smartbotic/nodes/sdcpp/sdcpp-model-load.js

/**
 * @node sdcpp-model-load
 * @name SD.cpp Load Model
 * @category sdcpp
 * @version 1.0.0
 * @description Make sure a model is loaded, without reloading one that already is
 * @icon box
 */

// The credential this node wants, named so it can be found. It is stored as a
// plain basic credential - that is what decides how it is encrypted - and this
// only says which basic credential is the SD.cpp one. Anything that accepts a
// basic credential still accepts this, and this node still accepts a plain
// basic credential, because the shape is identical.
const credentialTypes = [
    {
        id: 'sdcpp',
        label: 'SD.cpp Server',
        baseType: 'basic',
        description: 'The username and password you sign in to sdcpp-restapi with. The node exchanges them for a token before every call',
        usernameLabel: 'Username',
        passwordLabel: 'Password'
    }
];

const configSchema = {
    type: 'object',
    uiGroups: [
        { title: 'Connection', fields: ['serverUrl', 'credentialId'] },
        { title: 'Model', fields: ['modelName', 'modelType', 'whenDifferent', 'force'] },
        { title: 'Components', fields: ['vae', 'clipL', 'clipG', 't5xxl', 'llm', 'taesd', 'controlnet'] },
        { title: 'Loading', fields: ['flashAttn', 'diffusionFlashAttn', 'enableMmap', 'eagerLoad',
                                     'streamLayers', 'maxVram', 'nThreads', 'weightType'] },
        { title: 'Advanced', fields: ['vaeFormat', 'prediction', 'rngType', 'samplerRngType',
                                      'loraApplyMode', 'vaeConvDirect', 'diffusionConvDirect',
                                      'taePreviewOnly', 'forceSdxlVaeConvScale', 'backend', 'paramsBackend', 'rpcServers',
                                      'modelArgs', 'tensorTypeRules', 'options', 'timeout'] }
    ],
    // Pressing this asks the server what it has loaded and writes it into the
    // settings below - the model and every component it reports - so a workflow
    // can be built from a server that is already set up the way it should be,
    // rather than by typing the same names again.
    prefill: {
        label: 'Take from the server',
        description: 'Fill these in from the model the server currently has loaded',
        node: 'sdcpp-health',
        needs: ['serverUrl'],
        map: {
            modelName: 'modelName',
            modelType: 'modelType',
            'loadedComponents.vae': 'vae',
            'loadedComponents.clip_l': 'clipL',
            'loadedComponents.clip_g': 'clipG',
            'loadedComponents.t5xxl': 't5xxl',
            'loadedComponents.llm': 'llm',
            'loadedComponents.taesd': 'taesd',
            'loadedComponents.controlnet': 'controlnet',
            'loadOptions.flash_attn': 'flashAttn',
            'loadOptions.diffusion_flash_attn': 'diffusionFlashAttn',
            'loadOptions.enable_mmap': 'enableMmap',
            'loadOptions.eager_load': 'eagerLoad',
            'loadOptions.stream_layers': 'streamLayers',
            'loadOptions.max_vram': 'maxVram',
            'loadOptions.n_threads': 'nThreads',
            'loadOptions.vae_format': 'vaeFormat',
            'loadOptions.rng_type': 'rngType',
            'loadOptions.lora_apply_mode': 'loraApplyMode',
            'loadOptions.vae_conv_direct': 'vaeConvDirect',
            'loadOptions.diffusion_conv_direct': 'diffusionConvDirect',
            'loadOptions.tae_preview_only': 'taePreviewOnly',
            'loadOptions.force_sdxl_vae_conv_scale': 'forceSdxlVaeConvScale',
            'loadOptions.rng_type': 'rngType',
            'loadOptions.lora_apply_mode': 'loraApplyMode',
            'loadOptions.backend': 'backend',
            'loadOptions.rpc_servers': 'rpcServers',
            'loadOptions.model_args': 'modelArgs',
            'loadOptions.backend': 'backend',
            'loadOptions.params_backend': 'paramsBackend',
            'loadOptions.rpc_servers': 'rpcServers',
            'loadOptions.model_args': 'modelArgs'
        }
    },
    properties: {
        serverUrl: {
            type: 'string', title: 'Server URL',
            description: 'Base address of the sdcpp-restapi server',
            default: 'http://localhost:8077'
