35 | }, 36 | "class_type": "CheckpointLoaderSimple", 37 | "_meta": { 38 | "title": "Load Checkpoint" 39 | } 40 | }, 41 | "5": { 42 | "inputs": { 43 | "width": 768, 44 | "height": 512, 45 | "batch_size": 1 46 | }, 47 | "class_type": "EmptyLatentImage", 48 | "_meta": { 49 | "title": "Empty Latent Image" 50 | } 51 | }, 52 | "6": { 53 | "inputs": { 54 | "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,", 55 | "clip": [ 56 | "4", 57 | 1 58 | ] 59 | }, 60 | "class_type": "CLIPTextEncode", 61 | "_meta": { 62 | "title": "CLIP Text Encode (Prompt)" 63 | } 64 | }, 65 | "7": { 66 | "inputs": { 67 | "text": "text, watermark", 68 | "clip": [ 69 | "4", 70 | 1 71 | ] 72 | }, 73 | "class_type": "CLIPTextEncode", 74 | "_meta": { 75 | "title": "CLIP Text Encode (Prompt)" 76 | } 77 | }, 78 | "8": { 79 | "inputs": { 80 | "samples": [ 81 | "3", 82 | 0 83 | ], 84 | "vae": [ 85 | "4", 86 | 2 87 | ] 88 | }, 89 | "class_type": "VAEDecode", 90 | "_meta": { 91 | "title": "VAE Decode" 92 | } 93 | }, 94 | "9": { 95 | "inputs": { 96 | "filename_prefix": "ComfyUI", 97 | "images": [ 98 | "18", 99 | 0 100 | ] 101 | }, 102 | "class_type": "SaveImage", 103 | "_meta": { 104 | "title": "Save Image" 105 | } 106 | }, 107 | "10": { 108 | "inputs": { 109 | "upscale_method": "nearest-exact", 110 | "width": 1536, 111 | "height": 1152, 112 | "crop": "disabled", 113 | "samples": [ 114 | "16", 115 | 0 116 | ] 117 | }, 118 | "class_type": "LatentUpscale", 119 | "_meta": { 120 | "title": "Upscale Latent" 121 | } 122 | }, 123 | "12": { 124 | "inputs": { 125 | "model_name": "RealESRGAN_x4plus.pth" 126 | }, 127 | "class_type": "UpscaleModelLoader", 128 | "_meta": { 129 | "title": "Load Upscale Model" 130 | } 131 | }, 132 | "14": { 133 | "inputs": { 134 | "upscale_model": [ 135 | "12", 136 | 0 137 | ], 138 | "image": [ 139 | "8", 140 | 0 141 | ] 142 | }, 143 | "class_type": "ImageUpscaleWithModel", 144 | "_meta": { 145 | "title": "Upscale Image (using Model)" 146 | } 147 | }, 148 | "15": { 149 | "inputs": { 150 | "upscale_method": "area", 151 | "width": 1152, 152 | "height": 768, 153 | "crop": "disabled", 154 | "image": [ 155 | "14", 156 | 0 157 | ] 158 | }, 159 | "class_type": "ImageScale", 160 | "_meta": { 161 | "title": "Upscale Image" 162 | } 163 | }, 164 | "16": { 165 | "inputs": { 166 | "pixels": [ 167 | "15", 168 | 0 169 | ], 170 | "vae": [ 171 | "4", 172 | 2 173 | ] 174 | }, 175 | "class_type": "VAEEncode", 176 | "_meta": { 177 | "title": "VAE Encode" 178 | } 179 | }, 180 | "18": { 181 | "inputs": { 182 | "samples": [ 183 | "19", 184 | 0 185 | ], 186 | "vae": [ 187 | "4", 188 | 2 189 | ] 190 | }, 191 | "class_type": "VAEDecode", 192 | "_meta": { 193 | "title": "VAE Decode" 194 | } 195 | }, 196 | "19": { 197 | "inputs": { 198 | "seed": 1069147258069384, 199 | "steps": 8, 200 | "cfg": 8, 201 | "sampler_name": "euler", 202 | "scheduler": "sgm_uniform", 203 | "denoise": 0.6, 204 | "model": [ 205 | "4", 206 | 0 207 | ], 208 | "positive": [ 209 | "21", 210 | 0 211 | ], 212 | "negative": [ 213 | "22", 214 | 0 215 | ], 216 | "latent_image": [ 217 | "10", 218 | 0 219 | ] 220 | }, 221 | "class_type": "KSampler", 222 | "_meta": { 223 | "title": "KSampler" 224 | } 225 | }, 226 | "20": { 227 | "inputs": { 228 | "seed": 85387134314530, 229 | "steps": 20, 230 | "cfg": 5.74, 231 | "sampler_name": "dpm_2", 232 | "scheduler": "normal", 233 | "denoise": 1 234 | }, 235 | "class_type": "KSampler", 236 | "_meta": { 237 | "title": "KSampler" 238 | } 239 | }, 240 | "21": { 241 | "inputs": { 242 | "text": "", 243 | "clip": [ 244 | "4", 245 | 1 246 | ] 247 | }, 248 | "class_type": "CLIPTextEncode", 249 | "_meta": { 250 | "title": "CLIP Text Encode (Prompt)" 251 | } 252 | }, 253 | "22": { 254 | "inputs": { 255 | "text": "", 256 | "clip": [ 257 | "4", 258 | 1 259 | ] 260 | }, 261 | "class_type": "CLIPTextEncode", 262 | "_meta": { 263 | "title": "CLIP Text Encode (Prompt)" 264 | } 265 | } 266 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_txt2img_hr_fix_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "sampler": 3, 3 | "prompt": 6, 4 | "image_size": 5, 5 | "negative_prompt": 7, 6 | "checkpoint": 4, 7 | "output": 9, 8 | "latentupscale": 10, 9 | "resize": 15, 10 | "hr_steps": 19, 11 | "hr_prompt": 21, 12 | "hr_negative_prompt": 22 13 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/comfyui_workflows/sdbase_txt2img_reflex.json: -------------------------------------------------------------------------------- 1 | { 2 | "sampler": 3, 3 | "prompt": 6, 4 | "image_size": 5, 5 | "negative_prompt": 7, 6 | "checkpoint": 4, 7 | "output": 9 8 | } -------------------------------------------------------------------------------- /DrawBridgeAPI/config_example.yaml: -------------------------------------------------------------------------------- 1 | enable_backends: 2 | enable_txt2img_backends: 3 | comfyui: [0] 4 | a1111webui: [0,1] 5 | novelai: [{"0": "queue"}] # 使用队列管理 (禁止并发) 6 | enable_sdapi_backends: 7 | a1111webui: [0,1] 8 | enable_img2img_backends: 9 | a1111webui: [0,1] 10 | enable_comfyui_backends: 11 | comfyui: [0] 12 | 13 | backends: 14 | civitai: # {"token": []} 15 | api: 16 | - b0ab2e10591a.... 17 | name: 18 | - civitaiAPI 19 | a1111webui: 20 | api: 21 | - http://192.168.1.104:7860 22 | - http://127.0.0.1:20224 23 | name: 24 | - RTX2060 25 | - RTX2080 26 | auth: 27 | - false 28 | - false 29 | username: 30 | - admin 31 | - admin 32 | password: 33 | - admin 34 | - admin 35 | fal_ai: 36 | api: 37 | - 6e3c0665-f... 38 | name: 39 | - myfirsttoken 40 | replicate: 41 | api: 42 | - r8_bBAjALaO.. 43 | name: 44 | - default 45 | liblibai: 46 | # https://www.liblib.art/ # 按下F12 -> 应用 -> cookies -> https://www.liblib.art -> usertoken 的值 d812c12d83c640..... 47 | api: # 48 | - f45123447d # 大号 49 | - 4de16d49bf7c44c0 # 2580120068 50 | - 66c70f855fd # 微信 51 | name: 52 | - google 53 | - 2580120068 54 | - wechat 55 | override: 56 | - 57 | model_path: 2931607 58 | model: NoobXL 59 | prompt: 60 | negative_prompt: 61 | xl: true 62 | steps: 35 63 | 64 | - model_path: 2675606 65 | model: liblib.art/modelinfo/fe3aac47589d4a20b24d0a6b045d607e 66 | prompt: 67 | negative_prompt: easynegative 68 | xl: false 69 | steps: 20 70 | 71 | - model_path: 2676318 72 | model: liblib.art/modelinfo/5ecc3218f1ef483ab63eeb4e4cff30cc 73 | prompt: "score_9,score_8_up,score_7_up,score_anime,score_anime_8_up" 74 | negative_prompt: "score_3,poorly drawn,bad anatomy,bad proportions, watercolor painting, brush strokes,3d,2.5d,signature,watermark,bad face,distorted face,messed up eyes,deformed,(low quality, bad quality, worst quality:1.2),bad hand" 75 | xl: true 76 | steps: 28 77 | 78 | tusiart: 79 | api: 80 | - eyJhbGciOiJIUzI1NiIsInR5... 