sorry folks - that's a wrap

src/providers/video-generation/generateVideoWithAnimateDiffLightning.mts

@@ -5,12 +5,14 @@ import { getValidNumber } from "../../utils/validators/getValidNumber.mts"
 
 const accessToken = `${process.env.VC_MICROSERVICE_SECRET_TOKEN || ""}`
 
+// @deprecated This endpoint has been decommissioned. Please use the AiTube API instead (check aitube.at/api/v1/render)
 export const generateVideoWithAnimateDiffLightning = async (
   request: RenderRequest,
   response: RenderedScene,
 ): Promise<RenderedScene> => {
   
-  const debug = false
+  throw new Error(`This endpoint has been decommissioned. Please use the AiTube API instead (check aitube.at/api/v1/render)`)
+  const debug = true
 
 
   const actualFunction = async (): Promise<RenderedScene> => {
@@ -32,17 +34,19 @@ export const generateVideoWithAnimateDiffLightning = async (
 
     // 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, 256)
+    const nbSteps = 4// getValidNumber(request.nbSteps, 1, 8, 4)
+    const width = 512 // getValidNumber(request.width, 256, 1024, 512)
+    const height = 288 // getValidNumber(request.height, 256, 1024, 256)
 
-    const nbFrames = getValidNumber(request.nbFrames, 10, 60, 10)
-    const nbFPS = getValidNumber(request.nbFPS, 10, 60, 10)
+    const nbFrames = 16 // getValidNumber(request.nbFrames, 10, 60, 10)
+    const nbFPS = 10 //  getValidNumber(request.nbFPS, 10, 60, 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 durationInSec = nbFrames / nbFPS
+    // no, we need decimals
+    // const durationInSec = Math.round(nbFrames / nbFPS)
     const framesPerSec = nbFPS
 
     try {

src/providers/video-generation/generateVideoWithAnimateLCM.mts

@@ -1,169 +0,0 @@
-import { VideoGenerationParams } from "../../types.mts"
-import { generateSeed } from "../../utils/misc/generateSeed.mts"
-import { tryApiCalls } from "../../utils/misc/tryApiCall.mts"
-import { addBase64HeaderToMp4 } from "./addBase64HeaderToMp4.mts"
-
-import { getNegativePrompt, getPositivePrompt } from "./defaultPrompts.mts"
-
-// const gradioApi = `${process.env.AI_TUBE_MODEL_ANIMATELCM_GRADIO_URL || ""}`
-const gradioApi = "https://jbilcke-hf-ai-tube-model-animatelcm.hf.space"
-const accessToken = `${process.env.VC_MICROSERVICE_SECRET_TOKEN || ""}`
-
-export const generateVideoWithAnimateLCM = async ({
-  prompt,
-  orientation,
-  projection,
-  width,
-  height,
-  style = "",
-  nbSteps = 4,
-  nbFrames = 20,
-  seed,
-  debug,
-}: VideoGenerationParams): Promise<string> => {
-  
-
-  const actualFunction = async () => {
-    
-    // seed = seed || generateSeed()
-    seed = generateSeed()
-
-    // label="Sampling steps", value=6, minimum=1, maximum=25, step=1
-    // we wanna keep this one low (this is LCM after all)
-    // but values like 10 also give nice results
-    const nbSteps = 6 // 25
-  
-
-    // label="LoRA alpha", value=0.8, minimum=0, maximum=2
-    const loraAlpha = 0.8 // lora_alpha_slider,
-
-
-            // label="LCM LoRA alpha", value=0.8, minimum=0.0, maximum=1.0
-    const lcmLoraAlpha = 0.8 // spatial_lora_slider,
-
-    // label="Width", value=512, minimum=256, maximum=1024, step=64)
-
-    // label="Animation length", value=16,  minimum=12,   maximum=20,   step=1)
-    const nbFrames = 16
-
-    // label="Height", value=512, minimum=256, maximum=1024, step=64)
-   
-    //  label="CFG Scale", value=1.5, minimum=1,   maximum=2)
-    const cfgScale = 1.5
-
-    // pimp the prompt
-
-    /*
-    // we put it at the start, to make sure it is always part of the prompt
-    const positivePrompt = getPositivePrompt([
-      style,
-      prompt
-    ].map(x => x.trim()).filter(x => x).join(", "))
-
-    const negativePrompt = getNegativePrompt(negPrompt)
-    */
-   const positivePrompt = "Close-up of the dancing duo as they take a bow. The orange tabby cat wears a sequined top hat, and the Siamese cat hugs their shoulders, also wearing a sequined outfit"
-   const negativePrompt = ""
-
-    try {
-      if (debug) {
-        console.log(`calling AnimateLCM API with params (some are hidden):`, {
-          loraAlpha,
-          lcmLoraAlpha,
-          positivePrompt,
-          negativePrompt,
-          width,
-          height,
-          nbSteps,
-          nbFrames,
-          cfgScale,
-          seed,
-        })
-      }
-
-      const res = await fetch(gradioApi + (gradioApi.endsWith("/") ? "" : "/") + "api/predict", {
-        method: "POST",
-        headers: {
-          "Content-Type": "application/json",
-          // Authorization: `Bearer ${token}`,
-        },
-        body: JSON.stringify({
-          fn_index: 4, // <- important! it is currently 4, not 1!
-          data: [
-            accessToken,
-
-            // label="LoRA alpha", value=0.8, minimum=0, maximum=2
-            loraAlpha, // lora_alpha_slider,
-
-
-            // label="LCM LoRA alpha", value=0.8, minimum=0.0, maximum=1.0
-            lcmLoraAlpha, // spatial_lora_slider,
-
-            // 
-            positivePrompt, // prompt_textbox,
-
-            negativePrompt, // negative_prompt_textbox,
-            
-            // this is the scheduler
-            // so.. LCM, it is
-            "LCM", // sampler_dropdown,
-
-            // label="Sampling steps", value=6, minimum=1, maximum=25, step=1
-            // we wanna keep this one low (this is LCM after all)
-            // but values like 10 also give nice results
-            nbSteps, // sample_step_slider,
-
-            // label="Width",            value=512, minimum=256, maximum=1024, step=64)
-            width, // width_slider,
-
-            // label="Animation length", value=16,  minimum=12,   maximum=20,   step=1)
-            nbFrames, // length_slider,
-
-            // label="Height",           value=512, minimum=256, maximum=1024, step=64)
-            height, // height_slider,
-
-            //  label="CFG Scale",        value=1.5, minimum=1,   maximum=2)
-            cfgScale, // cfg_scale_slider,
-            
-            seed, // seed_textbox,
-          ],
-        }),
-        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)`)
-      }
-
-      return addBase64HeaderToMp4(base64Content)
-    } catch (err) {
-      if (debug) {
-        console.error(`failed to call the AnimateLCM API:`)
-        console.error(err)
-      }
-      throw err
-    }
-  }
-
-  return tryApiCalls({
-    func: actualFunction,
-    debug,
-    failureMessage: "failed to call the AnimateLCM endpoint"
-  })
-}