        },
        credentialId: {
            type: 'string', title: 'Credential',
            description: 'A basic credential holding the sdcpp-restapi username and password',
            dynamicOptions: { source: 'credentials', filter: { type: ['sdcpp', 'basic'] } }
        },
        modelName: {
            type: 'string', title: 'Model',
            description: 'File name of the model, relative to its type directory. Browse lists what the server has of the Model Type chosen below',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        modelType: {
            type: 'string', title: 'Model Type',
            enum: ['', 'checkpoint', 'diffusion'],
            default: '',
            description: 'checkpoint bundles U-Net, CLIP and VAE and suits SD1, SD2 and SDXL. diffusion holds only the U-Net or DiT and needs its components named separately, which is how Flux, SD3, Qwen, Wan and Z-Image load'
        },
        vae: {
            type: 'string', title: 'VAE',
            description: 'Component file name',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'vae' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        clipL: {
            type: 'string', title: 'CLIP-L',
            description: 'Component file name',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'clip' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        clipG: {
            type: 'string', title: 'CLIP-G',
            description: 'Component file name',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'clip' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        t5xxl: {
            type: 'string', title: 'T5-XXL',
            description: 'Component file name',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 't5' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        llm: {
            type: 'string', title: 'LLM',
            description: 'Component file name, used by Z-Image, Qwen, Anima and Flux2',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'llm' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        taesd: {
            type: 'string', title: 'TAESD',
            description: 'Tiny autoencoder for progress previews',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'taesd' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        controlnet: {
            type: 'string', title: 'ControlNet',
            description: 'Component file name',
            dynamicOptions: {
                source: 'node',
                node: 'sdcpp-model',
                config: { listOnly: true, modelType: 'controlnet' },
                itemsPath: 'models',
                valueKey: 'name',
                labelKey: 'name',
                needs: ['serverUrl', 'credentialId']
            }
        },
        flashAttn: { type: 'boolean', title: 'Flash Attention', description: 'For CLIP and T5. A large speed and memory win on modern GPUs' },
        diffusionFlashAttn: { type: 'boolean', title: 'Flash Attention (diffusion)', description: 'Flash attention for the diffusion model specifically' },
        enableMmap: { type: 'boolean', title: 'Memory-map Weights', description: 'Recommended for large files' },
        eagerLoad: { type: 'boolean', title: 'Eager Load', description: 'Move every parameter to the compute backend at load time instead of on demand' },
        streamLayers: { type: 'boolean', title: 'Stream Layers', description: 'Stream diffusion layers when the model does not fit in VRAM. Pair with a VRAM budget' },
        maxVram: { type: 'number', title: 'VRAM Budget (GiB)', description: 'Budget for segmented parameter offload. 0 leaves it to the server' },
        nThreads: { type: 'number', title: 'CPU Threads', description: '-1 lets the server decide' },
        weightType: {
            type: 'string', title: 'Weight Type',
            enum: ['', 'f32', 'f16', 'bf16', 'q8_0', 'q5_0', 'q5_1', 'q4_0', 'q4_1', 'q4_k', 'q5_k', 'q6_k', 'q8_k', 'q3_k', 'q2_k', 'mxfp4', 'nvfp4', 'q1_0'],
            enumLabels: ['auto - use the weights in the file', 'f32', 'f16', 'bf16', 'q8_0', 'q5_0', 'q5_1', 'q4_0', 'q4_1', 'q4_k', 'q5_k', 'q6_k', 'q8_k', 'q3_k', 'q2_k', 'mxfp4', 'nvfp4', 'q1_0'],
            default: '', description: 'Force a quantisation. Left on auto, sd.cpp uses whatever the model file holds'