81 | name: 82 | - 移动 83 | referer: 84 | - https://tusiart.com/u/759779980971380287 85 | override: 86 | - model_path: 758751795863586176 87 | prompt: "best quality" 88 | 89 | seaart: 90 | api: 91 | - eyJhbGciOiJSUzUxMiIs... 92 | name: 93 | - default 94 | 95 | yunjie: 96 | api: 97 | - "rsat:NUh5MOBfEccVuUmuwsyqT4fmX7O" 98 | name: 99 | - 移动 100 | 101 | comfyui: 102 | api: 103 | - http://10.147.20.155:8188 104 | name: 105 | - RTX2080TI 106 | override: 107 | - model_path: noobaiXLNAIXL_epsilonPred075.safetensors 108 | prompt: "best quality" 109 | negative_prompt: ",bad hands, worst quality, low quality," 110 | comfyui_api_json: sdbase_txt2img 111 | 112 | novelai: 113 | api: 114 | - eyJhbGciOi... 115 | name: 116 | - default 117 | override: 118 | - model_path: nai-diffusion-3 119 | midjourney: 120 | api: 121 | - http://192.168.5.206:8081 122 | name: 123 | - myserver 124 | auth_toekn: 125 | - null 126 | 127 | 128 | server_settings: 129 | redis_server: 130 | - 127.0.0.1 131 | - 6379 132 | - null 133 | - 2 134 | enable_nsfw_check: 135 | false 136 | save_image: 137 | true 138 | build_in_tagger: 139 | false 140 | llm_caption: 141 | enable: 142 | false 143 | clip: 144 | google/siglip-so400m-patch14-384 145 | llm: 146 | unsloth/Meta-Llama-3.1-8B-bnb-4bit 147 | image_adapter: # https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/tree/main/wpkklhc6 148 | image_adapter.pt 149 | build_in_photoai: 150 | exec_path: 151 | "C:\\Program Files\\Topaz Labs LLC\\Topaz Photo AI\\tpai.exe" 152 | 153 | start_gradio: 154 | False 155 | same_port_with_api: 156 | False 157 | 158 | proxy: 159 | null 160 | 161 | -------------------------------------------------------------------------------- /DrawBridgeAPI/dbapi.py: -------------------------------------------------------------------------------- 1 | import os 2 | import subprocess 3 | 4 | os.chdir('/root') 5 | 6 | os.environ['PIP_INDEX_URL'] = 'https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple' 7 | os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' 8 | 9 | subprocess.run(['apt', 'update']) 10 | subprocess.run(['apt', 'install', '-y', 'aria2c', 'sshpass']) 11 | subprocess.run(['pip', 'install', 'jupyterlab', '--break-system-packages']) 12 | 13 | while True: 14 | os.system('python app.py & jupyter-lab --no-browser --ip=0.0.0.0 --allow-root --notebook-dir=/ --port=65432 --LabApp.allow_origin=* --LabApp.token= --LabApp.base_url=/diao') -------------------------------------------------------------------------------- /DrawBridgeAPI/locales/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import gettext 3 | import locale 4 | 5 | locale_dir = os.path.join(os.path.dirname(__file__), '../locales') 6 | lang = os.getenv('LANG', None) 7 | language, _ = locale.getdefaultlocale() 8 | 9 | lang = gettext.translation( 10 | 'messages', localedir=locale_dir, languages=[lang or language], fallback=True 11 | ) 12 | 13 | lang.install() 14 | 15 | _ = lang.gettext 16 | -------------------------------------------------------------------------------- /DrawBridgeAPI/locales/zh_CN/LC_MESSAGES/messages.mo: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/DrawBridgeAPI/locales/zh_CN/LC_MESSAGES/messages.mo -------------------------------------------------------------------------------- /DrawBridgeAPI/locales/zh_CN/LC_MESSAGES/messages.po: -------------------------------------------------------------------------------- 1 | msgid "Loading config file completed" 2 | msgstr "加载配置文件完成" 3 | 4 | msgid "API initialization completed" 5 | msgstr "API初始化完成" 6 | 7 | msgid "Redis connection successful" 8 | msgstr "Redis连接成功" 9 | 10 | msgid "Exec TXT2IMG" 11 | msgstr "开始进行文生图" 12 | 13 | msgid "IMG2IMG Requires image to start" 14 | msgstr "图生图需要图片来启动" 15 | 16 | msgid "Exec IMG2IMG" 17 | msgstr "开始进行图生图" 18 | 19 | msgid "Caption Successful" 20 | msgstr "打标成功" 21 | 22 | msgid "Lock to backend has configured" 23 | msgstr "设置已经锁定后端" 24 | 25 | msgid "URL detected" 26 | msgstr "检测到url" 27 | 28 | msgid "Image download failed!" 29 | msgstr "图片下载失败!" 30 | 31 | msgid "Exec forwarding" 32 | msgstr "开始进行转发" 33 | 34 | msgid "Waiting for API initialization" 35 | msgstr "请等待API初始化" 36 | 37 | msgid "Loading LLM" 38 | msgstr "LLM加载中" 39 | 40 | msgid "LLM loading completed, waiting for command" 41 | msgstr "LLM加载完成,等待命令" 42 | 43 | msgid "Loading Checkpoint" 44 | msgstr "模型加载中" 45 | 46 | msgid "Checkpoint loading completed, waiting for command" 47 | msgstr "模型加载完成,等待命令" 48 | 49 | msgid "Server is ready!" 50 | msgstr "服务器准备就绪!" 51 | 52 | msgid "Manually select model" 53 | msgstr "手动选择模型" 54 | 55 | msgid "Backend select" 56 | msgstr "已选择后端" 57 | 58 | msgid "Backend locked" 59 | msgstr "已锁定后端" 60 | 61 | msgid "Starting backend selection" 62 | msgstr "开始进行后端选择" 63 | 64 | msgid "Backend {0} is down" 65 | msgstr "后端 {0} 掉线" 66 | 67 | msgid "Backend {0} is failed or locked" 68 | msgstr "后端 {0} 出错或者锁定中" 69 | 70 | msgid "No available backend" 71 | msgstr "没有可用后端" 72 | 73 | msgid "Backend: {0} Average work time: {1} seconds, Current tasks: {2}" 74 | msgstr "后端: {0} 平均工作时间: {1}秒, 现在进行中的任务: {2}" 75 | 76 | msgid "Extra time weight" 77 | msgstr "额外的时间权重" 78 | 79 | msgid "Backend: {0} is the fastest, has been selected" 80 | msgstr "后端{0}最快, 已经选择" 81 | 82 | msgid "Task completed successfully" 83 | msgstr "任务成功完成" 84 | 85 | msgid "Task failed" 86 | msgstr "任务失败" 87 | 88 | msgid "Remaining tasks in the queue" 89 | msgstr "队列中的剩余任务" 90 | 91 | msgid "No remaining tasks in the queue" 92 | msgstr "队列中已无任务" 93 | 94 | msgid "Forwarding request" 95 | msgstr "已转发请求" 96 | 97 | msgid "Backend returned error" 98 | msgstr "后端返回错误" 99 | 100 | msgid "Backend not using built-in multi-image generation management" 101 | msgstr "后端不使用内置多图生成管理" 102 | 103 | msgid "A1111 Backend, not using built-in multi-image generation management" 104 | msgstr "A1111后端, 不使用内置多图生成管理" 105 | 106 | msgid "Over maximum retry times, posting still failed" 107 | msgstr "超过最大重试次数之后依然失败" 108 | 109 | msgid "Request completed, took {0} seconds" 110 | msgstr "请求完成, 花费{0}秒" 111 | 112 | msgid "VRAM OOM detected, auto model unload and reload" 113 | msgstr "检测到爆显存，执行自动模型释放并加载" 114 | 115 | msgid "Get a respond image, processing" 116 | msgstr "获取到返回图片，正在处理" 117 | 118 | msgid "Request failed, error message:" 119 | msgstr "请求失败，错误信息:" 120 | 121 | msgid "Downloading image successful" 122 | msgstr "图片下载成功" 123 | 124 | msgid "Selected ComfyUI style" 125 | msgstr "已选择ComfyUI工作流" 126 | 127 | msgid "Working directory: {0}" 128 | msgstr "工作目录：{0}" 129 | 130 | msgid "Starting image audit" 131 | msgstr "开始图片审核" 132 | 133 | msgid "NSFW Detected" 134 | msgstr "检测到不合适图片" 135 | 136 | msgid "Image safe" 137 | msgstr "图片安全" 138 | 139 | msgid "Backend {0} is enabled" 140 | msgstr "后端 {0} 已启用" -------------------------------------------------------------------------------- /DrawBridgeAPI/ui/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/DrawBridgeAPI/ui/__init__.py -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/__init__.