Krea 2 Turbo — RAM 8bit (MLX)
A mixed-precision MLX build of krea/Krea-2-Turbo, produced with the RAM Pipeline by baa.ai. Runs natively on Apple Silicon through mflux — no PyTorch, no MPS fallback.
RAM's quality ceiling build. At the predicted global peak the allocator's saturation gate refuses every upgrade past 8-bit — the probe measured no further SQNR gain above it — so the optimal allocation converges on uniform 8-bit. Highest fidelity of the three.
| Property | Value |
|---|---|
| Transformer size | 13.92 GB (from 25.64 GB bf16 — 1.84× smaller) |
| Total repo size | 22.48 GB (incl. bf16 text encoder + VAE) |
| Average bits | 8.00 |
| Bit distribution | 8-bit × 256 |
| Quantization | affine, group size 64 |
| Format | MLX (mflux Krea 2) |
| Base model | krea/Krea-2-Turbo |
The Qwen3-VL text encoder is deliberately left at bf16 — quantizing it measurably degrades prompt conditioning. The VAE is also left at full precision.
⚠️ Required: this checkpoint needs the bundled loader
This build is uniform 8-bit across the transformer, but the VAE and text encoder are
deliberately left at full precision, which stock mflux's own predicate would not
reproduce. Use the bundled load_krea2_ram.py (with ram_bits.json) so the module
structure matches the stored weights exactly. The loader raises rather than silently
mis-loading.
Usage
pip install "mflux>=0.18,<0.19"
hf download baa-ai/Krea-2-Turbo-RAM-8bit-MLX --local-dir Krea-2-Turbo-RAM-8bit-MLX
# CLI — writes out.png
python load_krea2_ram.py --model Krea-2-Turbo-RAM-8bit-MLX \
--prompt "a photograph of a red fox in a sunlit forest clearing, sharp focus, bokeh" \
--seed 42 --steps 8 --width 1024 --height 1024 --output out.png
# Python
from load_krea2_ram import load_ram_krea2
model = load_ram_krea2("Krea-2-Turbo-RAM-8bit-MLX")
image = model.generate_image(
seed=42,
prompt="a photograph of a red fox in a sunlit forest clearing, sharp focus, bokeh",
num_inference_steps=8, width=1024, height=1024, guidance=1.0,
)
image.image.save("out.png")
Requirements
- Apple Silicon Mac (M1 or later)
- 32 GB+ unified memory recommended
mflux>=0.18,<0.19— earlier versions have no Krea 2 support
Turbo is a timestep-distilled model: use 8 steps and guidance 1.0. More steps will not improve it.
Benchmark results
10 prompts, matched seeds, 1024×1024, 8 steps, guidance 1.0, measured on an M2 Ultra. Every arm quantizes the same 256 transformer linears and differs only in bit allocation, so the comparison isolates allocation policy. LPIPS is perceptual distance from the bf16 reference — lower is better.
| Build | Transformer | LPIPS ↓ | PSNR ↑ | CLIP ↑ | s/image |
|---|---|---|---|---|---|
| Uniform 4-bit | 7.67 GB | 0.2423 | 19.2 | 34.50 | 56 s |
| RAM 9 GB (this family) | 9.19 GB | 0.2076 | 20.6 | 34.41 | 58 s |
| Hand-tuned mixed 4/8* | 9.84 GB | 0.2102 | 20.3 | 34.42 | 57 s |
| RAM 11.4 GB | 11.44 GB | 0.1319 | 24.4 | 34.44 | 59 s |
| RAM 8bit (this family) | 13.92 GB | 0.0630 | 29.2 | 34.22 | 59 s |
| bf16 (reference) | 25.64 GB | ref | ref | 34.35 | 56 s |
* Our reimplementation of the community hand-tuned 4/8 recipe (8-bit down_proj +
first/last-2 attention, 4-bit elsewhere, bf16 text-fusion), not that author's checkpoint.
How to read these numbers — including where RAM does *not* win
- At 9 GB, RAM matches the hand-tuned recipe while being 0.65 GB smaller. RAM wins on 7 of 10 prompts, but a paired t-test gives p = 0.89 — that is not a statistically significant quality difference at n = 10. The honest claim is equal quality, fewer bytes, not better quality.
- At aggressive budgets RAM currently loses. A 7.4 GB RAM build scored LPIPS 0.301 against 0.242 for uniform 4-bit at 7.7 GB — uniform won 10/10 prompts, p = 0.001. RAM's 3-bit assignments hurt at that budget. Do not use RAM below ~9 GB for this model.
- 8-bit is not free. q8 still measures LPIPS 0.063 / PSNR 29.2 dB against bf16, so 8-bit is not perceptually lossless here.
- CLIP is flat everywhere (34.2–34.9, within noise). Quantization changes which image you get more than whether it matches the prompt.
- Quantization buys memory, not speed. All arms ran 56–60 s/image; bf16 was fastest at 55.7 s. Generation is attention-bound and attention math is not quantized.
Sensitivity was measured weights-only (SQNR per tensor per bit width), because a
diffusion transformer has no mlx_lm forward path for RAM's usual activation probe. A
uniform-5-bit control at matched bytes is still pending; these results will be updated
when it lands.
How it was built
RAM probed all 256 transformer linears at 2/3/4/5/6/8 bits, measured per-tensor SQNR, and solved a knapsack allocation against a size budget. The resulting per-layer curve is U-shaped — layers 0 and 27 get ~5.4 bits, mid-stack ~4.7 — which the probe measured rather than being told; no architectural priors were applied. K/V projections landed at 8-bit, Q/O at 6-bit, and the MLP bulk at 4–4.7 bits.
License
Krea 2 is licensed under the Krea 2 Community License Agreement. For more information, visit https://krea.ai/krea-2-licensing.
This is a modified version of the Krea Model — post-training weight quantization only;
no retraining, fine-tuning or distillation. It is not an official Krea product and is
not endorsed by Krea. A copy of the Agreement is included as LICENSE.pdf, and the
required attribution notice as Notice. Your use is subject to that Agreement, including
its Acceptable Use Policy and the commercial-use revenue threshold in Section 2.3.
Quantized by baa.ai
Black Sheep AI Products
Shepherd — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.
Watchman — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.
Learn more at baa.ai — Sovereign AI.
Quantized