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
코드에서 이 워크플로우 사용하기
모든 Comfy 워크플로우는 JSON 그래프입니다. 아래 페이로드는 이 워크플로우 그 자체로, ComfyUI에서 실행되는 것과 동일합니다 — URL에서 가져오거나, 버전 관리에 보관하거나, ComfyUI에서 불러와 노드별로 실행할 수 있습니다.
Comfy SDK로 TypeScript 또는 Python에서 실행하세요. 동일한 코드는 Comfy Cloud 또는 직접 호스팅하는 ComfyUI 모두에 적용됩니다 — 기본 URL만 다릅니다.
// 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");SDK는 API 형식의 워크플로우를 사용합니다: 이 워크플로우를 ComfyUI에서 열고 파일 → 워크플로우 내보내기(API)를 사용하세요.
