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QuantFunc/Ideogram-4-Series

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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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](https://github.com/RealJonathanYip/ComfyUI-QuantFunc) — 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.

Community

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