Easy Local AI Face Swapping with Z-Image Turbo in ComfyUI

This ComfyUI workflow — originally shared by Reddit user Retro Gaza Spurs — uses the Z-Image Turbo model combined with your custom character LoRA and the SAM 3 segmentation model to seamlessly place your character's face onto any target image. It's a clean, consistent face-swapping approach that runs on as little as 8 GB VRAM.

How It Works

Required Files

All files come from the Comfy-Org HuggingFace page (link in video description). Navigate to Files and Versions → split_files:

Manual Setup — ComfyUI Installation

1. Download the ComfyUI Portable ZIP from the ComfyUI releases page. Extract it with 7-Zip.
2. Navigate into the custom_nodes folder, click the address bar, type cmd, press Enter.
3. Clone the ComfyUI Manager:
   git clone https://github.com/ltdrdata/ComfyUI-Manager
4. Navigate back to the main ComfyUI portable directory (where the Python embedded folder is) and run the dependency install command from the written guide in the description.
5. Place your downloaded model files in the correct ComfyUI folders as shown in the table above.

Loading the Workflow

1. Launch ComfyUI. Download the workflow JSON file (link in video description) and drag it into the ComfyUI interface.
2. Red nodes will appear — open Manager → Install Missing Nodes and install each one, then restart ComfyUI.
3. After restart, verify the workflow has no red nodes.

Configuring the Workflow

The workflow is split into two sections:

Top Section — Model Loaders and SAM 3 Configuration

• Check the CLIP loader node — ensure it points to your Qwen3-4B GGUF clip model.
• Check the VAE loader nodes — the VAE appears in three different places in this workflow. Verify all three are correct.
• Leave SAM 3 settings at their defaults — they're well-optimized. SAM 3 will auto-download on the first run.

Bottom Section — Image Input and Generation

1. Upload your target image — the image whose face you want to replace.
2. In the Model Loader node, select your Z-Image Turbo diffusion model. Set weight dtype to FP8 to reduce VRAM usage.
3. In the LoRA Loader, select your character LoRA.
4. Review the Joy Caption node settings — toggle the true/false options for lighting, camera angles, and watermarks as needed. The Joy Caption model will auto-download on first run (use the 4-bit quantized version to save ~11 GB vs. the full precision model).
5. After Joy Caption generates a prompt, add your character's trigger word (and any missing details) in the "add important extra info here" node.

Running the Generation

1. Click Run. The first run downloads SAM 3 and Joy Caption automatically — this takes longer than subsequent runs.
2. Watch the preview nodes during generation — they show the segmentation mask in real-time so you can see exactly which areas are being processed.
3. Subsequent runs typically take 30 seconds to a few minutes depending on your hardware.

Tips for Best Results

• Set weight dtype to FP8 in the model loader to reduce VRAM requirements significantly.
• Check all three VAE loader nodes — it's easy to miss one, leading to errors or incorrect outputs.
• Use the Qwen3-4B GGUF clip model, not the FP8 version — the GGUF version is what the workflow was designed for.
• Always add your trigger word in the extra info node — Joy Caption won't include it automatically.
• Target images with clear, well-lit faces produce the best segmentation and face swap results.
• On 8 GB VRAM setups, the BF-16 model is the minimum — lower than 8 GB requires the NVFP4 variant.