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FLUX.2 Klein 9B - Reference-Based Face Swap Workflow

Updated: Jun 5, 2026

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Published

Jun 5, 2026

Base Model

Flux.2 Klein 9B

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AutoV2
D41A242AA6
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veribe12

The FLUX.1 [dev] Model is licensed by Black Forest Labs. Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs. Inc.

IN NO EVENT SHALL BLACK FOREST LABS, INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.

FLUX.2 Klein 9B - Reference-Based Face Swap Workflow

⚡ Production-tested workflow.

This workflow originated from an internal automation pipeline capable of processing hundreds of images automatically through the ComfyUI API on a single NVIDIA T4 GPU.

A highly consistent face swap workflow for ComfyUI built around FLUX.2 Klein 9B, latent references, and a dedicated face swap LoRA.

Unlike traditional face swap methods, this workflow uses both a Target Reference and a Source Face Reference to preserve composition while transferring identity.


Requirements

Before loading the workflow, make sure you have:

  • FLUX.2 Klein 9B

  • FLUX2 VAE

  • Qwen 3 8B FP8 Mixed Text Encoder

  • Face Swap LoRA

  • LanPaint Custom Nodes


Required Models

FLUX.2 Klein 9B

Place inside:

ComfyUI/models/unet/

Model:

flux-2-klein-9b.safetensors

FLUX2 VAE

Place inside:

ComfyUI/models/vae/

Model:

flux2-vae.safetensors

Qwen 3 8B FP8 Mixed

Place inside:

ComfyUI/models/text_encoders/

Model:

qwen_3_8b_fp8mixed.safetensors

Face Swap LoRA

Place inside:

ComfyUI/models/loras/

Model:

bfs_head_v1_flux-klein_9b_step3500_rank128.safetensors

Required Custom Nodes

LanPaint

This workflow uses the LanPaint sampler.

Install it through:

ComfyUI Manager → Install Missing Custom Nodes


Workflow Overview

This workflow uses two different references:

Target Reference

Preserves:

  • Composition

  • Pose

  • Framing

  • Perspective

  • Scene structure

  • Lighting context

Source Face Reference

Transfers:

  • Face identity

  • Facial proportions

  • Hair characteristics

  • Eyes

  • Skin details

By combining both references, the workflow achieves significantly better consistency than traditional face swap approaches.


How To Use

Step 1 - Load The Source Face

Node:

Source Face Image

This image provides the identity that will be transferred.

Recommended:

✅ Front-facing portraits

✅ High-resolution images

✅ Clear facial visibility

✅ Good lighting

Avoid:

❌ Motion blur

❌ Heavy occlusions

❌ Sunglasses

❌ Extreme angles


Step 2 - Load The Target Image

Node:

Target Image

This image provides:

  • Background

  • Body

  • Clothing

  • Pose

  • Composition

  • Framing

  • Perspective

The workflow attempts to preserve these elements while replacing the identity.

Recommended:

✅ Portrait photos

✅ Medium shots

✅ Visible face

✅ Clear lighting

Avoid:

❌ Tiny faces

❌ Hidden faces

❌ Extreme side profiles


Step 3 - Queue Prompt

Press:

Queue Prompt

The workflow automatically:

  1. Reads target dimensions

  2. Creates target latent references

  3. Creates source face latent references

  4. Applies dual-reference conditioning

  5. Generates the face-swapped result

  6. Preserves original scene composition

No additional setup is required.


What This Workflow Preserves

  • Original background

  • Original composition

  • Original lighting

  • Original perspective

  • Original pose

  • Original framing

  • Original camera distance

  • Original scene layout


Ideal Use Cases

  • UGC Creator Replacement

  • Influencer Face Swaps

  • Character Consistency

  • Marketing Creatives

  • AI Avatars

  • Personal Branding

  • Dataset Generation

  • Batch Face Swap Pipelines


Performance

Tested on:

  • NVIDIA T4

  • RTX 3060

  • RTX 4070

  • RTX 4090

VRAM requirements will vary depending on image resolution.


Batch Automation

This public release contains the single-image workflow.

Internally, I use a custom Python automation system connected directly to the ComfyUI API that can process large batches automatically.

Example setup:

  • 1 source face

  • 400+ target images

  • Automatic queue management

  • Automatic retries

  • Organized output folders

  • GPU-friendly processing

  • Designed for T4 cloud instances

This automation system is not included in the public release.


Commercial Applications

This workflow can be used for:

  • UGC generation

  • Marketing campaigns

  • Creator replacement

  • Character consistency projects

  • AI influencer pipelines

  • Content automation


Need Batch Processing?

I also develop custom automation systems built around ComfyUI.

Examples:

  • Face swap automation

  • Folder-based processing

  • ComfyUI API integrations

  • Large-scale image generation pipelines

  • Dataset creation workflows

  • Custom workflow development

If you're interested in automation or custom solutions, feel free to send me a direct message on Civitai.


Credits

Built and tested using FLUX.2 Klein 9B, latent reference conditioning, and a dedicated face swap LoRA for high-consistency identity transfer.

If you find this workflow useful, consider leaving a rating and sharing your results.