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| import { generateSeed, getValidNumber } from "@aitube/clap" | |
| import { getClusterMachine, token } from "./cluster" | |
| export async function render(request: { | |
| prompt?: string | |
| seed?: number | |
| width?: number | |
| height?: number | |
| nbFrames?: number | |
| nbFPS?: number | |
| nbSteps?: number | |
| debug?: boolean | |
| }): Promise<string> { | |
| const prompt = request.prompt || "" | |
| if (!prompt) { | |
| throw new Error(`missing prompt`) | |
| } | |
| const debug = !!request.debug | |
| const seed = request?.seed || generateSeed() | |
| // see https://huggingface.co/spaces/jbilcke-hf/ai-tube-model-animatediff-lightning/blob/main/app.py#L15-L18 | |
| const baseModel = "epiCRealism" | |
| // the motion LoRA - could be useful one day | |
| const motion = "" | |
| // can be 1, 2, 4 or 8 | |
| // but values below 4 look bad | |
| const nbSteps = getValidNumber(request.nbSteps, 1, 8, 4) | |
| const width = getValidNumber(request.width, 256, 1024, 512) | |
| const height = getValidNumber(request.height, 256, 1024, 288) | |
| const nbFrames = getValidNumber(request.nbFrames, 10, 120, 10) | |
| const nbFPS = getValidNumber(request.nbFPS, 10, 120, 10) | |
| // by default AnimateDiff generates about 2 seconds of video at 10 fps | |
| // the Gradio API now has some code to optional fix that using FFmpeg, | |
| // but this will add some delay overhead, so use with care! | |
| const durationInSec = Math.round(nbFrames / nbFPS) | |
| const framesPerSec = nbFPS | |
| const machine = await getClusterMachine() | |
| try { | |
| if (debug) { | |
| console.log(`calling AnimateDiff Lightning API with params (some are hidden):`, { | |
| baseModel, | |
| motion, | |
| nbSteps, | |
| width, | |
| height, | |
| nbFrames, | |
| nbFPS, | |
| durationInSec, | |
| framesPerSec, | |
| }) | |
| } | |
| const res = await fetch(machine.url + (machine.url.endsWith("/") ? "" : "/") + "api/predict", { | |
| method: "POST", | |
| headers: { | |
| "Content-Type": "application/json", | |
| // Authorization: `Bearer ${token}`, | |
| }, | |
| body: JSON.stringify({ | |
| fn_index: 0, // <- important! it is currently 4, not 1! | |
| data: [ | |
| token, | |
| prompt, | |
| baseModel, | |
| width, | |
| height, | |
| motion, | |
| nbSteps, | |
| durationInSec, | |
| framesPerSec, | |
| ], | |
| }), | |
| // necessary since we are using the fetch() provided by NextJS | |
| cache: "no-store", | |
| // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache) | |
| // next: { revalidate: 1 } | |
| }) | |
| // console.log("res:", res) | |
| const { data } = await res.json() | |
| // console.log("data:", data) | |
| // Recommendation: handle errors | |
| if (res.status !== 200 || !Array.isArray(data)) { | |
| // This will activate the closest `error.js` Error Boundary | |
| throw new Error(`Failed to fetch data (status: ${res.status})`) | |
| } | |
| // console.log("data:", data.slice(0, 50)) | |
| const base64Content = (data?.[0] || "") as string | |
| if (!base64Content) { | |
| throw new Error(`invalid response (no content)`) | |
| } | |
| // this API already emits a data-uri with a content type | |
| // addBase64HeaderToMp4(base64Content) | |
| return base64Content | |
| } catch (err) { | |
| if (debug) { | |
| console.error(`failed to call the AnimateDiff Lightning API:`) | |
| console.error(err) | |
| } | |
| throw err | |
| } finally { | |
| // important: we need to free up the machine! | |
| machine.busy = false | |
| } | |
| } |