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Ideogram v4 distilled for Apple Silicon (mflux)
Two single-branch distillations of the Ideogram v4 conditional DiT (9.3B, fp8), targeting local inference on Apple Silicon via mflux:
cfg-fold-12step/— classifier-free guidance folded into the conditional transformer (LoRA self-distillation on guided dual-DiT trajectories, then fused with per-row fp8 requantization). Halves every step: 24→12 forwards at 12 steps.step-fold-6step/— the CFG-fold further distilled 12→6 steps on compiled teacher trajectories (6-step grid = the even nodes of the 12-step grid, proven exact).
Only the derivative conditional transformer is redistributed here. Text encoder (Qwen3-VL-8B), VAE (FLUX.2), scheduler and the unused unconditional transformer come from the gated upstream ideogram-ai/ideogram-4-fp8 release, which you must accept separately. An assembly script in the companion GitHub repo links both into a runnable mflux model directory.
Results (M4 Pro, 48GB, 800×992, warm)
| model | forwards | e2e | notes |
|---|---|---|---|
| stock dual-DiT, 12 steps | 24 | ~250s | baseline |
| cfg-fold-12step | 12 | ~135s | seed-compatible with baseline character |
| step-fold-6step | 6 | ~86s | ≈ fal ideogram-v4-instant (8 fwd) wall-clock, −25% compute |
Character notes from blind A/Bs: the folds preserve the base model's filmic/atmospheric range (dragged-shutter motion, halation, editorial grades); fal's official instant distill reads crisper/more commercial on the same briefs. The 6-step variant is style-faithful but recomposes at a fixed seed relative to 12-step.
An additional measured property of single-branch operation: the dual-DiT's seed-dependent refusal placards (which trigger on many harmless prompts at production resolutions) do not occur in either fold, consistent with fal's official single-branch distillation. Nothing about the model's content training was modified; users remain bound by Ideogram's Acceptable Use Policy.
How to use
See the companion GitHub repository for download_assemble.py, the guidance-1.0 cadence
runner, prompt-schema notes and the benchmark harness. Minimal facts: run with
guidance 1.0 (single branch); 6-step preset mu 0.5, std 1.75; dimensions divisible
by 16; structured-JSON prompts.
Training / distillation procedure
- CFG-fold: rank-32+ LoRA on the conditional DiT, trained to match guided dual-DiT
outputs on 56 self-generated caption briefs (text-weighted loss), then fused into the
fp8 weights via per-row requantization (
nscale = rowmax|W′|/448). - Step-fold: teacher trajectories captured through the compiled predictor (eager capture drifts — SSIM 0.96 by step 12 vs 1.0000 compiled), student trained on trajectory states at the 6-step grid nodes, encoder-resident encode-per-visit.
- Verification: bit-deterministic golden renders (SSIM 1.0000), fused-vs-adapter equivalence, text-rendering checks, refusal-rate matrix, speed ladder.
No images beyond the base model's own outputs were used for these distillations.
Limitations & risks
Non-commercial only (license flows down). Baked guidance (no CFG sweeps). 6-step recomposition vs 12-step. Incidental background text unreliable (as in the base model). fp8 weights: Apple-Silicon-tuned; CUDA users should prefer fal's official release.
Attribution
“Ideogram 4 is provided under and subject to the Ideogram Non-Commercial Model Agreement available at https://github.com/ideogram-oss/ideogram-4/model_licenses/LICENSE-IDEOGRAM-4-NON-COMMERCIAL. All rights reserved. Copyright © Ideogram, Inc.” — see NOTICE. Distillations by Felix Brener (2026); modified files are enumerated in MODIFICATIONS.md. Not an official Ideogram product; not endorsed, approved or validated by Ideogram, Inc.
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Base model
ideogram-ai/ideogram-4-fp8