        },
        vaeFormat: {
            type: 'string', title: 'VAE Format',
            enum: ['', 'auto', 'flux', 'sd3', 'flux2', 'wan'],
            enumLabels: ['not set - leave it to the server', 'auto - detect from the file', 'flux', 'sd3', 'flux2', 'wan'],
            default: '', description: 'Override VAE format detection'
        },
        prediction: {
            type: 'string', title: 'Prediction Type',
            enum: ['', 'eps', 'v', 'edm_v', 'sd3_flow', 'flux_flow', 'flux2_flow', 'sefi_flow', 'minit2i_flow'],
            enumLabels: ['auto - detect from the model', 'eps', 'v', 'edm_v', 'sd3_flow', 'flux_flow', 'flux2_flow', 'sefi_flow', 'minit2i_flow'],
            default: '', description: 'Override the prediction type. Left on auto it is detected from the model'
        },
        rngType: {
            type: 'string', title: 'RNG', enum: ['', 'cuda', 'std_default', 'cpu'],
            enumLabels: ['server default', 'cuda', 'std_default', 'cpu'],
            default: '', description: 'Affects whether a seed reproduces across backends'
        },
        samplerRngType: {
            type: 'string', title: 'Sampler RNG', enum: ['', 'cuda', 'std_default', 'cpu'],
            enumLabels: ['same as RNG above', 'cuda', 'std_default', 'cpu'],
            default: '',
            description: 'Override the RNG for sampling only. The server does not report this one, so Fill in leaves it alone'
        },
        loraApplyMode: {
            type: 'string', title: 'LoRA Apply Mode', enum: ['', 'auto', 'immediately', 'at_runtime'],
            enumLabels: ['server default', 'auto', 'immediately', 'at_runtime'],
            default: '', description: 'When a LoRA named in a prompt is applied'
        },
        vaeConvDirect: { type: 'boolean', title: 'Direct VAE Convolution', description: 'Use the ggml_conv2d_direct path for the VAE' },
        diffusionConvDirect: { type: 'boolean', title: 'Direct Diffusion Convolution', description: 'Use the ggml_conv2d_direct path for the diffusion model' },
        forceSdxlVaeConvScale: { type: 'boolean', title: 'Force SDXL VAE Conv Scale', description: 'SDXL-specific VAE convolution scaling' },
        taePreviewOnly: { type: 'boolean', title: 'TAESD For Preview Only', description: 'Load TAESD purely to render progress previews, skipping the full VAE' },
        backend: { type: 'string', title: 'Backend', description: 'Per-component placement, such as te=cpu,vae=cpu,controlnet=cpu' },
        paramsBackend: { type: 'string', title: 'Parameter Backend', description: 'Global parameter placement, such as *=cpu to hold weights in system RAM' },
        rpcServers: { type: 'string', title: 'RPC Servers', description: 'Comma separated RPC backend endpoints' },
        modelArgs: { type: 'string', title: 'Model Args', description: 'Architecture-specific key=value knobs, comma separated' },
        tensorTypeRules: { type: 'string', title: 'Tensor Type Rules', description: 'Per-tensor weight overrides using regex, such as ^vae\\.=f16' },
        options: {
            type: 'object', title: 'Other Load Options',
            description: 'Extra load options passed through, such as flash_attn, enable_mmap, weight_type, stream_layers or max_vram'
        },
        whenDifferent: {
            type: 'string', title: 'When A Different Model Is Loaded',
            enum: ['load', 'fail'],
            default: 'load',
            description: 'load swaps it. fail stops the run instead - for a workflow that depends on a particular model already being in place and should not quietly spend minutes swapping it'
        },
        force: {
            type: 'boolean', title: 'Force Reload',
            default: false,
            description: 'Load again even when the right model is already loaded. Costs the full load time; useful after changing components or options, which this node cannot see from outside',
            showWhen: { field: 'whenDifferent', value: 'load' }
        },
        timeout: {
            type: 'number', title: 'Timeout (ms)',
            description: 'Loading reads gigabytes from disk and can take minutes',
            default: 300000
        }
    },
    required: []
};