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | import httpx 4 | from fastapi.exceptions import HTTPException 5 | from ..base_config import init_instance 6 | config = init_instance.config 7 | import asyncio 8 | 9 | 10 | async def http_request( 11 | method, 12 | target_url, 13 | headers=None, 14 | params=None, 15 | content=None, 16 | format=True 17 | ): 18 | async with httpx.AsyncClient() as client: 19 | 20 | response = await client.request( 21 | method, 22 | target_url, 23 | headers=headers, 24 | params=params, 25 | content=content 26 | ) 27 | 28 | if response.status_code != 200: 29 | raise HTTPException(500) 30 | if format: 31 | return response.json() 32 | else: 33 | return response 34 | 35 | 36 | async def run_later(func, delay=1): 37 | loop = asyncio.get_running_loop() 38 | loop.call_later( 39 | delay, 40 | lambda: loop.create_task( 41 | func 42 | ) 43 | ) 44 | 45 | 46 | async def txt_audit( 47 | msg, 48 | prompt=''' 49 | 接下来请你对一些聊天内容进行审核, 50 | 如果内容出现政治/暴恐内容（特别是我国的政治人物/或者和我国相关的政治）则请你输出, 51 | 如果没有则输出 52 | ''' 53 | ): 54 | 55 | from ..backend import Backend 56 | 57 | system = [ 58 | {"role": "system", 59 | "content": prompt} 60 | ] 61 | 62 | prompt = [{"role": "user", "content": msg}] 63 | 64 | try: 65 | resp = Backend.http_request( 66 | "POST", 67 | f"http://{config['prompt_audit']['site']}/v1/chat/completions", 68 | {"Authorization": config['prompt_audit']['api_key']}, 69 | timeout=300, 70 | format=True, 71 | content= json.dumps( 72 | { 73 | "model": "gpt-3.5-turbo", 74 | "messages": system + prompt, 75 | "max_tokens": 4000, 76 | } 77 | ) 78 | ) 79 | except: 80 | return "yes" 81 | else: 82 | res: str = remove_punctuation(resp['choices'][0]['message']['content'].strip()) 83 | return res 84 | 85 | 86 | def remove_punctuation(text): 87 | import string 88 | for i in range(len(text)): 89 | if text[i] not in string.punctuation: 90 | return text[i:] 91 | return "" -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/custom_class.py: -------------------------------------------------------------------------------- 1 | from fal_client.client import AsyncClient 2 | from fal_client.auth import fetch_credentials 3 | 4 | import httpx 5 | import os 6 | 7 | USER_AGENT = "fal-client/0.2.2 (python)" 8 | 9 | 10 | class CustomAsyncClient(AsyncClient): 11 | def __init__(self, key=None, default_timeout=120.0): 12 | if key is None: 13 | key = os.getenv("FAL_KEY") 14 | super().__init__(key=key, default_timeout=default_timeout) 15 | 16 | @property 17 | def _client(self): 18 | key = self.key 19 | if key is None: 20 | key = fetch_credentials() 21 | 22 | return httpx.AsyncClient( 23 | headers={ 24 | "Authorization": f"Key {key}", 25 | "User-Agent": USER_AGENT, 26 | }, 27 | timeout=self.default_timeout, 28 | ) -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/exceptions.py: -------------------------------------------------------------------------------- 1 | class DrawBridgeAPIException(Exception): 2 | 3 | class DBAPIExceptions(Exception): 4 | pass 5 | 6 | class TokenExpired(DBAPIExceptions): 7 | def __init__(self, message="Token expired."): 8 | self.message = message 9 | super().__init__(self.message) 10 | 11 | class NeedRecaptcha(DBAPIExceptions): 12 | def __init__(self, message="Need Recaptcha."): 13 | self.message = message 14 | super().__init__(self.message) 15 | 16 | -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/llm_caption_requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | numpy 3 | pillow 4 | transformers>=4.43.3 5 | huggingface_hub 6 | protobuf 7 | bitsandbytes 8 | sentencepiece 9 | accelerate -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/llm_captions.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import base64 3 | import warnings 4 | warnings.simplefilter(action='ignore', category=UserWarning) 5 | from torch import nn 6 | from io import BytesIO 7 | from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, \ 8 | AutoModelForCausalLM 9 | import torch 10 | import torch.amp.autocast_mode 11 | from PIL import Image 12 | import numpy as np 13 | from io import BytesIO 14 | 15 | from ..base_config import init_instance , setup_logger 16 | from ..locales import _ 17 | 18 | llm_logger = setup_logger('[LLM-Caption]') 19 | 20 | class JoyPipeline: 21 | def __init__(self): 22 | self.clip_model = None 23 | self.clip_processor = None 24 | self.tokenizer = None 25 | self.text_model = None 26 | self.image_adapter = None 27 | self.parent = None 28 | 29 | def clearCache(self): 30 | self.clip_model = None 31 | self.clip_processor = None 32 | self.tokenizer = None 33 | self.text_model = None 34 | self.image_adapter = None 35 | 36 | 37 | class ImageAdapter(nn.Module): 38 | def __init__(self, input_features: int, output_features: int): 39 | super().