How much does IP-Adapter FaceID Plus v2 cost via Railwail?

No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of IP-Adapter FaceID Plus v2?

IP-Adapter FaceID Plus v2 supports a unknown context window — enough for typical AI workloads.

How fast is IP-Adapter FaceID Plus v2?

Latency depends on prompt length and load — typically 200ms to 2s for short prompts. We measure p50/p95 in real-time on /rankings.

Is IP-Adapter FaceID Plus v2 better than Flux 1.1 Pro Ultra?

It depends on your use case. IP-Adapter FaceID Plus v2 (Replicate) and Flux 1.1 Pro Ultra (Black Forest Labs) are both strong choices in image generation. Compare them side-by-side at /compare/ip-adapter-faceid-plus-v2-vs-flux-pro-ultra.

Does IP-Adapter FaceID Plus v2 support image input (vision)?

Yes — IP-Adapter FaceID Plus v2 accepts image inputs in addition to text. Send images via the standard OpenAI-compatible `messages` array with `image_url` content blocks. Supported formats: image, text.

IP-Adapter FaceID Plus v2

Name: IP-Adapter FaceID Plus v2
Brand: Replicate
SKU: ip-adapter-faceid-plus-v2
Availability: InStock

Replicate

Image Generation

Tencent's face-identity conditioning adapter for SD/SDXL. Face embedding + CLIP for ID-consistent generation.

Generate with IP-Adapter FaceID Plus v2

Describe what you want and pick a size — the image renders inline.

Size

Result appears here

TL;DR·Last updated May 16, 2026

IP-Adapter FaceID Plus v2 is image generation AI model from Replicate, priced at €0.000 per 1M input tokens with a unknown context window.

Try IP-Adapter FaceID Plus v2

Prompt

Aspect Ratio

Quality

Pricing

Price per Generation

Per generationFree

API Integration

Use our OpenAI-compatible API to integrate IP-Adapter FaceID Plus v2 into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const images = await rw.run("ip-adapter-faceid-plus-v2", "A beautiful sunset over Tokyo");
console.log(images[0].url);

// Or use the image() method for full control
const res = await rw.image("ip-adapter-faceid-plus-v2", "A cat in space", {
  size: "1024x1024",
  n: 1,
});
console.log(res.data[0].url);

Specifications

Developer

Replicate

Deep dive — Tencent (ARC Lab)'s IP-Adapter FaceID Plus v2

About Tencent (ARC Lab)

Founded 1998 · Shenzhen, China

IP-Adapter is a family of open-source image-prompt adapters developed by the Applied Research Center (ARC Lab) at Tencent. The original IP-Adapter paper (Ye et al. 2023, arXiv 2308.06721) introduced a decoupled cross-attention scheme that lets a pretrained text-to-image diffusion model accept image prompts as a parallel conditioning channel without retraining the backbone. The FaceID family extends this idea with face-identity embeddings (extracted by InsightFace's ArcFace recogniser) instead of CLIP image embeddings, achieving high identity preservation. IP-Adapter FaceID Plus v2 is a refined version released by author Hu Ye in late 2023 / early 2024 combining ArcFace identity vectors with CLIP image features for better likeness and prompt control. The code and weights are released open-source on GitHub (tencent-ailab/IP-Adapter).

Visit Tencent (ARC Lab) →

Architecture

Cross-attention adapter (decoupled image prompt) on top of SDXL/SD1.5

IP-Adapter FaceID Plus v2 is an adapter module attached to a frozen Stable Diffusion XL (or SD 1.5) backbone. The architecture is based on the IP-Adapter design (Ye et al. 2023): a small MLP projector maps an external image embedding into the cross-attention space of the diffusion U-Net, and a parallel set of cross-attention layers is added so that text and image conditioning operate on decoupled keys/values. The 'FaceID' line replaces the CLIP image encoder with InsightFace's ArcFace face recogniser, which produces a 512-dim identity embedding focused on facial geometry rather than appearance. The 'Plus v2' variant combines the ArcFace embedding with a CLIP image embedding of the face crop, giving the model both identity (from ArcFace) and pose/lighting cues (from CLIP). At inference the user supplies one or more reference face images plus a text prompt, and the adapter steers the diffusion process toward a portrait that matches the identity while following the prompt. Training used a large face dataset with paired identity vectors and aligned face crops.

Parameters: ~100M adapter parameters on top of a frozen SDXL or SD1.5 backbone
Context: 75 tokens

What it can do

High-quality face-identity preservation from one reference image
Works on top of SDXL and SD 1.5 with frozen weights
Compatible with LoRAs, ControlNets and other adapters
Open-source under non-commercial / academic terms
Strong prompt control for clothing, scene and style while preserving face
Used as the backbone for many consumer 'AI photo' apps in 2024
Best for: avatar generation, personalised marketing, headshot apps, character consistency.

Training & License

Trained on large public face datasets (LAION subsets, FFHQ-style data) with InsightFace embeddings extracted per face. Exact composition is not disclosed.

License: Open-source weights under the Tencent IP-Adapter license, which allows research and non-commercial use. Commercial use generally requires negotiation with Tencent ARC Lab.

Known limitations