const inputSchema = { type: 'object', properties: { data: { type: 'any' } } };

const outputSchema = {
    type: 'object',
    properties: {
        modelName: { type: 'string', description: 'The model that is loaded now' },
        modelType: { type: 'string' },
        architecture: { type: 'string', description: 'Architecture the server detected, which decides generation defaults' },
        loaded: { type: 'boolean', description: 'True when this node performed a load' },
        alreadyLoaded: { type: 'boolean', description: 'True when the right model, with the right settings, was already in place' },
        reloadedFor: { type: 'array', description: 'Settings that differed on an otherwise-correct model, when that is why it was reloaded' },
        previousModel: { type: 'string', description: 'What was loaded before, when this node swapped it' },
        loadedComponents: { type: 'object' },
        elapsedMs: { type: 'number' }
    }
};

function normalizeServer(url) {
    const value = String(url || '').trim();
    if (!value) {
        throw new Error('SD.cpp: a server URL is required, such as http://localhost:8077');
    }
    return value.replace(/\/+$/, '');
}

function readCredential(credentialId) {
    const auth = smartbotic.credentials.get(credentialId);
    if (!auth || auth.success !== true) {
        throw new Error('SD.cpp: could not read the credential: ' +
            ((auth && auth.error) || 'unknown error'));
    }

    const value = auth.headerValue || '';
    if (value.indexOf('Basic ') !== 0) {
        throw new Error('SD.cpp: the credential must be a basic one, holding the sdcpp-restapi ' +
            'username and password');
    }

    const decoded = smartbotic.utils.base64Decode(value.substring(6));
    const separator = decoded.indexOf(':');
    if (separator < 1) {
        throw new Error('SD.cpp: the credential is malformed, expected a username and a password');
    }

    return {
        username: decoded.substring(0, separator),
        password: decoded.substring(separator + 1)
    };
}

function call(options) {
    const response = smartbotic.http.request(options);

    let body = response.data;
    if (typeof body === 'string' && body.length > 0) {
        try {
            body = JSON.parse(body);
        } catch (e) {
            const snippet = body.substring(0, 200).replace(/\s+/g, ' ');
            throw new Error('SD.cpp: ' + options.what + ' returned HTTP ' + response.status +
                ' with a body that is not JSON: ' + snippet);
        }
    }

    if (response.status < 200 || response.status >= 300) {
        const detail = (body && (body.message || body.error)) || ('HTTP ' + response.status);
        throw new Error('SD.cpp: ' + options.what + ' failed: ' + detail);
    }

    return body || {};
}

function login(server, credential, timeout) {
    const session = call({
        method: 'POST',
        url: server + '/auth/login',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
            username: credential.username,
            password: credential.password
        }),
        timeout: timeout,
        what: 'signing in'
    });

    if (!session.token) {
        throw new Error('SD.cpp: the server accepted the login but returned no token');
    }
    return session.token;
}

// /health is unauthenticated, and it is the only way to find out what is
// already loaded without asking for a token first.
function readHealth(server, timeout) {
    return call({
        method: 'GET',
        url: server + '/health',
        timeout: timeout,
        what: 'reading server health'
    });
}

function putIfSet(target, key, value) {
    if (value === undefined || value === null || value === '') {
        return;
    }
    target[key] = value;
}

const LOAD_OPTIONS = [
    { setting: 'flashAttn', server: 'flash_attn' },
    { setting: 'diffusionFlashAttn', server: 'diffusion_flash_attn' },
    { setting: 'enableMmap', server: 'enable_mmap' },
    { setting: 'eagerLoad', server: 'eager_load' },
    { setting: 'streamLayers', server: 'stream_layers' },
    { setting: 'maxVram', server: 'max_vram' },
    { setting: 'nThreads', server: 'n_threads' },
    { setting: 'weightType', server: 'weight_type' },
    { setting: 'vaeFormat', server: 'vae_format' },
    { setting: 'prediction', server: 'prediction' },
    { setting: 'rngType', server: 'rng_type' },
    { setting: 'samplerRngType', server: 'sampler_rng_type' },
    { setting: 'loraApplyMode', server: 'lora_apply_mode' },
    { setting: 'vaeConvDirect', server: 'vae_conv_direct' },
    { setting: 'diffusionConvDirect', server: 'diffusion_conv_direct' },
    { setting: 'taePreviewOnly', server: 'tae_preview_only' },
    { setting: 'forceSdxlVaeConvScale', server: 'force_sdxl_vae_conv_scale' },
    { setting: 'backend', server: 'backend' },
    { setting: 'paramsBackend', server: 'params_backend' },
    { setting: 'rpcServers', server: 'rpc_servers' },
    { setting: 'modelArgs', server: 'model_args' },
    { setting: 'tensorTypeRules', server: 'tensor_type_rules' },
];