__init__() 40 | self.linear1 = nn.Linear(input_features, output_features) 41 | self.activation = nn.GELU() 42 | self.linear2 = nn.Linear(output_features, output_features) 43 | 44 | def forward(self, vision_outputs: torch.Tensor): 45 | x = self.linear1(vision_outputs) 46 | x = self.activation(x) 47 | x = self.linear2(x) 48 | return x 49 | 50 | 51 | class Joy_caption_load: 52 | 53 | def __init__(self): 54 | self.model = None 55 | self.pipeline = JoyPipeline() 56 | self.pipeline.parent = self 57 | self.config = init_instance.config 58 | pass 59 | 60 | def loadCheckPoint(self): 61 | # 清除一波 62 | if self.pipeline != None: 63 | self.pipeline.clearCache() 64 | 65 | # clip 66 | model_id = self.config.server_settings['llm_caption']['clip'] 67 | 68 | model = AutoModel.from_pretrained(model_id) 69 | clip_processor = AutoProcessor.from_pretrained(model_id) 70 | clip_model = AutoModel.from_pretrained( 71 | model_id, 72 | trust_remote_code=True 73 | ) 74 | 75 | clip_model = clip_model.vision_model 76 | clip_model.eval() 77 | clip_model.requires_grad_(False) 78 | clip_model.to("cuda") 79 | 80 | # LLM 81 | model_path_llm = self.config.server_settings['llm_caption']['llm'] 82 | tokenizer = AutoTokenizer.from_pretrained(model_path_llm, use_fast=False) 83 | assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, 84 | PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}" 85 | 86 | text_model = AutoModelForCausalLM.from_pretrained(model_path_llm, device_map="auto", trust_remote_code=True) 87 | text_model.eval() 88 | 89 | # Image Adapte 90 | 91 | image_adapter = ImageAdapter(clip_model.config.hidden_size, 92 | text_model.config.hidden_size) # ImageAdapter(clip_model.config.hidden_size, 4096) 93 | image_adapter.load_state_dict(torch.load(self.config.server_settings['llm_caption']['image_adapter'], map_location="cpu", weights_only=True)) 94 | adjusted_adapter = image_adapter # AdjustedImageAdapter(image_adapter, text_model.config.hidden_size) 95 | adjusted_adapter.eval() 96 | adjusted_adapter.to("cuda") 97 | 98 | self.pipeline.clip_model = clip_model 99 | self.pipeline.clip_processor = clip_processor 100 | self.pipeline.tokenizer = tokenizer 101 | self.pipeline.text_model = text_model 102 | self.pipeline.image_adapter = adjusted_adapter 103 | 104 | def clearCache(self): 105 | if self.pipeline != None: 106 | self.pipeline.clearCache() 107 | 108 | def gen(self, model): 109 | if self.model == None or self.model != model or self.pipeline == None: 110 | self.model = model 111 | self.loadCheckPoint() 112 | return (self.pipeline,) 113 | 114 | 115 | class Joy_caption: 116 | 117 | def __init__(self): 118 | pass 119 | 120 | @staticmethod 121 | def tensor2pil(t_image: torch.Tensor) -> Image: 122 | return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) 123 | 124 | def gen( 125 | self, 126 | joy_pipeline=JoyPipeline, 127 | image=Image, 128 | prompt="A descriptive caption for this image", 129 | max_new_tokens=300, 130 | temperature=0.5, 131 | cache=False 132 | ): 133 | 134 | if joy_pipeline.clip_processor == None: 135 | joy_pipeline.parent.loadCheckPoint() 136 | 137 | clip_processor = joy_pipeline.clip_processor 138 | tokenizer = joy_pipeline.tokenizer 139 | clip_model = joy_pipeline.clip_model 140 | image_adapter = joy_pipeline.image_adapter 141 | text_model = joy_pipeline.text_model 142 | 143 | input_image = image 144 | 145 | # Preprocess image 146 | pImge = clip_processor(images=input_image, return_tensors='pt').pixel_values 147 | pImge = pImge.to('cuda') 148 | 149 | # Tokenize the prompt 150 | prompt = tokenizer.encode(prompt, return_tensors='pt', padding=False, truncation=False, 151 | add_special_tokens=False) 152 | # Embed image 153 | with torch.amp.autocast_mode.autocast('cuda', enabled=True): 154 | vision_outputs = clip_model(pixel_values=pImge, output_hidden_states=True) 155 | image_features = vision_outputs.hidden_states[-2] 156 | embedded_images = image_adapter(image_features) 157 | embedded_images = embedded_images.to('cuda') 158 | 159 | # Embed prompt 160 | prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda')) 161 | assert prompt_embeds.shape == (1, prompt.shape[1], 162 | text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}" 163 | embedded_bos = text_model.model.embed_tokens( 164 | torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64)) 165 | 166 | # Construct prompts 167 | inputs_embeds = torch.cat([ 168 | embedded_bos.expand(embedded_images.shape[0], -1, -1), 169 | embedded_images.to(dtype=embedded_bos.dtype), 170 | prompt_embeds.expand(embedded_images.shape[0], -1, -1), 171 | ], dim=1) 172 | 173 | input_ids = torch.cat([ 174 | torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long), 175 | torch.zeros((1, embedded_images.shape[1]), dtype=torch.long), 176 | prompt, 177 | ], dim=1).to('cuda') 178 | attention_mask = torch.ones_like(input_ids) 179 | 180 | generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, 181 | max_new_tokens=max_new_tokens, do_sample=True, top_k=10, 182 | temperature=temperature, suppress_tokens=None) 183 | 184 | # Trim off the prompt 185 | generate_ids = generate_ids[:, input_ids.shape[1]:] 186 | if generate_ids[0][-1] == tokenizer.eos_token_id: 187 | generate_ids = generate_ids[:, :-1] 188 | 189 | caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] 190 | r = caption.strip() 191 | 192 | if cache == False: 193 | joy_pipeline.parent.clearCache() 194 | 195 | return (r,) 196 | 197 | 198 | class JoyCaptionHandler: 199 | def __init__(self, config): 200 | self.config = config 201 | self.pipeline, self.joy_caption = self._initialize() 202 | 203 | def _initialize(self): 204 | llm_logger.info(_("Loading LLM")) 205 | joy_caption_load = Joy_caption_load() 206 | model_path = self.config.server_settings['llm_caption']['llm'] 207 | pipeline, = joy_caption_load.gen(model_path) 208 | joy_caption = Joy_caption() 209 | llm_logger.info(_("LLM loading completed, waiting for command")) 210 | return pipeline, joy_caption 211 | 212 | async def get_caption(self, image, ntags=[]): 213 | if image.startswith(b"data:image/png;base64,"): 214 | image = image.replace("data:image/png;base64,", "") 215 | image = Image.open(BytesIO(base64.b64decode(image))).convert(mode="RGB") 216 | 217 | extra_ = f"do not describe {','.join(ntags)} if it exist" if ntags else '' 218 | loop = asyncio.get_event_loop() 219 | 220 | caption = await loop.run_in_executor( 221 | None, 222 | self.joy_caption.gen, 223 | self.pipeline, 224 | image, 225 | f"A descriptive caption for this image, do not describe a signature or text in the image,{extra_}", 226 | 300, 227 | 0.5, 228 | True 229 | ) 230 | 231 | return caption[0] 232 | 233 | 234 | config = init_instance.config 235 | if config.server_settings['llm_caption']['enable']: 236 | joy_caption_handler = JoyCaptionHandler(config) 237 | -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/request_model.