Name: NL Diffusion Image GGUF
Author: realrebelai

Overview

NL Diffusion Image GGUF brings high-quality image synthesis to local environments by leveraging the GGUF quantization format. For developers, this means a significantly lower memory footprint and the ability to run diffusion processes on consumer-grade hardware without sacrificing substantial fidelity. Unlike standard PyTorch checkpoints, the GGUF implementation allows for faster loading and better integration with llama.cpp-based ecosystems, making it ideal for building edge-AI applications or local creative tools. It is particularly useful for developers needing an Apache-2.0 licensed model for commercial projects where deployment costs and VRAM overhead are primary constraints.

Highlights

GGUF quantization for reduced VRAM and memory overhead
Apache-2.0 license allows flexible commercial integration
Optimized for local deployment on consumer-grade hardware
Fast loading times compared to standard diffusion weights

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("realrebelai/NL-Diffusion-Image_GGUF")
tokenizer = AutoTokenizer.from_pretrained("realrebelai/NL-Diffusion-Image_GGUF")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download realrebelai/NL-Diffusion-Image_GGUF

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download realrebelai/NL-Diffusion-Image_GGUF config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('realrebelai/NL-Diffusion-Image_GGUF')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/realrebelai/NL-Diffusion-Image_GGUF

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/realrebelai/NL-Diffusion-Image_GGUF

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('realrebelai/NL-Diffusion-Image_GGUF')
tokenizer = AutoTokenizer.from_pretrained('realrebelai/NL-Diffusion-Image_GGUF')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model realrebelai/NL-Diffusion-Image_GGUF

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model realrebelai/NL-Diffusion-Image_GGUF README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('realrebelai/NL-Diffusion-Image_GGUF')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/realrebelai/NL-Diffusion-Image_GGUF.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/realrebelai/NL-Diffusion-Image_GGUF.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'realrebelai/NL-Diffusion-Image_GGUF')

Full Documentation

来源: HuggingFace

---
license_name: nvidia-license
license_link: LICENSE
base_model: nvidia/NL-Diffusion-Image
tags:

quantization

sefi

image-generation

---

Rebels NL-Diffusion-Image GGUFs

ComfyUI_Rebels_NLD Custom Nodes

GGUF loader + text-to-image nodes for NVIDIA NL-Diffusion-Image (masked discrete diffusion
LM + IBQ VQ decoder) on consumer hardware. By RealRebelAI.

https://github.com/RealRebelAI/ComfyUI_Rebels_NLD

NODES ARE OPERATIONAL BUT SLOW. Currently working on patches for speed ups. they will run in their current state but i recommend git pulling frequently.

Install

1. Clone into ComfyUI/custom_nodes/.
2. Requires the city96 ComfyUI-GGUF fork in the same custom_nodes/ folder (used for dequant).
3. Put the model files (dropdown-selected, no paths):
- dLM GGUF → ComfyUI/models/unet/
- vqvae (bf16 .safetensors) → ComfyUI/models/vae/
4. The config/tokenizer/modeling code ships in model_assets/

IMPORTANT!

5. model.safetensors file MUST go in "custom_nodes\ComfyUI_Rebels_NLD\model_assets\emu3_vqvae"

https://huggingface.co/nvidia/NL-Diffusion-Image/blob/main/emu3_vqvae/model.safetensors

Nodes

NL-Diffusion dLM Loader (GGUF) — pick gguf_name and vqvae_name from dropdowns, choose device.

NL-Diffusion Text to Image — prompt, size, steps, guidance, temperature, seed → IMAGE.

Notes

The dLM generates discrete token indices; the vqvae decoder turns them into pixels. It is not

a latent VAE — it loads through this pack, not ComfyUI's VAELoader.

Vocab embeddings use row-gather dequant, so the 131k-row tensors never fully materialize.

Vision-tower (image-understanding / edit) weights are left on meta and not needed for t2i.

License

Model is under the NVIDIA One-Way Noncommercial License (research/development only). Quants
inherit those terms — publish as license: other with the upstream terms linked.