Add job argument to State.begin()

            p.outpath_grids = opts.outdir_txt2img_grids

            p.outpath_samples = opts.outdir_txt2img_samples

            shared.state.begin()

            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

            p.outpath_grids = opts.outdir_img2img_grids

            p.outpath_samples = opts.outdir_img2img_samples

            shared.state.begin()

            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

    def create_embedding(self, args: dict):

        try:

            shared.state.begin()

            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

            shared.state.end()

    def create_hypernetwork(self, args: dict):

        try:

            shared.state.begin()

            shared.state.begin(job="create_hypernetwork")

            filename = create_hypernetwork(**args) # create empty embedding

            shared.state.end()

            return models.CreateResponse(info=f"create hypernetwork filename: {filename}")

    def preprocess(self, args: dict):

        try:

            shared.state.begin()

            shared.state.begin(job="preprocess")

            preprocess(**args) # quick operation unless blip/booru interrogation is enabled

            shared.state.end()

            return models.PreprocessResponse(info = 'preprocess complete')

    def train_embedding(self, args: dict):

        try:

            shared.state.begin()

            shared.state.begin(job="train_embedding")

            apply_optimizations = shared.opts.training_xattention_optimizations

            error = None

            filename = ''

    def train_hypernetwork(self, args: dict):

        try:

            shared.state.begin()

            shared.state.begin(job="train_hypernetwork")

            shared.loaded_hypernetworks = []

            apply_optimizations = shared.opts.training_xattention_optimizations

            error = None

            id_task = None

        with queue_lock:

            shared.state.begin()

            shared.state.begin(job=id_task)

            progress.start_task(id_task)

            try:

def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):

    shared.state.begin()

    shared.state.job = 'model-merge'

    shared.state.begin(job="model-merge")

    def fail(message):

        shared.state.textinfo = message

    def interrogate(self, pil_image):

        res = ""

        shared.state.begin()

        shared.state.job = 'interrogate'

        shared.state.begin(job="interrogate")

        try:

            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

                lowvram.send_everything_to_cpu()

def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):

    devices.torch_gc()

    shared.state.begin()

    shared.state.job = 'extras'

    shared.state.begin(job="extras")

    image_data = []

    image_names = []