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel, conint 2 | from dataclasses import field 3 | from typing import Optional, List, Dict, Any 4 | from pathlib import Path 5 | import random 6 | 7 | 8 | class RequetModelClass(BaseModel): 9 | pass 10 | 11 | 12 | class Txt2ImgRequest(RequetModelClass): 13 | prompt: Optional[str] = "" 14 | negative_prompt: Optional[str] = "" 15 | styles: List[str] = [] 16 | seed: int = -1 17 | subseed: int = -1 18 | subseed_strength: float = 0 19 | seed_resize_from_h: int = -1 20 | seed_resize_from_w: int = -1 21 | sampler_name: str = "Euler a" 22 | batch_size: int = 1 23 | n_iter: int = 1 24 | steps: int = 20 25 | cfg_scale: float = 7 26 | width: int = 512 27 | height: int = 512 28 | restore_faces: bool = False 29 | tiling: bool = False 30 | do_not_save_samples: bool = False 31 | do_not_save_grid: bool = False 32 | eta: float = 0 33 | denoising_strength: float = 1 34 | s_min_uncond: float = 0 35 | s_churn: float = 0 36 | s_tmax: float = 0 37 | s_tmin: float = 0 38 | s_noise: float = 0 39 | override_settings: Dict[str, Any] = {} 40 | override_settings_restore_afterwards: bool = False 41 | refiner_checkpoint: str = "" 42 | refiner_switch_at: int = 0 43 | disable_extra_networks: bool = False 44 | comments: Dict[str, Any] = {} 45 | enable_hr: bool = False 46 | firstphase_width: int = 0 47 | firstphase_height: int = 0 48 | hr_scale: float = 2 49 | hr_upscaler: str = "" 50 | hr_second_pass_steps: int = 10 51 | hr_resize_x: int = 0 52 | hr_resize_y: int = 0 53 | hr_checkpoint_name: str = "" 54 | hr_sampler_name: str = "" 55 | hr_prompt: str = "" 56 | hr_negative_prompt: str = "" 57 | sampler_index: str = "Euler a" 58 | script_name: str = "" 59 | script_args: List[Any] = [] 60 | send_images: bool = True 61 | save_images: bool = True 62 | alwayson_scripts: Dict[str, Any] = {} 63 | scheduler: str = "Automatic" 64 | 65 | 66 | class Img2ImgRequest(RequetModelClass): 67 | prompt: Optional[str] = "" 68 | negative_prompt: Optional[str] = "" 69 | styles: List[str] = [] 70 | seed: int = -1 71 | subseed: int = -1 72 | subseed_strength: float = 0 73 | seed_resize_from_h: int = -1 74 | seed_resize_from_w: int = -1 75 | sampler_name: str = "Euler a" 76 | batch_size: int = 1 77 | n_iter: int = 1 78 | steps: int = 50 79 | cfg_scale: float = 7 80 | width: int = 512 81 | height: int = 512 82 | restore_faces: bool = False 83 | tiling: bool = False 84 | do_not_save_samples: bool = False 85 | do_not_save_grid: bool = False 86 | eta: float = 0 87 | denoising_strength: float = 0.75 88 | s_min_uncond: float = 0 89 | s_churn: float = 0 90 | s_tmax: float = 0 91 | s_tmin: float = 0 92 | s_noise: float = 0 93 | override_settings: Dict[str, Any] = {} 94 | override_settings_restore_afterwards: bool = False 95 | refiner_checkpoint: str = "" 96 | refiner_switch_at: int = 0 97 | disable_extra_networks: bool = False 98 | comments: Dict[str, Any] = {} 99 | init_images: List[str] = [""] 100 | resize_mode: int = 0 101 | image_cfg_scale: float = 0 102 | mask: str = None 103 | mask_blur_x: int = 4 104 | mask_blur_y: int = 4 105 | mask_blur: int = 0 106 | inpainting_fill: int = 0 107 | inpaint_full_res: bool = True 108 | inpaint_full_res_padding: int = 0 109 | inpainting_mask_invert: int = 0 110 | initial_noise_multiplier: float = 0 111 | latent_mask: str = "" 112 | sampler_index: str = "Euler a" 113 | include_init_images: bool = False 114 | script_name: str = "" 115 | script_args: List[Any] = [] 116 | send_images: bool = True 117 | save_images: bool = True 118 | alwayson_scripts: Dict[str, Any] = {} 119 | scheduler: str = "Automatic" 120 | # 以下为拓展 121 | 122 | 123 | class TaggerRequest(RequetModelClass): 124 | image: str = '', 125 | model: Optional[str] = 'wd14-vit-v2' 126 | threshold: Optional[float] = 0.35, 127 | exclude_tags: Optional[List[str]] = [] 128 | 129 | 130 | class TopazAiRequest(BaseModel): 131 | image: Optional[str] = None 132 | input_folder: Optional[str | Path] 133 | output_folder: Optional[str] = None 134 | overwrite: Optional[bool] = False 135 | recursive: Optional[bool] = False 136 | format: Optional[str] = "preserve" # 可选值: jpg, jpeg, png, tif, tiff, dng, preserve 137 | quality: Optional[conint(ge=0, le=100)] = 95 # JPEG 质量，0到100之间 138 | compression: Optional[conint(ge=0, le=10)] = 2 # PNG 压缩，0到10之间 139 | bit_depth: Optional[conint(strict=True, ge=8, le=16)] = 16 # TIFF 位深度，8或16 140 | tiff_compression: Optional[str] = "zip" # 可选值: none, lzw, zip 141 | show_settings: Optional[bool] = False 142 | skip_processing: Optional[bool] = False 143 | verbose: Optional[bool] = False 144 | upscale: Optional[bool] = None 145 | noise: Optional[bool] = None 146 | sharpen: Optional[bool] = None 147 | lighting: Optional[bool] = None 148 | color: Optional[bool] = None 149 | 150 | 151 | class SetConfigRequest(BaseModel): 152 | class Config: 153 | extra = "allow" 154 | -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/shared.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | import os 3 | 4 | PATH_TO_COMFYUI_WORKFLOWS = Path(f"{os.path.dirname(os.path.abspath(__file__))}/../comfyui_workflows") 5 | 6 | -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/tagger-requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | numpy 3 | pillow 4 | huggingface_hub 5 | onnxruntime 6 | -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/tagger.py: -------------------------------------------------------------------------------- 1 | import os 2 | import asyncio 3 | 4 | import pandas as pd 5 | import numpy as np 6 | import base64 7 | 8 | from typing import Tuple, List, Dict 9 | from io import BytesIO 10 | from PIL import Image 11 | 12 | from pathlib import Path 13 | from huggingface_hub import hf_hub_download 14 | 15 | from ..base_config import setup_logger, init_instance 16 | from ..locales import _ 17 | 18 | 19 | use_cpu = True 20 | tf_device_name = '/gpu:0' if not use_cpu else '/cpu:0' 21 | 22 | wd_logger = setup_logger('[TAGGER]') 23 | # https://github.com/toriato/stable-diffusion-webui-wd14-tagger 24 | 25 | 26 | class Interrogator: 27 | @staticmethod 28 | def postprocess_tags( 29 | tags: Dict[str, float], 30 | threshold=0.35, 31 | additional_tags: List[str] = [], 32 | exclude_tags: List[str] = [], 33 | sort_by_alphabetical_order=False, 34 | add_confident_as_weight=False, 35 | replace_underscore=False, 36 | replace_underscore_excludes: List[str] = [], 37 | escape_tag=False 38 | ) -> Dict[str, float]: 39 | for t in additional_tags: 40 | tags[t] = 1.0 41 | 42 | tags = { 43 | t: c 44 | for t, c in sorted( 45 | tags.items(), 46 | key=lambda i: i[0 if sort_by_alphabetical_order else 1], 47 | reverse=not sort_by_alphabetical_order 48 | ) 49 | if ( 50 | c >= threshold 51 | and t not in exclude_tags 52 | ) 53 | } 54 | 55 | new_tags = [] 56 | for