@huggingface/inference
Version:
Typescript client for the Hugging Face Inference Providers and Inference Endpoints
83 lines (82 loc) • 3.85 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.BlackForestLabsTextToImageTask = void 0;
/**
* See the registered mapping of HF model ID => Black Forest Labs model ID here:
*
* https://huggingface.co/api/partners/blackforestlabs/models
*
* This is a publicly available mapping.
*
* If you want to try to run inference for a new model locally before it's registered on huggingface.co,
* you can add it to the dictionary "HARDCODED_MODEL_ID_MAPPING" in consts.ts, for dev purposes.
*
* - If you work at Black Forest Labs and want to update this mapping, please use the model mapping API we provide on huggingface.co
* - If you're a community member and want to add a new supported HF model to Black Forest Labs, please open an issue on the present repo
* and we will tag Black Forest Labs team members.
*
* Thanks!
*/
const errors_js_1 = require("../errors.js");
const delay_js_1 = require("../utils/delay.js");
const omit_js_1 = require("../utils/omit.js");
const providerHelper_js_1 = require("./providerHelper.js");
const BLACK_FOREST_LABS_AI_API_BASE_URL = "https://api.us1.bfl.ai";
class BlackForestLabsTextToImageTask extends providerHelper_js_1.TaskProviderHelper {
constructor() {
super("black-forest-labs", BLACK_FOREST_LABS_AI_API_BASE_URL);
}
preparePayload(params) {
return {
...(0, omit_js_1.omit)(params.args, ["inputs", "parameters"]),
...params.args.parameters,
prompt: params.args.inputs,
};
}
prepareHeaders(params, binary) {
const headers = {
Authorization: params.authMethod !== "provider-key" ? `Bearer ${params.accessToken}` : `X-Key ${params.accessToken}`,
};
if (!binary) {
headers["Content-Type"] = "application/json";
}
return headers;
}
makeRoute(params) {
if (!params) {
throw new errors_js_1.InferenceClientInputError("Params are required");
}
return `/v1/${params.model}`;
}
async getResponse(response, url, headers, outputType) {
const urlObj = new URL(response.polling_url);
for (let step = 0; step < 5; step++) {
await (0, delay_js_1.delay)(1000);
console.debug(`Polling Black Forest Labs API for the result... ${step + 1}/5`);
urlObj.searchParams.set("attempt", step.toString(10));
const resp = await fetch(urlObj, { headers: { "Content-Type": "application/json" } });
if (!resp.ok) {
throw new errors_js_1.InferenceClientProviderApiError("Failed to fetch result from black forest labs API", { url: urlObj.toString(), method: "GET", headers: { "Content-Type": "application/json" } }, { requestId: resp.headers.get("x-request-id") ?? "", status: resp.status, body: await resp.text() });
}
const payload = await resp.json();
if (typeof payload === "object" &&
payload &&
"status" in payload &&
typeof payload.status === "string" &&
payload.status === "Ready" &&
"result" in payload &&
typeof payload.result === "object" &&
payload.result &&
"sample" in payload.result &&
typeof payload.result.sample === "string") {
if (outputType === "url") {
return payload.result.sample;
}
const image = await fetch(payload.result.sample);
return await image.blob();
}
}
throw new errors_js_1.InferenceClientProviderOutputError(`Timed out while waiting for the result from black forest labs API - aborting after 5 attempts`);
}
}
exports.BlackForestLabsTextToImageTask = BlackForestLabsTextToImageTask;