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
通过代码使用此工作流
每个 Comfy 工作流都是一个 JSON 图。下面的内容就是此工作流的完整数据，ComfyUI 运行时也是如此——你可以从该 URL 获取、放入版本控制，或在 ComfyUI 中加载并逐节点运行。
使用 Comfy SDK 从 TypeScript 或 Python 运行。相同代码可用于 Comfy Cloud 或你自托管的 ComfyUI——只需更改基础 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 需要 API 格式的工作流：在 ComfyUI 中打开此工作流，使用 文件 → 导出工作流（API）。
