🎨 R-Image-Base

Where Z-Image Base Meets the Signature Rebel Aesthetic.


🚨 UPDATE: GGUF Model Now Available!

The GGUF version is officially live. To get it running, follow these steps:

  • Custom Node Required: Download and extract the Rebels_RIB_Nodes folder, Place it inside your custom_nodes directory.
  • Custom Node Required: city96s gguf custom node pack 'ComfyUI-GGUF'. (download in comfyui manager or github)
  • Required Files: You must download the provided text encoder (qwen_3_4b_fp8_mixed) and VAE (ae) if you do not already have them in your text encoder and vae folders.
  • Plug & Play: The node is configured to call all necessary files automatically.
  • Important: Please USE THE PROVIDED WORKFLOW to ensure everything scales and loads correctly.

πŸ“Œ About the Model

R-Image-Base is a merged checkpoint consisting of Z-image Base and a custom selection of LoRAs. It displays a heavy focus on portraits with extreme photorealistic detail and aesthetic enhancement, taking the foundation of Z-Image and pushing it into a high-fidelity space.

βš™οΈ Recommended Settings

For the best results, stick to these parameters:

Setting Recommended Value
Resolution 2048 x 2048
Steps 30 - 50
CFG Scale 4.0 - 5.0

πŸ› οΈ Installation

Option 1: Full Weight Installation (Standard)

Use this if you want the uncompressed, full-fidelity experience.

  • Download: The full weight Checkpoint file (place in checkpoints folder).
  • Workflow: Use the provided JSON bf16 workflow for the best results.

Option 2: GGUF Installation (Optimized)

Use this for faster performance or lower VRAM usage.

  • Download: the zip file 'Rebels_RIB_Nodes'. Place the Rebels_RIB_Nodes folder inside your custom_nodes directory.
  • Requirements: You must also have the text encoder (qwen_3_4b_fp8_mixed) and the VAE (ae) downloaded.
  • Workflow: You must use the provided JSON gguf workflow for the GGUF node to function correctly.

Developed by Rebel AI ```

EXAMPLES:

R-IMAGE_00008_ R-IMAGE_00010_

R-IMAGE_00015_

R-IMAGE_00016_ R-IMAGE_00023_ R-IMAGE_00024_ R-IMAGE_00020_ R-IMAGE_00021_ R-IMAGE_00022_

R-IMAGE_00025_

R-IMAGE_00050_ R-IMAGE_00051_ R-IMAGE_00053_ R-IMAGE_00055_ R-IMAGE_00056_ R-IMAGE_00058_ R-IMAGE_00030_ R-IMAGE_00032_ R-IMAGE_00034_ R-IMAGE_00037_ R-IMAGE_00038_ R-IMAGE_00040_ R-IMAGE_00041_ R-IMAGE_00042_ R-IMAGE_00044_ R-IMAGE_00045_ R-IMAGE_00048_ R-IMAGE_00049_

R-IMAGE_00063_ R-IMAGE_00060_ R-IMAGE_00061_ R-IMAGE_00062_

R-IMAGE_00068_ R-IMAGE_00069_

R-IMAGE_00081_ R-IMAGE_00082_ R-IMAGE_00083_ R-IMAGE_00070_ R-IMAGE_00074_ R-IMAGE_00075_ R-IMAGE_00076_ R-IMAGE_00077_ R-IMAGE_00078_ R-IMAGE_00079_ R-IMAGE_00080_

Model Description

This is a merged checkpoint of Z-image Base and 2 of my own custom trained LoRAs. values are what i would consider perfect for any image generation and the loras were trained to be detail enhancing.

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