RealGen-V2 — ComfyUI-ready LoRA for Z-Image

A drop-in ComfyUI build of Yunncheng/RealGen-V2. Same weights as upstream, repackaged so ComfyUI's stock LoraLoader can load it without a custom node. Works on both Z-Image base and Z-Image-Turbo.

Examples

Each row is the same prompt and seed, rendered with and without the LoRA at strength 1.0.

Z-Image (base)

Without LoRA	With RealGen-V2

Z-Image-Turbo

Without LoRA	With RealGen-V2

Note: on Turbo the LoRA still affects the image even though negative prompts don't — CFG=1 disables the negative branch, not the LoRA patch.

Why this repo exists

The upstream release ships the adapter in PEFT format (base_model.model.<path>.lora_A.<adapter>.weight keys, with lora_alpha living separately in adapter_config.json). ComfyUI's stock LoraLoader doesn't understand that layout, so this repo provides:

• the same weights, repackaged with diffusers-style keys (<path>.lora_down.weight, <path>.lora_up.weight) and
• per-module alpha tensors baked into the file so the alpha/rank scaling ComfyUI applies matches what PEFT would have applied at runtime.

No retraining, no quantisation, no surgery beyond key renaming and alpha injection — the math is identical to running the original adapter through PEFT.

Files

File	Purpose
realgen_v2.safetensors	The repackaged LoRA. Drop into ComfyUI/models/loras/.
scripts/convert_realgen_v2.py	The script used to produce it from the upstream PEFT adapter. Re-runnable for transparency.
examples/	Side-by-side renders, with and without the LoRA, on both Z-Image base and Z-Image-Turbo.
LICENSE	Apache 2.0 (matches both RealGen-V2 and Z-Image upstream).

Usage in ComfyUI

1. Download realgen_v2.safetensors and place it in ComfyUI/models/loras/.
2. Build a graph: Load Diffusion Model (Z-Image) → LoraLoader → sampler.
   • Select realgen_v2.safetensors in the loader.
   • Strength 1.0 reproduces the upstream training intent (alpha=128, rank=64 → scale=2.0).
   • Lower (e.g. 0.5–0.8) for a softer effect; the LoRA scales linearly.

That's it — there is no custom node to install.

Reproducing the conversion

If you'd rather convert the upstream weights yourself:

# from a Python env with torch + safetensors + packaging:
python scripts/convert_realgen_v2.py adapter_model.safetensors realgen_v2.safetensors

The script reads lora_alpha from adapter_config.json (sitting next to the adapter), strips the base_model.model. prefix, rewrites lora_A/lora_B → lora_down/lora_up, and writes one <module>.alpha tensor per LoRA module. See the source for the full mapping.

Credits

• Original RealGen-V2 weights: Yunncheng/RealGen-V2
• RealGen training code: yejy53/RealGen (RealGen_v2/)
• Base models: Tongyi-MAI/Z-Image and Tongyi-MAI/Z-Image-Turbo

This repo redistributes the weights under their original Apache 2.0 license; all credit for the LoRA itself belongs to the upstream authors.