d26e48afae727a16149600d9a8cdb5607e526b5ef6a156db24a34aad6e706740

modules/api/api.py

@@ -0,0 +1,742 @@
+import base64
+import io
+import os
+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, errors, restart
+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, checkpoint_aliases
+from modules.sd_vae import vae_dict
+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
+
+
+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"):
+            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/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):
+        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:
+            with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
+                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()
+
+        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:
+            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.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()
+
+        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)
+
+        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):
+        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_sd_vaes(self):
+        return [{"model_name": x, "filename": vae_dict[x]} for x in 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 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.")
+

modules/api/models.py

@@ -0,0 +1,314 @@
+import inspect
+
+from pydantic import BaseModel, Field, create_model
+from typing import Any, Optional
+from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
+from modules.shared import sd_upscalers, opts, parser
+from typing import Dict, List
+
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    # "do_not_save_samples",
+    # "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+    field_exclude: bool = False
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+
+            return Optional[field_type]
+
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+
+
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]),
+                field_alias=fields["key"],
+                field_type=fields["type"],
+                field_value=fields["default"],
+                field_exclude=fields["exclude"] if "exclude" in fields else False))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img",
+    StableDiffusionProcessingTxt2Img,
+    [
+        {"key": "sampler_index", "type": str, "default": "Euler"},
+        {"key": "script_name", "type": str, "default": None},
+        {"key": "script_args", "type": list, "default": []},
+        {"key": "send_images", "type": bool, "default": True},
+        {"key": "save_images", "type": bool, "default": False},
+        {"key": "alwayson_scripts", "type": dict, "default": {}},
+    ]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img",
+    StableDiffusionProcessingImg2Img,
+    [
+        {"key": "sampler_index", "type": str, "default": "Euler"},
+        {"key": "init_images", "type": list, "default": None},
+        {"key": "denoising_strength", "type": float, "default": 0.75},
+        {"key": "mask", "type": str, "default": None},
+        {"key": "include_init_images", "type": bool, "default": False, "exclude" : True},
+        {"key": "script_name", "type": str, "default": None},
+        {"key": "script_args", "type": list, "default": []},
+        {"key": "send_images", "type": bool, "default": True},
+        {"key": "save_images", "type": bool, "default": False},
+        {"key": "alwayson_scripts", "type": dict, "default": {}},
+    ]
+).generate_model()
+
+class TextToImageResponse(BaseModel):
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
+    info: str
+
+class ImageToImageResponse(BaseModel):
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
+    info: str
+
+class ExtrasBaseRequest(BaseModel):
+    resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+    show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+    gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+    codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+    codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
+    upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
+
+class ExtraBaseResponse(BaseModel):
+    html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class FileData(BaseModel):
+    data: str = Field(title="File data", description="Base64 representation of the file")
+    name: str = Field(title="File name")
+
+class ExtrasBatchImagesRequest(ExtrasBaseRequest):
+    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class PNGInfoRequest(BaseModel):
+    image: str = Field(title="Image", description="The base64 encoded PNG image")
+
+class PNGInfoResponse(BaseModel):
+    info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
+    items: dict = Field(title="Items", description="An object containing all the info the image had")
+
+class ProgressRequest(BaseModel):
+    skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
+
+class ProgressResponse(BaseModel):
+    progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
+    eta_relative: float = Field(title="ETA in secs")
+    state: dict = Field(title="State", description="The current state snapshot")
+    current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+    textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
+
+class InterrogateRequest(BaseModel):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
+
+class InterrogateResponse(BaseModel):
+    caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
+
+class TrainResponse(BaseModel):
+    info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
+
+class CreateResponse(BaseModel):
+    info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
+
+class PreprocessResponse(BaseModel):
+    info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
+
+fields = {}
+for key, metadata in opts.data_labels.items():
+    value = opts.data.get(key)
+    optType = opts.typemap.get(type(metadata.default), type(metadata.default))
+
+    if metadata.default is None:
+        pass
+    elif metadata is not None:
+        fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+    if(_options[key].dest != 'help'):
+        flag = _options[key]
+        _type = str
+        if _options[key].default is not None:
+            _type = type(_options[key].default)
+        flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+    name: str = Field(title="Name")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+    name: str = Field(title="Name")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
+    scale: Optional[float] = Field(title="Scale")
+
+class LatentUpscalerModeItem(BaseModel):
+    name: str = Field(title="Name")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: Optional[str] = Field(title="Short hash")
+    sha256: Optional[str] = Field(title="sha256 hash")
+    filename: str = Field(title="Filename")
+    config: Optional[str] = Field(title="Config file")
+
+class SDVaeItem(BaseModel):
+    model_name: str = Field(title="Model Name")
+    filename: str = Field(title="Filename")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: Optional[str] = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+    name: str = Field(title="Name")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
+
+
+class EmbeddingItem(BaseModel):
+    step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
+    sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
+    sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
+    shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
+    vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
+
+class EmbeddingsResponse(BaseModel):
+    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
+
+class MemoryResponse(BaseModel):
+    ram: dict = Field(title="RAM", description="System memory stats")
+    cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
+
+
+class ScriptsList(BaseModel):
+    txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
+    img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
+
+
+class ScriptArg(BaseModel):
+    label: str = Field(default=None, title="Label", description="Name of the argument in UI")
+    value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
+    minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
+    maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
+    step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
+    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+
+
+class ScriptInfo(BaseModel):
+    name: str = Field(default=None, title="Name", description="Script name")
+    is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
+    is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
+    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")