// The options this node asks for, as the server names them. Only settings that
// were actually filled in are included: an untouched setting means "whatever
// the server does", not "the default", so it is neither sent nor compared.
function wantedOptions(config) {
    var wanted = {};
    for (var i = 0; i < LOAD_OPTIONS.length; i++) {
        var entry = LOAD_OPTIONS[i];
        var value = config[entry.setting];
        if (value === undefined || value === null || value === '') continue;
        if (typeof value === 'number' && !isFinite(value)) continue;
        wanted[entry.server] = value;
    }
    if (config.options && typeof config.options === 'object') {
        var keys = Object.keys(config.options);
        for (var k = 0; k < keys.length; k++) {
            var extra = config.options[keys[k]];
            if (extra !== undefined && extra !== null && extra !== '') {
                wanted[keys[k]] = extra;
            }
        }
    }
    return wanted;
}

// Which of them the server is not currently loaded with.
//
// This is what makes the node able to correct a server someone else changed:
// the same model loaded with streaming off is not the same thing as the model
// this workflow needs, and reloading it is the whole point of saying so here.
function optionsThatDiffer(wanted, current) {
    var differing = [];
    var keys = Object.keys(wanted);
    for (var i = 0; i < keys.length; i++) {
        var key = keys[i];
        var have = current ? current[key] : undefined;
        var want = wanted[key];
        // Numbers arrive as 0 or 0.0 depending on the field, and a boolean may
        // come back as a string from a form, so compare on value rather than
        // on type.
        var same = (typeof want === 'number' || typeof have === 'number')
            ? Number(have) === Number(want)
            : String(have) === String(want);
        if (!same) {
            differing.push(key + ': server has ' + JSON.stringify(have) +
                           ', this node wants ' + JSON.stringify(want));
        }
    }
    return differing;
}

async function execute(config, input, context) {
    const server = normalizeServer(config.serverUrl);
    const timeout = config.timeout > 0 ? config.timeout : 300000;
    const modelName = String(config.modelName || '').trim();

    if (!modelName) {
        throw new Error('SD.cpp: a model name is required. Connect an SD.cpp Model node, ' +
            'or type the file name');
    }

    // Ask what is loaded before loading anything. A load takes minutes and
    // unloads whatever was there, so doing it when the right model is already
    // resident is pure cost - and on a shared server it disrupts other work.
    const startedAt = Date.now();
    const health = readHealth(server, Math.min(timeout, 15000));
    const current = health.model_name || '';
    const sameModel = current === modelName;

    // The same model loaded with different settings is not the model this
    // workflow asked for. The server swaps models between queue items, so what
    // is loaded now may have been put there by something else entirely.
    const wanted = wantedOptions(config);
    const differing = optionsThatDiffer(wanted, health.load_options || {});

    if (sameModel && differing.length === 0 && config.force !== true) {
        smartbotic.log.info('SD.cpp: ' + modelName + ' is already loaded with the wanted settings');
        return {
            modelName: current,
            modelType: health.model_type || '',
            architecture: health.model_architecture || '',
            loaded: false,
            alreadyLoaded: true,
            reloadedFor: [],
            previousModel: '',
            loadedComponents: health.loaded_components || {},
            elapsedMs: Date.now() - startedAt
        };
    }

    if ((config.whenDifferent || 'load') === 'fail' && (!sameModel || differing.length > 0)) {
        if (!sameModel) {
            throw new Error('SD.cpp: this workflow expects "' + modelName + '" to be loaded, but ' +
                (current ? 'the server has "' + current + '"' : 'no model is loaded') +
                '. Set When A Different Model Is Loaded to "load" to swap it automatically');
        }
        throw new Error('SD.cpp: "' + modelName + '" is loaded, but not with the settings this ' +
            'workflow needs - ' + differing.join('; ') +
            '. Set When A Different Model Is Loaded to "load" to reload it');
    }

