api.py

import base64
import io
import time
import datetime
import uvicorn
from threading import Lock
from io import BytesIO
from gradio.processing_utils import decode_base64_to_file
from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
from fastapi.security import HTTPBasic, HTTPBasicCredentials
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.models import *
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
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 List
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:
        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")

def script_name_to_index(name, scripts):
    try:
        return [script.title().lower() for script in scripts].index(name.lower())
    except:
        raise HTTPException(status_code=422, detail=f"Script '{name}' not found")

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 err:
        raise HTTPException(status_code=500, detail="Invalid encoded image")

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):
    @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


class Api:
    def __init__(self, app: FastAPI, queue_lock: Lock):
        if shared.cmd_opts.api_auth:
            self.credentials = dict()
            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=TextToImageResponse)
        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=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=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=FlagsModel)
        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=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=CreateResponse)
        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)

    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_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_script_args(self, request, selectable_scripts, selectable_idx, 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
        # 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
        else:
            # if 0 then none
            script_args[0] = 0

        # Now check for always on scripts
        if request.alwayson_script_name and (len(request.alwayson_script_name) > 0):
            # always on script with no arg should always run, but if you include their name in the api request, send an empty list for there args
            if not request.alwayson_script_args:
                raise HTTPException(status_code=422, detail=f"Script {request.alwayson_script_name} has no arg list")
            if len(request.alwayson_script_name) != len(request.alwayson_script_args):
                raise HTTPException(status_code=422, detail=f"Number of script names and number of script arg lists doesn't match")

            for alwayson_script_name, alwayson_script_args in zip(request.alwayson_script_name, request.alwayson_script_args):
                alwayson_script = self.get_script(alwayson_script_name, script_runner)
                if alwayson_script == 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 == False:
                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
                if alwayson_script_args != []:
                    script_args[alwayson_script.args_from:alwayson_script.args_to] = alwayson_script_args
        return script_args

    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
        script_runner = scripts.scripts_txt2img
        if not script_runner.scripts:
            script_runner.initialize_scripts(False)
            ui.create_ui()
        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": True,
            "do_not_save_grid": True
            }
        )

        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_script_name', None)
        args.pop('alwayson_script_args', None)

        script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner)

        with self.queue_lock:
            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
            p.scripts = script_runner

            shared.state.begin()
            if selectable_scripts != None:
                p.script_args = script_args
                p.outpath_grids = opts.outdir_txt2img_grids
                p.outpath_samples = opts.outdir_txt2img_samples
                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))

        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())

    def img2imgapi(self, img2imgreq: 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()
        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": True,
            "do_not_save_grid": True,
            "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_script_name', None)
        args.pop('alwayson_script_args', None)

        script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner)

        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

            shared.state.begin()
            if selectable_scripts != None:
                p.script_args = script_args
                p.outpath_grids = opts.outdir_img2img_grids
                p.outpath_samples = opts.outdir_img2img_samples
                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 not img2imgreq.include_init_images:
            img2imgreq.init_images = None
            img2imgreq.mask = None

        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())

    def extras_single_image_api(self, req: 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 ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])

    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
        reqDict = setUpscalers(req)

        def prepareFiles(file):
            file = decode_base64_to_file(file.data, file_path=file.name)
            file.orig_name = file.name
            return file

        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
        reqDict.pop('imageList')

        with self.queue_lock:
            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)

        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])

    def pnginfoapi(self, req: PNGInfoRequest):
        if(not req.image.strip()):
            return PNGInfoResponse(info="")

        image = decode_base64_to_image(req.image.strip())
        if image is None:
            return PNGInfoResponse(info="")

        geninfo, items = images.read_info_from_image(image)
        if geninfo is None:
            geninfo = ""

        items = {**{'parameters': geninfo}, **items}

        return PNGInfoResponse(info=geninfo, items=items)

    def progressapi(self, req: ProgressRequest = Depends()):
        # copy from check_progress_call of ui.py

        if shared.state.job_count == 0:
            return 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 ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)

    def interrogateapi(self, interrogatereq: 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 InterrogateResponse(caption=processed)

    def interruptapi(self):
        shared.state.interrupt()

        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 CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
        except AssertionError as e:
            shared.state.end()
            return TrainResponse(info = "create embedding error: {error}".format(error = e))

    def create_hypernetwork(self, args: dict):
        try:
            shared.state.begin()
            filename = create_hypernetwork(**args) # create empty embedding
            shared.state.end()
            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
        except AssertionError as e:
            shared.state.end()
            return TrainResponse(info = "create hypernetwork error: {error}".format(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 PreprocessResponse(info = 'preprocess complete')
        except KeyError as e:
            shared.state.end()
            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
        except AssertionError as e:
            shared.state.end()
            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
        except FileNotFoundError as e:
            shared.state.end()
            return PreprocessResponse(info = 'preprocess error: {error}'.format(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 TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
        except AssertionError as msg:
            shared.state.end()
            return TrainResponse(info = "train embedding error: {msg}".format(msg = 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 TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
        except AssertionError as msg:
            shared.state.end()
            return TrainResponse(info="train embedding error: {error}".format(error=error))

    def get_memory(self):
        try:
            import os, 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 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)