import base64 import io import os import time import datetime import uvicorn import ipaddress import requests import gradio as gr from threading import Lock from io import BytesIO from fastapi import APIRouter, Depends, FastAPI, Request, Response from fastapi.security import HTTPBasic, HTTPBasicCredentials from fastapi.exceptions import HTTPException from fastapi.responses import JSONResponse from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items from modules.api import models from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.textual_inversion.textual_inversion import create_embedding, train_embedding from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases from modules.sd_models_config import find_checkpoint_config_near_filename from modules.realesrgan_model import get_realesrgan_models from modules import devices from typing import Dict, List, Any import piexif import piexif.helper from contextlib import closing ''' Eris Bot API modifier Eris Promptlog: Every Prompt that is send from through the API will be saved in a Textfile, with the current date as name, in a folder named "logs" Eris Imagelog: Every generated Images will be saved in the default "outputs" folder Eris Consolelog: Every Prompt that is send from through the API will be displayed in the console/terminal window Eris TRTpatch: Necessary modification for the TRT Plugin to work. Please refer the Readme on https://git.foxo.me/Hitmare/Eris_api_tensor_patch for the full TRTpatch instructions Eris Imagelimit: Will resize every request down to 1024px max on height and width, keeping the aspect ratio. Also reduces Steps down to 35 if the set Steps exceeds 35 Compatible with the TRTpatch. Please refer the Readme on https://git.foxo.me/Hitmare/Eris_api_tensor_patch for the full TRTpatch instructions To activate any modifications, change the "False" to "True". The capital "T" is important for example: eris_imagelog = True ''' # Eris modifier switches eris_promtlog = False eris_imagelog = False eris_consolelog = False eris_TRTpatch = False eris_imagelimit = False def script_name_to_index(name, scripts): try: return [script.title().lower() for script in scripts].index(name.lower()) except Exception as e: raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e def validate_sampler_name(name): config = sd_samplers.all_samplers_map.get(name, None) if config is None: raise HTTPException(status_code=404, detail="Sampler not found") return name def setUpscalers(req: dict): reqDict = vars(req) reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None) reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None) return reqDict def verify_url(url): """Returns True if the url refers to a global resource.""" import socket from urllib.parse import urlparse try: parsed_url = urlparse(url) domain_name = parsed_url.netloc host = socket.gethostbyname_ex(domain_name) for ip in host[2]: ip_addr = ipaddress.ip_address(ip) if not ip_addr.is_global: return False except Exception: return False return True def decode_base64_to_image(encoding): if encoding.startswith("http://") or encoding.startswith("https://"): if not opts.api_enable_requests: raise HTTPException(status_code=500, detail="Requests not allowed") if opts.api_forbid_local_requests and not verify_url(encoding): raise HTTPException(status_code=500, detail="Request to local resource not allowed") headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {} response = requests.get(encoding, timeout=30, headers=headers) try: image = Image.open(BytesIO(response.content)) return image except Exception as e: raise HTTPException(status_code=500, detail="Invalid image url") from e if encoding.startswith("data:image/"): encoding = encoding.split(";")[1].split(",")[1] try: image = Image.open(BytesIO(base64.b64decode(encoding))) return image except Exception as e: raise HTTPException(status_code=500, detail="Invalid encoded image") from e def encode_pil_to_base64(image): with io.BytesIO() as output_bytes: if opts.samples_format.lower() == 'png': use_metadata = False metadata = PngImagePlugin.PngInfo() for key, value in image.info.items(): if isinstance(key, str) and isinstance(value, str): metadata.add_text(key, value) use_metadata = True image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): if image.mode == "RGBA": image = image.convert("RGB") parameters = image.info.get('parameters', None) exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } }) if opts.samples_format.lower() in ("jpg", "jpeg"): image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality) else: image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality) else: raise HTTPException(status_code=500, detail="Invalid image format") bytes_data = output_bytes.getvalue() return base64.b64encode(bytes_data) def api_middleware(app: FastAPI): rich_available = False try: if os.environ.get('WEBUI_RICH_EXCEPTIONS', None) is not None: import anyio # importing just so it can be placed on silent list import starlette # importing just so it can be placed on silent list from rich.console import Console console = Console() rich_available = True except Exception: pass @app.middleware("http") async def log_and_time(req: Request, call_next): ts = time.time() res: Response = await call_next(req) duration = str(round(time.time() - ts, 4)) res.headers["X-Process-Time"] = duration endpoint = req.scope.get('path', 'err') if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'): print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format( t=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), code=res.status_code, ver=req.scope.get('http_version', '0.0'), cli=req.scope.get('client', ('0:0.0.0', 0))[0], prot=req.scope.get('scheme', 'err'), method=req.scope.get('method', 'err'), endpoint=endpoint, duration=duration, )) return res def handle_exception(request: Request, e: Exception): err = { "error": type(e).