This workflow turns a single image into a short, cinematic video using LTX‑2.5’s DFR (Diffusion Fidelity Rendering) path. The subgraph “Image to Video (LTX‑2.5 DFR)” runs a half‑resolution generation to establish motion and generate detailing keyframes, then applies a spatial ×2 detailing pass with a pixel‑spatial IC‑LoRA to recover fine texture. For projects that need extra smoothness, you can optionally add a temporal ×2 pass to densify motion between frames. The result is a cohesive clip that preserves your image’s look while adding natural camera movement and subject animation, with synchronized audio when provided by the subgraph.
Technically, the pipeline is compact and easy to drive in ComfyUI: LoadImage brings in your source frame, ResolutionSelector sets aspect and target size, and the DFR subgraph node (97d5d35b-bb0d-4212-b2f2-97ab13cbfb1f) encapsulates the LTX‑2 family model and its IC‑LoRA detailing passes. PreviewImage helps you quickly inspect a representative frame before final encoding, and SaveVideo writes the finished clip (including audio if the subgraph outputs it). MarkdownNote nodes in the template annotate key choices and link to ComfyUI’s Subgraph feature, so you can open the subgraph and tweak options without getting lost.
API
從程式碼使用此工作流
每個 Comfy 工作流都是一個 JSON 圖。下方的 payload 就是此工作流，與 ComfyUI 執行時完全相同 — 你可以從此 URL 取得、放入版本控制，或在 ComfyUI 載入並逐節點執行。
使用 Comfy SDK 以 TypeScript 或 Python 執行。相同程式碼可用於 Comfy Cloud 或你自架的 ComfyUI — 只需更改 base URL。
// Install (beta)
npm i @comfyorg/sdk
// Run this workflow (TypeScript)
import { Comfy } from "@comfyorg/sdk";
const client = new Comfy({ apiKey: "comfyui-..." });
const wf = await client.workflows.fromFile("workflow_api.json");
const job = await client.run(wf);
await job.getOutputs("<output-node-id>")[0].toFile("output.png");SDK 需使用 API 格式的工作流：請在 ComfyUI 開啟此工作流，並使用 檔案 → 匯出工作流（API）。