    if (sameModel && differing.length > 0) {
        smartbotic.log.info('SD.cpp: reloading ' + modelName + ' because ' + differing.join('; '));
    }

    const credential = readCredential(config.credentialId);
    const token = login(server, credential, Math.min(timeout, 30000));

    const body = { model_name: modelName };
    putIfSet(body, 'model_type', config.modelType);
    putIfSet(body, 'vae', config.vae);
    putIfSet(body, 'clip_l', config.clipL);
    putIfSet(body, 'clip_g', config.clipG);
    putIfSet(body, 't5xxl', config.t5xxl);
    putIfSet(body, 'llm', config.llm);
    putIfSet(body, 'taesd', config.taesd);
    putIfSet(body, 'controlnet', config.controlnet);
    if (Object.keys(wanted).length > 0) {
        body.options = wanted;
    }

    smartbotic.log.info('SD.cpp: loading ' + modelName +
        (current ? ' (replacing ' + current + ')' : ''));

    // The slot has to be emptied first. The API documentation says a load
    // replaces whatever is there, but the server answers "A model is already
    // loaded. Call POST /models/unload first" - so it is unloaded here rather
    // than leaving every reload to fail on a server that already has a model.
    //
    // This is also the only way to change the settings of a model that is
    // already loaded, which is the case this node exists to handle.
    if (health.model_loaded === true) {
        call({
            method: 'POST',
            url: server + '/models/unload',
            headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer ' + token },
            body: '{}',
            timeout: Math.min(timeout, 60000),
            what: 'unloading ' + (current || 'the current model') + ' before loading ' + modelName
        });
        smartbotic.log.info('SD.cpp: unloaded ' + (current || 'the previous model'));
    }

    // Loading unloads whatever was in the slot first, and the server holds a
    // mutex for the duration, so this blocks until the weights are resident.
    const loaded = call({
        method: 'POST',
        url: server + '/models/load',
        headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer ' + token },
        body: JSON.stringify(body),
        timeout: timeout,
        what: 'loading model ' + modelName
    });

    // The load call comes back before the model is in memory. The API
    // documentation describes it as blocking, and it is not: /health reports
    // model_loading with a step count for some time afterwards. Returning here
    // would tell the workflow the model is ready and let the next node ask it
    // to generate, which fails with "no model loaded" - a confusing way to
    // learn that this node lied.
    const deadline = startedAt + timeout;
    let after = readHealth(server, 15000);
    let lastStep = -1;

    while (after.model_loading === true && Date.now() < deadline) {
        const step = after.loading_step;
        const total = after.loading_total_steps;
        if (typeof step === 'number' && step !== lastStep) {
            lastStep = step;
            smartbotic.log.info('SD.cpp: loading ' + (after.loading_model_name || modelName) +
                ' - ' + step + (total ? '/' + total : ''));
        }
        smartbotic.utils.sleep(2000);
        after = readHealth(server, 15000);
    }

    if (after.model_loading === true) {
        throw new Error('SD.cpp: ' + modelName + ' was still loading after ' +
            Math.round((Date.now() - startedAt) / 1000) + 's. It may still finish on the server; ' +
            'raise the timeout on this node if this model is simply slow to load');
    }

    if (after.model_loaded !== true) {
        throw new Error('SD.cpp: the server accepted the load but has no model loaded afterwards' +
            (after.last_error ? ': ' + after.last_error : ''));
    }

    return {
        modelName: loaded.model_name || modelName,
        modelType: loaded.model_type || config.modelType || '',
        architecture: after.model_architecture || '',
        loaded: true,
        alreadyLoaded: false,
        // Empty when the model itself changed; otherwise the settings that
        // forced a reload of a model that was already there.
        reloadedFor: sameModel ? differing : [],
        previousModel: sameModel ? '' : current,
        loadedComponents: loaded.loaded_components || after.loaded_components || {},
        elapsedMs: Date.now() - startedAt
    };
}

module.exports = { configSchema, inputSchema, outputSchema, execute };