__name__, "detail": vars(e).get('detail', ''), "body": vars(e).get('body', ''), "errors": str(e), } if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions message = f"API error: {request.method}: {request.url} {err}" if rich_available: print(message) console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200])) else: errors.report(message, exc_info=True) return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err)) @app.middleware("http") async def exception_handling(request: Request, call_next): try: return await call_next(request) except Exception as e: return handle_exception(request, e) @app.exception_handler(Exception) async def fastapi_exception_handler(request: Request, e: Exception): return handle_exception(request, e) @app.exception_handler(HTTPException) async def http_exception_handler(request: Request, e: HTTPException): return handle_exception(request, e) class Api: def __init__(self, app: FastAPI, queue_lock: Lock): if shared.cmd_opts.api_auth: self.credentials = {} for auth in shared.cmd_opts.api_auth.split(","): user, password = auth.split(":") self.credentials[user] = password self.router = APIRouter() self.app = app self.queue_lock = queue_lock api_middleware(self.app) self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse) self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse) self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse) self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse) self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse) self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse) self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"]) self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"]) self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel) self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel) self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem]) self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem]) self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem]) self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem]) self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem]) self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem]) self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem]) self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem]) self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse) self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse) self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo]) if shared.cmd_opts.api_server_stop: self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"]) self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"]) self.default_script_arg_txt2img = [] self.default_script_arg_img2img = [] def add_api_route(self, path: str, endpoint, **kwargs): if shared.cmd_opts.api_auth: return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) return self.app.add_api_route(path, endpoint, **kwargs) def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())): if credentials.username in self.credentials: if compare_digest(credentials.password, self.credentials[credentials.username]): return True raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) def get_selectable_script(self, script_name, script_runner): if script_name is None or script_name == "": return None, None script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) script = script_runner.selectable_scripts[script_idx] return script, script_idx def get_scripts_list(self): t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None] i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None] return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist) def get_script_info(self): res = [] for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]: res += [script.api_info for script in script_list if script.api_info is not None] return res def get_script(self, script_name, script_runner): if script_name is None or script_name == "": return None, None script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] def init_default_script_args(self, script_runner): #find max idx from the scripts in runner and generate a none array to init script_args last_arg_index = 1 for script in script_runner.scripts: if last_arg_index < script.args_to: last_arg_index = script.args_to # None everywhere except position 0 to initialize script args script_args = [None]*last_arg_index script_args[0] = 0 # get default values with gr.Blocks(): # will throw errors calling ui function without this for script in script_runner.scripts: if script.ui(script.is_img2img): ui_default_values = [] for elem in script.ui(script.is_img2img): ui_default_values.append(elem.value) script_args[script.args_from:script.args_to] = ui_default_values return script_args def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner): script_args = default_script_args.copy() # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run() if selectable_scripts: script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args script_args[0] = selectable_idx + 1 # Now