QuantFunc/Ideogram-4-Series

QuantFunc

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Ideogram-4-Series

⚠️ Config-only repository — no model weights. This repo contains only a QuantFunc per-layer precision config (precision-config/ideogram4_a4w4.json). It does not contain, mirror, or redistribute any Ideogram model weights. You bring your own officially-obtained Ideogram 4 model; this config only tells the QuantFunc engine how to quantize it at load time, on your own machine.

Powered by the QuantFunc ComfyUI plugin — the fastest diffusion inference engine:

• 🚀 2x–11x speedup over standard BF16/FP16 Python pipelines.
• ⚙️ Native C++/CUDA (libquantfunc.so / quantfunc.dll), zero Python model dependencies.
• 🧩 Universal format adapter — loads diffusers / BFL (Flux) / HF / nunchaku SVDQ layouts directly, no manual conversion.
• 🟢 Full GPU coverage — RTX 20/30/40/50 · A100/H100/H200/B100/B200/GB300 · RTX 6000 Ada / PRO Blackwell (CUDA 12 & 13); native FP4 on Blackwell.

👉 Install the plugin: https://github.com/RealJonathanYip/ComfyUI-QuantFunc

What this repository provides

Just the precision config — no weights:

Ideogram-4-Series/
├── config.json                     # canonical per-layer precision map (W4A4)
└── precision-config/
    └── ideogram4_a4w4.json         # identical copy, named for manual / plugin use

config.json and precision-config/ideogram4_a4w4.json are identical. Both are the W4A4 precision map — pick whichever your workflow expects.

We deliberately do not host Ideogram 4 weights. The QuantFunc Lighting backend does runtime quantization: you load the official weights and they are quantized in-memory at load, so no pre-quantized checkpoint is ever distributed.

How to use

1. Obtain the official Ideogram 4 model yourself in any QuantFunc-supported layout (diffusers, BFL/Flux-style, or HF). Follow Ideogram's official distribution channels and license terms.
2. Install the QuantFunc ComfyUI plugin: https://github.com/RealJonathanYip/ComfyUI-QuantFunc
3. Load the official model through the Build Pipeline node (universal format adapter).
4. Precision config — leave the node on auto detect (it recognizes Ideogram 4 and applies ideogram4_a4w4.json automatically), or point it at this file manually. The Lighting engine then runtime-quantizes the transformer to W4A4 (4-bit heavy GEMMs + 8-bit sensitive projections).

Precision config — ideogram4_a4w4.json

Per-layer precision map (mirrors the Klein-style configs). Measured on a dual-transformer 24 GB card (cuda_overhead 399 MB) to fit and render a coherent, prompt-matching image — with sharper detail than FP16-non-block.

Layer group	Precision	Why
layers.attention.qkv · layers.attention.o	4-bit (AUTO_4 → INT4 on SM89, FP4 on SM120)	self-attention projections; large K/N, quant-robust
layers.feed_forward.w1/w2/w3	4-bit	SwiGLU MLP — largest matrices, primary memory target
input_proj · llm_cond_proj · t_embedding.mlp_in/out · adaln_proj · final_layer.linear	8-bit (AUTO_8 → FP8 on SM89+, INT8 older; W8A8)	sensitive non-block projection GEMMs
layers.adaln_modulation · final_layer.adaln_modulation	FP16	M=1 modulation GEMVs — per-token activation quant collapses conditioning; engine skips them

Net: 170 block GEMMs @ 4-bit · 5 non-block projection GEMMs @ AUTO_8 (FP8 on SM89) · 2 adaLN-modulation GEMVs @ FP16.

Verified coherent on SM89 (INT8 dashboard-run + FP8 CLI-run, each cuda_overhead 399 MB). AUTO_8 picks FP8 on SM89 for better dynamic range on these sensitive projections.

Hardware

• NVIDIA RTX 20-series and above (CUDA 12 & 13). Native FP4 on Blackwell (SM120); INT4 on SM89.
• Fits a 24 GB card with the a4w4 map (measured cuda_overhead 399 MB).

Legal / Attribution

• This repository distributes only the QuantFunc precision-config JSON — our own work, Apache-2.0.
• It contains no Ideogram weights and is not affiliated with, nor endorsed by, Ideogram.
• "Ideogram" is a trademark of its respective owner. You are solely responsible for obtaining the official model and complying with its license and terms of use.

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