This workflow demonstrates how to upscale and restore images using SeedVR2 3B Int8, a one‑step diffusion-based video restoration model adapted here for still images and frame-by-frame processing. The core of the graph is a SeedVR2 inference node (id: 1970dcb4-982a-4c01-a373-4d239a3041e4) configured for the 3B Int8 weights and paired with the provided VAE (seedvr2_ema_vae_fp16.safetensors). You load your source with LoadImage, route it through the SeedVR2 node for enhancement/upscaling, compare results with ImageCompare, and finalize with SaveImage. A MarkdownNote in the graph provides inline guidance and links.
Technically, SeedVR2 performs a single forward pass to restore detail, reduce compression artifacts, and upscale, avoiding iterative denoising. The Int8 quantized 3B variant reduces VRAM usage and speeds up inference with a small quality trade-off versus full precision. While SeedVR2 is trained for video restoration and improved temporal consistency across sequences, it also produces strong results on individual images. If you process frame sequences consistently (e.g., exported video frames), the model’s training tends to yield visually stable outputs from frame to frame even when used one-at-a-time in ComfyUI.
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）。
