import base64 import io import time import datetime import uvicorn 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 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 checkpoints_list, unload_model_weights, reload_model_weights 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 def upscaler_to_index(name: str): try: return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) except Exception as e: raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e 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 decode_base64_to_image(encoding): 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"): 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 = True try: 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() except Exception: import traceback rich_available = False @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 print(f"API error: {request.method}: {request.url} {err}") if rich_available: console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200])) else: traceback.print_exc() 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/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) 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/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.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 = [str(title.lower()) for title in scripts.scripts_txt2img.titles] i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles] return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist) 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 and (len(request.alwayson_scripts) > 0): 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): 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) 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) with self.queue_lock: p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) p.scripts = script_runner p.outpath_grids = opts.outdir_txt2img_grids p.outpath_samples = opts.outdir_txt2img_samples shared.state.begin() 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) shared.state.end() 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): 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) 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) with self.queue_lock: p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) p.init_images = [decode_base64_to_image(x) for x in init_images] p.scripts = script_runner p.outpath_grids = opts.outdir_img2img_grids p.outpath_samples = opts.outdir_img2img_samples shared.state.begin() 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) shared.state.end() 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]): for k, v in req.items(): shared.opts.set(k, v) 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_sd_models(self): 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 checkpoints_list.values()] 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): shared.refresh_checkpoints() def create_embedding(self, args: dict): try: shared.state.begin() 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 shared.state.end() return models.CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: shared.state.end() return models.TrainResponse(info=f"create embedding error: {e}") def create_hypernetwork(self, args: dict): try: shared.state.begin() filename = create_hypernetwork(**args) # create empty embedding shared.state.end() return models.CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: shared.state.end() return models.TrainResponse(info=f"create hypernetwork error: {e}") def preprocess(self, args: dict): try: shared.state.begin() preprocess(**args) # quick operation unless blip/booru interrogation is enabled shared.state.end() return models.PreprocessResponse(info = 'preprocess complete') except KeyError as e: shared.state.end() return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}") except AssertionError as e: shared.state.end() return models.PreprocessResponse(info=f"preprocess error: {e}") except FileNotFoundError as e: shared.state.end() return models.PreprocessResponse(info=f'preprocess error: {e}') def train_embedding(self, args: dict): try: shared.state.begin() 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() shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") except AssertionError as msg: shared.state.end() return models.TrainResponse(info=f"train embedding error: {msg}") def train_hypernetwork(self, args: dict): try: shared.state.begin() 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 AssertionError: shared.state.end() return models.TrainResponse(info=f"train embedding error: {error}") 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): self.app.include_router(self.router) uvicorn.run(self.app, host=server_name, port=port)