check for always on scripts if request.alwayson_scripts: for alwayson_script_name in request.alwayson_scripts.keys(): alwayson_script = self.get_script(alwayson_script_name, script_runner) if alwayson_script is None: raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") # Selectable script in always on script param check if alwayson_script.alwayson is False: raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params") # always on script with no arg should always run so you don't really need to add them to the requests if "args" in request.alwayson_scripts[alwayson_script_name]: # min between arg length in scriptrunner and arg length in the request for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))): script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] return script_args def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI): # Eris TRTpacht if eris_TRTpatch: txt2imgreq.width = round(txt2imgreq.width / 64) * 64 txt2imgreq.height = round(txt2imgreq.height / 64) * 64 # Eris ______ # Eris imagelimit 1024x1024 35 Steps if eris_imagelimit: # Add a check to only resize if the dimensions are larger than 1024 if txt2imgreq.width > 1024 or txt2imgreq.height > 1024: # Calculate the aspect ratio aspect_ratio = txt2imgreq.width / txt2imgreq.height # Scale down to the maximum allowed dimensions while retaining the aspect ratio if aspect_ratio > 1: # If the image is wider than it is tall txt2imgreq.width = 1024 txt2imgreq.height = round(1024 / aspect_ratio) else: # If the image is as wide as it is tall or taller txt2imgreq.height = 1024 txt2imgreq.width = round(1024 * aspect_ratio) # Ensure both dimensions are a multiple of 64 (if this is a requirement for your use case) txt2imgreq.width = round(txt2imgreq.width // 64) * 64 txt2imgreq.height = round(txt2imgreq.height // 64) * 64 # Reduce maximum steps txt2imgreq.steps = min(txt2imgreq.steps, 35) # Eris ______ # Eris console prompt log -> writes promts into the console/terminal window if eris_consolelog: print('[t2i]', txt2imgreq.width, 'x', txt2imgreq.height, '|', txt2imgreq.prompt) # Eris ______ script_runner = scripts.scripts_txt2img if not script_runner.scripts: script_runner.initialize_scripts(False) ui.create_ui() if not self.default_script_arg_txt2img: self.default_script_arg_txt2img = self.init_default_script_args(script_runner) selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) # Eris save generated images -> will be saved in default outputs folder if eris_imagelog: txt2imgreq.save_images = True # Eris ______ populate = txt2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), "do_not_save_samples": not txt2imgreq.save_images, "do_not_save_grid": not txt2imgreq.save_images, }) if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) args.pop('script_name', None) args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner) send_images = args.pop('send_images', True) args.pop('save_images', None) # Eris Promtlog -> writing daily log file for txt2img into logs folder if eris_imagelog: logfolder = "logs" if not os.path.exists(logfolder): os.makedirs(logfolder) apilogtxt2imgfile = open(f"{logfolder}/{datetime.date.today()}-txt2img.txt", "a") apilogtxt2imgtext = f"[{datetime.datetime.now()}] Prompt: {txt2imgreq.prompt} | Negative prompt: {txt2imgreq.negative_prompt} | Steps: {txt2imgreq.steps} | Size: {txt2imgreq.width}x{txt2imgreq.height} | CFG: {txt2imgreq.cfg_scale}" #replace newlines and returns to keep the prompt in one line apilogtxt2imgtext.replace("\n", " ").replace("\r", " ") apilogtxt2imgfile.write(f"{apilogtxt2imgtext}\n") # Eris ______ with self.queue_lock: with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: p.is_api = True p.scripts = script_runner p.outpath_grids = opts.outdir_txt2img_grids p.outpath_samples = opts.outdir_txt2img_samples try: shared.state.begin(job="scripts_txt2img") if selectable_scripts is not None: p.script_args = script_args processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here else: p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) finally: shared.state.end() shared.total_tqdm.clear() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI): # Eris TRTpatch if eris_TRTpatch: img2imgreq.width = round(img2imgreq.width / 64) * 64 img2imgreq.height = round(img2imgreq.height / 64) * 64 # Eris ______ # Eris imagelimit 1024x1024 35 Steps if eris_imagelimit: # Add a check to only resize if the dimensions are larger than 1024 if img2imgreq.width > 1024 or img2imgreq.height > 1024: # Calculate the aspect ratio of the input image aspect_ratio = img2imgreq.width / img2imgreq.height # Scale down to the maximum allowed dimensions while retaining the aspect ratio if aspect_ratio > 1: # If the image is wider than it is tall img2imgreq.width = 1024 img2imgreq.height = round(1024 / aspect_ratio) else: # If the image is as wide as it is tall or taller img2imgreq.height = 1024 img2imgreq.width = round(1024 * aspect_ratio) # Ensure both dimensions are a multiple of 64 (if this is a requirement for your use case) img2imgreq.width = round(img2imgreq.width / 64) * 64 img2imgreq.height = round(img2imgreq.height / 64) * 64 # Reduce maximum steps img2imgreq.steps = min(img2imgreq.steps, 35) # Eris ______ # Eris console prompt log -> writes promts into the console/terminal window if eris_consolelog: print('[i2i]', img2imgreq.width, 'x', img2imgreq.height, '|', img2imgreq.prompt) # Eris ______ init_images = img2imgreq.init_images if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") mask = img2imgreq.mask if mask: mask = decode_base64_to_image(mask) script_runner = scripts.scripts_img2img if not script_runner.scripts: script_runner.initialize_scripts(True) ui.create_ui() if not self.default_script_arg_img2img: self.default_script_arg_img2img = self.init_default_script_args(script_runner) selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) # Eris save generated images -> will be saved in default outputs folder if eris_imagelog: img2imgreq.save_images = True # Eris ______ populate = img2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), "do_not_save_samples": not img2imgreq.save_images, "do_not_save_grid": not img2imgreq.save_images, "mask": mask, }) if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. args.pop('script_name', None) args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner) send_images = args.pop('send_images', True) args.pop('save_images', None) # Eris Promtlog -> writing daily log file for txt2img into logs folder if eris_promtlog: logfolder = "logs" if not os.path.exists(logfolder): os.makedirs(logfolder) apilogimg2imgfile = open(f"{logfolder}/{datetime.date.today()}-img2img.txt", "a") apilogimg2imgtext = f"[{datetime.datetime.now()}] Prompt: {img2imgreq.prompt} | Negative prompt: {img2imgreq.negative_prompt} | Steps: {img2imgreq.steps} | Size: {img2imgreq.width}x{img2imgreq.height} | CFG: {img2imgreq.cfg_scale} | Denoising: {img2imgreq.denoising_strength}" #replace newlines and returns to keep the prompt in one line apilogimg2imgtext.replace("\n", " ").replace("\r", " ") apilogimg2imgfile.write(f"{apilogimg2imgtext}\n") # Eris ______ with self.queue_lock: with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: p.init_images = [decode_base64_to_image(x) for x in init_images] p.is_api = True p.scripts = script_runner p.outpath_grids = opts.outdir_img2img_grids p.outpath_samples = opts.outdir_img2img_samples try: shared.state.begin(job="scripts_img2img") if selectable_scripts is not None: p.script_args = script_args processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here else: p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) finally: shared.state.end() shared.total_tqdm.clear() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] if not img2imgreq.include_init_images: img2imgreq.init_images = None img2imgreq.mask = None return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) def extras_single_image_api(self, req: models.ExtrasSingleImageRequest): reqDict = setUpscalers(req) reqDict['image'] = decode_base64_to_image(reqDict['image']) with self.queue_lock: result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict) return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest): reqDict = setUpscalers(req) image_list = reqDict.pop('imageList', []) image_folder = [decode_base64_to_image(x.data) for x in image_list] with self.queue_lock: result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) def pnginfoapi(self, req: models.PNGInfoRequest): if(not req.image.strip()): return models.PNGInfoResponse(info="") image = decode_base64_to_image(req.image.strip()) if image is None: return models.PNGInfoResponse(info="") geninfo, items = images.read_info_from_image(image) if geninfo is None: geninfo = "" items = {**{'parameters': geninfo}, **items} return models.PNGInfoResponse(info=geninfo, items=items) def progressapi(self, req: models.ProgressRequest = Depends()): # copy from check_progress_call of ui.py if shared.state.job_count == 0: return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo) # avoid dividing zero progress = 0.01 if shared.state.job_count > 0: progress += shared.state.job_no / shared.state.job_count if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start progress = min(progress, 1) shared.state.set_current_image() current_image = None if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo) def interrogateapi(self, interrogatereq: models.InterrogateRequest): image_b64 = interrogatereq.image if image_b64 is None: raise HTTPException(status_code=404, detail="Image not found") img = decode_base64_to_image(image_b64) img = img.convert('RGB') # Override object param with self.queue_lock: if interrogatereq.model == "clip": processed = shared.interrogator.interrogate(img) elif interrogatereq.model == "deepdanbooru": processed = deepbooru.model.tag(img) else: raise HTTPException(status_code=404, detail="Model not found") return models.InterrogateResponse(caption=processed) def interruptapi(self): shared.state.interrupt() return {} def unloadapi(self): unload_model_weights() return {} def reloadapi(self): reload_model_weights() return {} def skip(self): shared.state.skip() def get_config(self): options = {} for key in shared.opts.data.keys(): metadata = shared.opts.data_labels.get(key) if(metadata is not None): options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) else: options.update({key: shared.opts.data.get(key, None)}) return options def set_config(self, req: Dict[str, Any]): checkpoint_name = req.get("sd_model_checkpoint", None) if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases: raise RuntimeError(f"model {checkpoint_name!r} not found") for k, v in req.items(): shared.opts.set(k, v, is_api=True) shared.opts.save(shared.config_filename) return def get_cmd_flags(self): return vars(shared.cmd_opts) def get_samplers(self): return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers] def get_upscalers(self): return [ { "name": upscaler.name, "model_name": upscaler.scaler.model_name, "model_path": upscaler.data_path, "model_url": None, "scale": upscaler.scale, } for upscaler in shared.sd_upscalers ] def get_latent_upscale_modes(self): return [ { "name": upscale_mode, } for upscale_mode in [*(shared.latent_upscale_modes or {})] ] def get_sd_models(self): import modules.sd_models as sd_models return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in sd_models.checkpoints_list.values()] def get_sd_vaes(self): import modules.sd_vae as sd_vae return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()] def get_hypernetworks(self): return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] def get_face_restorers(self): return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] def get_realesrgan_models(self): return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] def get_prompt_styles(self): styleList = [] for k in shared.prompt_styles.styles: style = shared.prompt_styles.styles[k] styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]}) return styleList def get_embeddings(self): db = sd_hijack.model_hijack.embedding_db def convert_embedding(embedding): return { "step": embedding.step, "sd_checkpoint": embedding.sd_checkpoint, "sd_checkpoint_name": embedding.sd_checkpoint_name, "shape": embedding.shape, "vectors": embedding.vectors, } def convert_embeddings(embeddings): return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()} return { "loaded": convert_embeddings(db.word_embeddings), "skipped": convert_embeddings(db.skipped_embeddings), } def refresh_checkpoints(self): with self.queue_lock: shared.refresh_checkpoints() def refresh_vae(self): with self.queue_lock: shared_items.refresh_vae_list() def create_embedding(self, args: dict): try: shared.state.begin(job="create_embedding") filename = create_embedding(**args) # create empty embedding sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used return models.CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: return models.TrainResponse(info=f"create embedding error: {e}") finally: shared.state.end() def create_hypernetwork(self, args: dict): try: shared.state.begin(job="create_hypernetwork") filename = create_hypernetwork(**args) # create empty embedding return models.CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: return models.TrainResponse(info=f"create hypernetwork error: {e}") finally: shared.state.end() def preprocess(self, args: dict): try: shared.state.begin(job="preprocess") preprocess(**args) # quick operation unless blip/booru interrogation is enabled shared.state.end() return models.PreprocessResponse(info='preprocess complete') except KeyError as e: return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}") except Exception as e: return models.PreprocessResponse(info=f"preprocess error: {e}") finally: shared.state.end() def train_embedding(self, args: dict): try: shared.state.begin(job="train_embedding") apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' if not apply_optimizations: sd_hijack.undo_optimizations() try: embedding, filename = train_embedding(**args) # can take a long time to complete except Exception as e: error = e finally: if not apply_optimizations: sd_hijack.apply_optimizations() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") except Exception as msg: return models.TrainResponse(info=f"train embedding error: {msg}") finally: shared.state.end() def train_hypernetwork(self, args: dict): try: shared.state.begin(job="train_hypernetwork") shared.loaded_hypernetworks = [] apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' if not apply_optimizations: sd_hijack.undo_optimizations() try: hypernetwork, filename = train_hypernetwork(**args) except Exception as e: error = e finally: shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) if not apply_optimizations: sd_hijack.apply_optimizations() shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") except Exception as exc: return models.TrainResponse(info=f"train embedding error: {exc}") finally: shared.state.end() def get_memory(self): try: import os import psutil process = psutil.Process(os.getpid()) res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe ram = { 'free': ram_total - res.rss, 'used': res.rss, 'total': ram_total } except Exception as err: ram = { 'error': f'{err}' } try: import torch if torch.cuda.is_available(): s = torch.cuda.mem_get_info() system = { 'free': s[0], 'used': s[1] - s[0], 'total': s[1] } s = dict(torch.cuda.memory_stats(shared.device)) allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] } reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] } active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] } inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] } warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] } cuda = { 'system': system, 'active': active, 'allocated': allocated, 'reserved': reserved, 'inactive': inactive, 'events': warnings, } else: cuda = {'error': 'unavailable'} except Exception as err: cuda = {'error': f'{err}'} return models.MemoryResponse(ram=ram, cuda=cuda) def launch(self, server_name, port, root_path): self.app.include_router(self.router) uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path) def kill_webui(self): restart.stop_program() def restart_webui(self): if restart.is_restartable(): restart.restart_program() return Response(status_code=501) def stop_webui(request): shared.state.server_command = "stop" return Response("Stopping.")