tag in list(tags): 57 | new_tag = tag 58 | 59 | if replace_underscore and tag not in replace_underscore_excludes: 60 | new_tag = new_tag.replace('_', ' ') 61 | 62 | if escape_tag: 63 | new_tag = tag.replace('_', '\\_') 64 | 65 | if add_confident_as_weight: 66 | new_tag = f'({new_tag}:{tags[tag]})' 67 | 68 | new_tags.append((new_tag, tags[tag])) 69 | tags = dict(new_tags) 70 | 71 | return tags 72 | 73 | def __init__(self, name: str) -> None: 74 | self.name = name 75 | 76 | def load(self): 77 | raise NotImplementedError() 78 | 79 | def unload(self) -> bool: 80 | unloaded = False 81 | 82 | if hasattr(self, 'model') and self.model is not None: 83 | del self.model 84 | unloaded = True 85 | print(f'Unloaded {self.name}') 86 | 87 | if hasattr(self, 'prompt'): 88 | del self.tags 89 | 90 | return unloaded 91 | 92 | def interrogate( 93 | self, 94 | image: Image 95 | ) -> Tuple[ 96 | Dict[str, float], # rating confidents 97 | Dict[str, float] # tag confidents 98 | ]: 99 | raise NotImplementedError() 100 | 101 | 102 | class WaifuDiffusionInterrogator(Interrogator): 103 | def __init__( 104 | self, 105 | name: str, 106 | model_path='model.onnx', 107 | tags_path='selected_tags.csv', 108 | **kwargs 109 | ) -> None: 110 | super().__init__(name) 111 | self.model_path = model_path 112 | self.tags_path = tags_path 113 | self.kwargs = kwargs 114 | 115 | def download(self) -> Tuple[os.PathLike, os.PathLike]: 116 | wd_logger.info(f"Loading {self.name} model file from {self.kwargs['repo_id']}") 117 | 118 | model_path = Path(hf_hub_download( 119 | **self.kwargs, filename=self.model_path)) 120 | tags_path = Path(hf_hub_download( 121 | **self.kwargs, filename=self.tags_path)) 122 | return model_path, tags_path 123 | 124 | def load(self) -> None: 125 | model_path, tags_path = self.download() 126 | 127 | from onnxruntime import InferenceSession 128 | 129 | providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] 130 | if use_cpu: 131 | providers.pop(0) 132 | 133 | self.model = InferenceSession(str(model_path), providers=providers) 134 | 135 | wd_logger.info(f'Loaded {self.name} model from {model_path}') 136 | 137 | self.tags = pd.read_csv(tags_path) 138 | 139 | def interrogate( 140 | self, 141 | image: Image 142 | ) -> Tuple[ 143 | Dict[str, float], # rating confidents 144 | Dict[str, float] # tag confidents 145 | ]: 146 | if not hasattr(self, 'model') or self.model is None: 147 | self.load() 148 | 149 | _, height, _, _ = self.model.get_inputs()[0].shape 150 | 151 | image = image.convert('RGBA') 152 | new_image = Image.new('RGBA', image.size, 'WHITE') 153 | new_image.paste(image, mask=image) 154 | image = new_image.convert('RGB') 155 | image = np.asarray(image) 156 | 157 | image = image[:, :, ::-1] 158 | 159 | # 模拟`dbimutils`的make_square和smart_resize功能 160 | image = self.make_square(image, height) 161 | image = self.smart_resize(image, height) 162 | image = image.astype(np.float32) 163 | image = np.expand_dims(image, 0) 164 | 165 | input_name = self.model.get_inputs()[0].name 166 | label_name = self.model.get_outputs()[0].name 167 | confidents = self.model.run([label_name], {input_name: image})[0] 168 | 169 | tags = self.tags[:][['name']] 170 | tags['confidents'] = confidents[0] 171 | 172 | ratings = dict(tags[:4].values) 173 | tags = dict(tags[4:].values) 174 | 175 | return ratings, tags 176 | 177 | @staticmethod 178 | def make_square(image, size): 179 | old_size = image.shape[:2] 180 | ratio = float(size) / max(old_size) 181 | new_size = tuple([int(x * ratio) for x in old_size]) 182 | image = Image.fromarray(image) 183 | image = image.resize(new_size, Image.LANCZOS) 184 | new_image = Image.new("RGB", (size, size)) 185 | new_image.paste(image, ((size - new_size[0]) // 2, 186 | (size - new_size[1]) // 2)) 187 | return np.array(new_image) 188 | 189 | @staticmethod 190 | def smart_resize(image, size): 191 | image = Image.fromarray(image) 192 | image = image.resize((size, size), Image.LANCZOS) 193 | return np.array(image) 194 | 195 | 196 | class WaifuDiffusionTaggerHandler: 197 | def __init__(self, name, repo_id, revision, model_path, tags_path): 198 | self.name = name 199 | self.repo_id = repo_id 200 | self.revision = revision 201 | self.model_path = model_path 202 | self.tags_path = tags_path 203 | self.wd_instance = self._initialize() 204 | 205 | def _initialize(self): 206 | wd_instance = WaifuDiffusionInterrogator( 207 | name=self.name, 208 | repo_id=self.repo_id, 209 | revision=self.revision, 210 | model_path=self.model_path, 211 | tags_path=self.tags_path 212 | ) 213 | wd_logger.info(_("Loading Checkpoint")) 214 | wd_instance.load() 215 | wd_logger.info(_("Checkpoint loading completed, waiting for command")) 216 | return wd_instance 217 | 218 | async def tagger_main(self, base64_img, threshold, ntags=[], audit=False, ratings_=False): 219 | if base64_img.startswith("data:image/png;base64,"): 220 | base64_img = base64_img.replace("data:image/png;base64,", "") 221 | 222 | image_data = base64.b64decode(base64_img) 223 | image = Image.open(BytesIO(image_data)) 224 | 225 | loop = asyncio.get_event_loop() 226 | ratings, tags = await loop.run_in_executor( 227 | None, 228 | self.wd_instance.interrogate, 229 | image 230 | ) 231 | if ratings_: 232 | return ratings 233 | if audit: 234 | possibilities = ratings 235 | value = list(possibilities.values()) 236 | value.sort(reverse=True) 237 | reverse_dict = {value: key for key, value in possibilities.items()} 238 | return True if reverse_dict[value[0]] == "questionable" or reverse_dict[value[0]] == "explicit" else False 239 | 240 | # 处理标签 241 | processed_tags = Interrogator.postprocess_tags( 242 | tags=tags, 243 | threshold=threshold, 244 | additional_tags=['best quality', 'highres'], 245 | exclude_tags=['lowres'] + ntags, 246 | sort_by_alphabetical_order=False, 247 | add_confident_as_weight=True, 248 | replace_underscore=True, 249 | replace_underscore_excludes=[], 250 | escape_tag=False 251 | ) 252 | 253 | def process_dict(input_dict): 254 | processed_dict = {} 255 | for key, value in input_dict.items(): 256 | cleaned_key = key.strip('()').split(':')[0] 257 | processed_dict[cleaned_key] = value 258 | return processed_dict 259 | 260 | processed_tags = process_dict(processed_tags) 261 | 262 | return {**ratings, **processed_tags} 263 | 264 | 265 | config = init_instance.config 266 | if config.server_settings['build_in_tagger']: 267 | wd_tagger_handler = WaifuDiffusionTaggerHandler( 268 | name='WaifuDiffusion', 269 | repo_id='SmilingWolf/wd-convnext-tagger-v3', 270 | revision='v2.0', 271 | model_path='model.onnx', 272 | tags_path='selected_tags.csv' 273 | ) -------------------------------------------------------------------------------- /DrawBridgeAPI/utils/topaz.py: -------------------------------------------------------------------------------- 1 | import subprocess 2 | from ..base_config import init_instance, setup_logger 3 | 4 | topazai_logger = setup_logger('[TopaAI]') 5 | 6 | 7 | def run_tpai( 8 | input_folder, output_folder=None, overwrite=False, recursive=False, 9 | format="preserve", quality=95, compression=2, bit_depth=16, 10 | tiff_compression="zip", show_settings=False, skip_processing=False, 11 | verbose=False, upscale=None, noise=None, sharpen=None, 12 | lighting=None, color=None, **kwargs 13 | ): 14 | # 基本命令和输入文件夹 15 | command = [rf'"{init_instance.config.server_settings["build_in_photoai"]["exec_path"]}"', f'"{input_folder}"'] 16 | 17 | # 输出文件夹 18 | if output_folder: 19 | command.extend(["--output", f'"{output_folder}"']) 20 | 21 | # 覆盖现有文件 22 | if overwrite: 23 | command.append("--overwrite") 24 | 25 | # 递归处理子文件夹 26 | if recursive: 27 | command.append("--recursive") 28 | 29 | # 文件格式选项 30 | if format: 31 | command.extend(["--format", format]) 32 | if quality is not None: 33 | command.extend(["--quality", str(quality)]) 34 | if compression is not None: 35 | command.extend(["--compression", str(compression)]) 36 | if bit_depth is not None: 37 | command.extend(["--bit-depth", str(bit_depth)]) 38 | if tiff_compression: 39 | command.extend(["--tiff-compression", tiff_compression]) 40 | 41 | # 调试选项 42 | if show_settings: 43 | command.append("--showSettings") 44 | if skip_processing: 45 | command.append("--skipProcessing") 46 | if verbose: 47 | command.append("--verbose") 48 | 49 | # 设置选项（实验性） 50 | if upscale is not None: 51 | command.extend(["--upscale", f"enabled={str(upscale).lower()}"]) 52 | if noise is not None: 53 | command.extend(["--noise", f"enabled={str(noise).lower()}"]) 54 | if sharpen is not None: 55 | command.extend(["--sharpen", f"enabled={str(sharpen).lower()}"]) 56 | if lighting is not None: 57 | command.extend(["--lighting", f"enabled={str(lighting).lower()}"]) 58 | if color is not None: 59 | command.extend(["--color", f"enabled={str(color).lower()}"]) 60 | 61 | # 打印并执行命令 62 | topazai_logger.info(str(" ".join(command))) 63 | result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) 64 | # 返回结果，并忽略无法解码的字符 65 | return result.stdout.decode(errors='ignore'), result.stderr.decode(errors='ignore'), result.returncode 66 | 67 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Sena 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Calling Various Online AI Drawing Website APIs With a Simple Gradio UI 2 | 3 | ## API Compatible with A1111webui API 4 | 5 | https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API 6 | 7 | ## 如果有需要适配的后端欢迎留下个issue,会进行适配. 8 | ### If you have any backends that need to be adapted, please feel free to leave an issue, and I will work on the adaptation. 9 | ## 之后项目会进行完善,我会写一些API调用教程. 10 | ### After the project is completed, I will write some API call tutorials. 11 | 12 | ## [中文说明](README_ZH.md) 13 | This project plans to implement i18n. (woking on it) 14 | ## ![封面](images/cover.png) 15 | checkpoint by/模型 https://huggingface.co/nyanko7/flux-dev-anime-cg 16 | 17 | ## Environment Requirements: Python 3.10 and Redis 18 | 19 | ## Features 20 | - A Simple Gradio GUI, You can change your model here(Running on FastAPI port + 1) 21 | ![UI](images/gradio.png) 22 | - Multi-backend load balancing 23 | ![LB](images/posting.png) 24 | - Automatic locking for backends that do not support concurrency 25 | - Built-in caption feature(https://github.com/toriato/stable-diffusion-webui-wd14-tagger), currently supporting natural language tagging (CUDA/8G VRAM) and wd tagger 26 | ![启动](images/caption.png) 27 | 28 | ## Supported Backends 29 | - https://github.com/AUTOMATIC1111/stable-diffusion-webui 30 | - https://civitai.com/ 31 | - https://fal.ai/models/fal-ai/flux/schnell 32 | - https://replicate.com/black-forest-labs/flux-schnell 33 | - https://www.liblib.art/ 34 | - https://tusiart.com/ 35 | - https://www.seaart.ai/ 36 | - https://www.yunjie.art/ 37 | - https://github.com/comfyanonymous/ComfyUI 38 | - https://novelai.net/image 39 | - https://github.com/novicezk/midjourney-proxy 40 | 41 | ### Deployment Guide (For Windows CMD) 42 | ### You need to deploy a Redis server on the server! Please install it yourself. 43 | 44 | Python 3.10 45 | ``` 46 | git clone https://github.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI 47 | cd Stable-Diffusion-DrawBridgeAPI 48 | ``` 49 | 50 | #### Install Dependencies 51 | ``` 52 | python -m venv venv 53 | .\venv\Scripts\python -m pip install -r .\requirements.txt 54 | ``` 55 | 56 | #### Modify Configuration File 57 | Copy `config_example.yaml` to `config.yaml`. 58 | [See Detailed Instructions](DrawBridgeAPI/config_example.yaml) 59 | #### How to select enable backend? 60 | ![See Detailed Instructions](images/how_to_enable_backend1.png) 61 | #### Start 62 | ![See Detailed Instructions](images/how_to_enable_backend2.png) 63 | ``` 64 | .\venv\Scripts\python.exe -m DrawBridgeAPI.api_server --port=8000 --host=127.0.0.1 --conf .\DrawBridgeAPI\config.yaml 65 | ``` 66 | 67 | #### Access 68 | Visit http://localhost:8000/docs# for help. 69 | 70 | #### Warning 71 | The API currently has no authentication; do not expose this API to the public, or it may be abused. 72 | 73 | Testing with CURL 74 | ``` 75 | curl -X POST -H "Content-Type: application/json" -d '{"prompt": "reimu", "width": 512, "height": 768}' http://localhost:8000/sdapi/v1/txt2img 76 | ``` 77 | ..\venv\Scripts\python api_server.py --port=8000 --host=127.0.0.1 78 | 79 | #### Optional Service 80 | Start the built-in tagging server. 81 | Set `server_settings - build_in_tagger` to true in the `config.yaml` file to start it, and install the dependencies. 82 | 83 | If the working directory is `Stable-Diffusion-DrawBridgeAPI\DrawBridgeAPI`: 84 | ``` 85 | ..\venv\Scripts\python -m pip install -r utils/tagger-requirements.txt 86 | ``` 87 | ## TODO 88 | - A simple frontend for easy drawing, tagging, and other operations. 89 | ## Changelog 90 | 91 | ### 2024-08-28 92 | Updated to use LLM for natural language image tagging, adapted from https://github.com/StartHua/Comfyui_CXH_joy_caption. 