---
license: other
license_name: ideogram-4-non-commercial
license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8
base_model: ideogram-ai/ideogram-4-fp8
pipeline_tag: text-to-image
tags: [text-to-image, diffusion, flow-matching, quantization, gguf, q8_0, ideogram]
---

# Ideogram 4 — GGUF Q8_0 (Transformer Lab)

A **GGUF Q8_0** (8.5 bits/weight) quantization of the Ideogram 4 DiT.

> **Note:** this checkpoint is the **quantized DiT only** (both CFG branches). To run it you also need the **Qwen3-VL text encoder and VAE** from the base repo [`ideogram-ai/ideogram-4-fp8`](https://huggingface.co/ideogram-ai/ideogram-4-fp8) and the custom inference code at [`github.com/ideogram-oss/ideogram4`](https://github.com/ideogram-oss/ideogram4). The quantization recipe and loader are included **in this repo** (`recipe-q8_0.json`, `gguf_loader.py`).

## Why this one
Q8_0 is **quality-neutral** vs the FP8 reference (Pick 18.71 vs ceiling 18.71) — a clean,
near-lossless 8-bit GGUF at **19.7 GB**.

## Method
Weight-only GGUF Q8_0 (round-to-nearest) of the DiT linears; non-linear tensors kept F16.

## Numbers
- Quality-neutral vs FP8 on a 50-prompt slice. Latency ~176 s/img (48 steps, 1024², RTX 3090).

## How to run (self-contained)

Everything you need is in this repo. The GGUF is the **quantized DiT only**, so step 1
fetches the text encoder + VAE + the inference package.

```bash
python download_deps.py            # one-time (needs gated access to ideogram-ai/ideogram-4-fp8)
python usage.py "a poster that says HELLO"
```

Files here: `ideogram4-q8_0.gguf` (the Q8_0 DiT), `gguf_loader.py` (dequant + load, reference),
`download_deps.py`, `usage.py`, `recipe-q8_0.json`.

> `gguf_loader.py` is a **reference** (dequant math validated; standalone loader not yet
> GPU-tested). This is **not** a llama.cpp / stable-diffusion.cpp file — it loads only via
> this PyTorch path + the `ideogram4` pipeline.

## License
Derived from Ideogram 4 under its **non-commercial, research-only** license. See `LICENSE`.