modules/cache.py

@@ -0,0 +1,120 @@
+import json
+import os.path
+import threading
+import time
+
+from modules.paths import data_path, script_path
+
+cache_filename = os.path.join(data_path, "cache.json")
+cache_data = None
+cache_lock = threading.Lock()
+
+dump_cache_after = None
+dump_cache_thread = None
+
+
+def dump_cache():
+    """
+    Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written.
+    """
+
+    global dump_cache_after
+    global dump_cache_thread
+
+    def thread_func():
+        global dump_cache_after
+        global dump_cache_thread
+
+        while dump_cache_after is not None and time.time() < dump_cache_after:
+            time.sleep(1)
+
+        with cache_lock:
+            with open(cache_filename, "w", encoding="utf8") as file:
+                json.dump(cache_data, file, indent=4)
+
+            dump_cache_after = None
+            dump_cache_thread = None
+
+    with cache_lock:
+        dump_cache_after = time.time() + 5
+        if dump_cache_thread is None:
+            dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func)
+            dump_cache_thread.start()
+
+
+def cache(subsection):
+    """
+    Retrieves or initializes a cache for a specific subsection.
+
+    Parameters:
+        subsection (str): The subsection identifier for the cache.
+
+    Returns:
+        dict: The cache data for the specified subsection.
+    """
+
+    global cache_data
+
+    if cache_data is None:
+        with cache_lock:
+            if cache_data is None:
+                if not os.path.isfile(cache_filename):
+                    cache_data = {}
+                else:
+                    try:
+                        with open(cache_filename, "r", encoding="utf8") as file:
+                            cache_data = json.load(file)
+                    except Exception:
+                        os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
+                        print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
+                        cache_data = {}
+
+    s = cache_data.get(subsection, {})
+    cache_data[subsection] = s
+
+    return s
+
+
+def cached_data_for_file(subsection, title, filename, func):
+    """
+    Retrieves or generates data for a specific file, using a caching mechanism.
+
+    Parameters:
+        subsection (str): The subsection of the cache to use.
+        title (str): The title of the data entry in the subsection of the cache.
+        filename (str): The path to the file to be checked for modifications.
+        func (callable): A function that generates the data if it is not available in the cache.
+
+    Returns:
+        dict or None: The cached or generated data, or None if data generation fails.
+
+    The `cached_data_for_file` function implements a caching mechanism for data stored in files.
+    It checks if the data associated with the given `title` is present in the cache and compares the
+    modification time of the file with the cached modification time. If the file has been modified,
+    the cache is considered invalid and the data is regenerated using the provided `func`.
+    Otherwise, the cached data is returned.
+
+    If the data generation fails, None is returned to indicate the failure. Otherwise, the generated
+    or cached data is returned as a dictionary.
+    """
+
+    existing_cache = cache(subsection)
+    ondisk_mtime = os.path.getmtime(filename)
+
+    entry = existing_cache.get(title)
+    if entry:
+        cached_mtime = entry.get("mtime", 0)
+        if ondisk_mtime > cached_mtime:
+            entry = None
+
+    if not entry or 'value' not in entry:
+        value = func()
+        if value is None:
+            return None
+
+        entry = {'mtime': ondisk_mtime, 'value': value}
+        existing_cache[title] = entry
+
+        dump_cache()
+
+    return entry['value']