93 | Note: Requires GPU and 8G VRAM. 94 | ![vram](images/working.png) 95 | ```angular2html 96 | build_in_tagger: 97 | true 98 | llm_caption: # Use LLM for natural language tagging 99 | enable: 100 | true 101 | clip: 102 | google/siglip-so400m-patch14-384 103 | llm: 104 | unsloth/Meta-Llama-3.1-8B-bnb-4bit 105 | image_adapter: # https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/tree/main/wpkklhc6 106 | image_adapter.pt 107 | ``` 108 | Dependencies are in llm_caption_requirements.txt -------------------------------------------------------------------------------- /README_ZH.md: -------------------------------------------------------------------------------- 1 | # 调用各种在线AI绘图网站的API以及一个简易GUI 2 | 本项目计划进行i18n 3 | [[English]](README.md) 4 | 5 | ## 兼容A1111webui API的API 6 | https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API 7 | ## ![封面](images/cover.png) 8 | checkpoint by/模型 https://huggingface.co/nyanko7/flux-dev-anime-cg 9 | ## 环境要求 10 | Python 3.10 和 Redis 11 | 12 | ## 特性 13 | - Gradio 简易UI（端口号为FastAPI端口号+1），可以更换模型 14 | ![UI](images/gradio.png) 15 | - 多后端负载均衡 16 | ![LB](images/posting.png) 17 | - 不支持并发的后端自动上锁 18 | - 自带打标功能，现在支持自然语言打标（CUDA/8G VRAM）以及 wd tagger 19 | ![启动](images/caption.png) 20 | 21 | ## 已经适配的后端 22 | - https://github.com/AUTOMATIC1111/stable-diffusion-webui 23 | - https://civitai.com/ 24 | - https://fal.ai/models/fal-ai/flux/schnell 25 | - https://replicate.com/black-forest-labs/flux-schnell 26 | - https://www.liblib.art/ 27 | - https://tusiart.com/ 28 | - https://www.seaart.ai/ 29 | - https://www.yunjie.art/ 30 | - https://github.com/comfyanonymous/ComfyUI 31 | 32 | ## QQ群 575601916 33 | 34 | ### 部署教程（以下为Windows CMD） 35 | ### 需要在服务器上部署Redis服务器！请自行安装 36 | 37 | Python 3.10 38 | ``` 39 | git clone https://github.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI 40 | cd Stable-Diffusion-DrawBridgeAPI 41 | ``` 42 | 43 | #### 安装依赖 44 | ``` 45 | python -m venv venv 46 | .\venv\Scripts\python -m pip install -r .\requirements.txt 47 | ``` 48 | 49 | #### 更改配置文件 50 | 复制 `config_example.yaml` 为 `config.yaml` 51 | [查看详细说明](DrawBridgeAPI/config_example.yaml) 52 | 53 | #### 启动 54 | ``` 55 | .\venv\Scripts\python.exe -m DrawBridgeAPI.api_server --port=8000 --host=127.0.0.1 --conf .\DrawBridgeAPI\config.yaml 56 | ``` 57 | 58 | #### 访问 59 | 访问 http://localhost:8000/docs# 获取帮助 60 | 61 | #### 注意 62 | 目前API没有鉴权，请勿将此API暴露在公网，否则可能会被滥用。 63 | 64 | 使用CURL测试 65 | ``` 66 | curl -X POST -H "Content-Type: application/json" -d '{"prompt": "reimu", "width": 512, "height": 768}' http://localhost:8000/sdapi/v1/txt2img 67 | ``` 68 | 69 | #### 可选服务 70 | 启动服务器自带打标服务器。 71 | 修改 `config.yaml` 文件 `server_settings - build_in_tagger` 为 `true` 启动，安装依赖。 72 | 73 | 假如工作路径为 `Stable-Diffusion-DrawBridgeAPI\DrawBridgeAPI`: 74 | ``` 75 | ..\venv\Scripts\python -m pip install -r utils/tagger-requirements.txt 76 | ``` 77 | 78 | ### TODO 79 | 简单的前端，方便进行画图、打标等操作。 80 | 81 | ### 更新日志 82 | ### 2024-08-28 83 | 更新了使用LLM对图片进行自然语言打标，改自 https://github.com/StartHua/Comfyui_CXH_joy_caption 84 | 注意：需要GPU以及8G显存 85 | ![vram](images/working.png) 86 | ```yaml 87 | build_in_tagger: 88 | false 89 | llm_caption: # 使用LLM用自然语言打标 90 | enable: 91 | true 92 | clip: 93 | google/siglip-so400m-patch14-384 94 | llm: 95 | unsloth/Meta-Llama-3.1-8B-bnb-4bit 96 | image_adapter: # https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/tree/main/wpkklhc6 97 | image_adapter.pt 98 | ``` 99 | 依赖文件在 `llm_caption_requirements.txt` 100 | -------------------------------------------------------------------------------- /docs/API.md: -------------------------------------------------------------------------------- 1 | # API 特性 2 | -------------------------------------------------------------------------------- /images/caption.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/caption.png -------------------------------------------------------------------------------- /images/cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/cover.png -------------------------------------------------------------------------------- /images/gradio.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/gradio.png -------------------------------------------------------------------------------- /images/how_to_enable_backend1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/how_to_enable_backend1.png -------------------------------------------------------------------------------- /images/how_to_enable_backend2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/how_to_enable_backend2.png -------------------------------------------------------------------------------- /images/idle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/idle.png -------------------------------------------------------------------------------- /images/posting.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/posting.png -------------------------------------------------------------------------------- /images/working.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DiaoDaiaChan/Stable-Diffusion-DrawBridgeAPI/81f059531ee62d49fed089f80bfd77f8ecdf05ed/images/working.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "stable-diffusion-drawbridge-api" 3 | version = "1.4" 4 | description = "使用标准A1111-SDWEBUI API来调用各种绘画后端的多功能负载均衡API" 5 | authors = [ 6 | {name = "DiaoDaiaChan", email = "diaodaiachan@qq.com"}, 7 | ] 8 | dependencies = [ 9 | "aiohttp", 10 | "aiofiles", 11 | "Pillow>=10.4.0", 12 | "civitai-py", 13 | "civitai", 14 | "colorama", 15 | "fastapi", 16 | "fal-client", 17 | "httpx==0.27.0", 18 | "piexif", 19 | "pydantic", 20 | "PyYAML", 21 | "redis", 22 | "replicate", 23 | "tqdm", 24 | "uvicorn", 25 | "gradio" 26 | ] 27 | 28 | [tool.pdm] 29 | exclude = ["./DrawBridgeAPI/config.yaml", "./DrawBridgeAPI/*.log", "./DrawBridgeAPI/saved_images"] 30 | 31 | requires-python = ">=3.10" 32 | readme = "README.md" 33 | license = {text = "MIT"} 34 | 35 | [build-system] 36 | requires = ["pdm-backend"] 37 | build-backend = "pdm.backend" 38 | 39 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | aiofiles 2 | aiohttp 3 | Pillow 4 | civitai-py 5 | civitai 6 | colorama 7 | fastapi 8 | fal-client 9 | httpx 10 | httpx[http2] 11 | piexif 12 | pydantic 13 | PyYAML 14 | redis 15 | replicate 16 | 17 | tqdm 18 | uvicorn 19 | 20 | gradio>=4.0.0 --------------------------------------------------------------------------------