---
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, int8, w8a8, ideogram]
---

# Ideogram 4 — INT8 W8A8 (Transformer Lab)

An **INT8 W8A8** post-training quantization of the Ideogram 4 DiT (per-channel int8
weights + per-token dynamic int8 activations).

ℹ️ **Quantized DiT only.** This checkpoint is the DiT (both CFG branches). To generate 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.json`, `safetensors_loader.py`).

## Why INT8
INT8 **holds the FP8 quality ceiling**: on a 200-prompt benchmark the paired same-seed
bootstrap CI for INT8−FP8 includes zero on both Pick and CLIP (statistically
indistinguishable at this sample size), and it beats NF4 by **+1.9 CLIP** (CI excludes
zero). Text rendering stays legible (OCR NED 0.704 vs NF4 0.760).

## Samples
![image (8)](https://cdn-uploads.huggingface.co/production/uploads/6316131329411a6864b13751/1gGu1ZK500Sw4F02Qofil.png)

## Method
Per-channel int8 weights + per-token dynamic int8 activations + SmoothQuant (α=0.5) +
mixed-precision protection of the **top-17 fragility-prone layers** (the FFN
down-projections, ~8% of linears), kept in bf16. See `recipe.json` for the exact module
list and tensor layout.

## Notes
- On-disk **~20.4 GB** — at 8-bit weights this is **FP8-class in size, not smaller than
  NF4** (10.4 GB). Its win is *quality*, not memory.
- Without a fused Ampere INT8 GEMM it runs ~184 s/img (no speed win yet); a custom-kernel
  build for the speedup is planned.

## How to run (self-contained)

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

```bash
# 1) one-time: install the ideogram4 package + download the base components
#    (needs your own access to the GATED base repo ideogram-ai/ideogram-4-fp8)
python download_deps.py

# 2) generate
python usage.py "a poster that says HELLO"
```

Files here:
- `ideogram4-int8-w8a8.safetensors` — the INT8 W8A8 DiT (both CFG branches).
- `safetensors_loader.py` — reconstructs the W8A8 layers + loads them (reference impl).
- `download_deps.py`, `usage.py` — setup + a minimal generation example.
- `recipe.json` — the exact recipe (protected-layer list, tensor layout).

> `safetensors_loader.py` is a **reference**: the math is validated, but the standalone
> loader hasn't been GPU-tested end to end yet — verify before production use. This INT8
> build runs eager (no fused INT8 kernel yet), so it holds FP8 quality but isn't faster.

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