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⚠️ 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.
libquantfunc.so / quantfunc.dll), zero Python model dependencies.Ideogram-4-Series/
├── config.json # canonical per-layer precision map (W4A4)
└── precision-config/
└── ideogram4_a4w4.json # identical copy, named for manual / plugin useconfig.jsonandprecision-config/ideogram4_a4w4.jsonare identical. Both are the W4A4 precision map — pick whichever your workflow expects.
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).ideogram4_a4w4.jsoncuda_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 |
cuda_overhead 399 MB). AUTO_8 picks FP8 on SM89 for better dynamic range on these sensitive projections.cuda_overhead 399 MB).