Core AI model zoo

FLUX.2 klein 4B — Core AI

2026-10-10 re-export, two folders. macos-fp16/ and macos-int8/ of the HF repo were exported with apple/coreai-models 97a14be and load with its diffusion pipeline. The 2026-07-20 export that sat at the repo root (int4, plus the in-context edit transformers) was removed from main on 2026-10-10: current apple/coreai-models stops on it with “unsupported metadata_version”, and no app reads it since apps/CoreAIImageGen moved to the two folders. It is still there at revision 216f2841.

Black Forest Labs’ FLUX.2 [klein] 4B converted to Core AI for image generation on a Mac (macOS 27). It runs on Apple’s diffusion pipeline in apple/coreai-models.

FLUX.2 [klein] is step-distilled: 4 denoising steps at guidance 1.0 give a 1024×1024 image. It pairs a flow-matching diffusion transformer (DiT, 25 blocks) with a Qwen3 text encoder.

Mac only. Both folders are --platform macOS exports. The earlier int4 export already peaked at about 6.5 GB on an iPhone 17 Pro, over the ~6.1 GB a 12 GB iPhone gives one app (apps/CoreAIImageGen).

Files

Folder	Weights	Runs	Files	Bytes
`macos-fp16/`	fp16, one asset per graph	text-to-image	19	14,159,882,995
`macos-int8/`	int8 per-block 32 on the text encoder and the transformer, fp16 VAEs	text-to-image, image-to-image	25	7,777,120,667
revision `216f2841` and earlier (2026-07-20; removed from `main` 2026-10-10)	int4 per-block 32, plus the in-context edit transformers	no app	34	10,854,216,650

Text-to-image reads 16 files of a folder: 14,090,986,305 bytes from macos-fp16/, 7,540,032,209 bytes from macos-int8/. The VAE encoders and the half-size VAEs are only for image-to-image and tiled decoding.

macos-fp16/ leaves out the image-to-image transformer (Transformer_img2img_full.aimodel, 7.75 GB). Image-to-image with it stops with “this bundle has no img2img transformer”. macos-int8/ has one transformer with all eight entrypoints, image-to-image included.

Each folder has a MANIFEST.json: the size and SHA-256 of every file, the export command, and the gate result.

Usage

Sample apps

CoreAIImageGenMac (coreai-samples) runs macos-fp16/ or macos-int8/ on unmodified apple/coreai-models 97a14be: pick one, press Download & Load, type a prompt, press Generate.
apps/CoreAIImageGen runs macos-int8/ (text-to-image and the Edit tab = the stock one-reference image-to-image) or macos-fp16/ on apple/coreai-models 97a14be, beside GLM-Image and Z-Image-Turbo.

Swift

Add apple/coreai-models at commit 97a14be as a Swift package (product CoreAIDiffusion). folder is a downloaded macos-fp16/ or macos-int8/.

import CoreAIDiffusionPipeline

let pipeline = try await FlowTransformerPipeline(from: folder, mode: .auto)
try await pipeline.loadResources()
let configuration = PipelineConfiguration(
    prompt: "a red bicycle leaning against a blue wooden door", seed: 42,
    stepCount: 4, guidanceScale: 1.0, guidanceMode: .distilled, lazyModelLoading: false)
let result = try await pipeline.generateImages(configuration: configuration) { _ in true }
let image = result.images.first  // CGImage, 1024×1024

Command line

apple/coreai-models’ own runner, from a checkout at 97a14be:

swift run -c release diffusion-runner --model path/to/macos-fp16 \
  --prompt "a red bicycle leaning against a blue wooden door" --steps 4 --seed 42 --output bicycle.png

How it was converted

Apple’s stock exporter, no zoo code (recipe.toml: flux2-klein-4b-fp16, flux2-klein-4b-int8):

git clone https://github.com/apple/coreai-models && cd coreai-models
git checkout 97a14be40bb1c5b95badb08b8532530443d62d26

macos-fp16/ is exports/fp16/FLUX.2-klein-4B/ without Transformer_img2img_full.aimodel:

uv run coreai.diffusion.export flux2-klein-4b --platform macOS \
  --compression none --single-function --resolution 1024 --output-dir exports/fp16

macos-int8/ is exports/int8/FLUX.2-klein-4B/, made with the exporter’s own 4bit preset changed to dtype int8:

cat > int8-block32.json <<'EOF'
{"execution_mode": "eager",
 "global_config": {"op_state_spec": {"weight": {"dtype": "int8", "qscheme": "symmetric_with_clipping", "granularity": {"type": "per_block", "block_size": 32}}}, "op_input_spec": null, "op_output_spec": null},
 "module_type_configs": {"diffusers.models.normalization.RMSNorm": null, "transformers.models.qwen3.modeling_qwen3.Qwen3RMSNorm": null, "transformers.models.gemma2.modeling_gemma2.Gemma2RMSNorm": null, "transformers.models.umt5.modeling_umt5.UMT5LayerNorm": null}}
EOF
uv run coreai.diffusion.export flux2-klein-4b --platform macOS \
  --compression "$(cat int8-block32.json)" --output-dir exports/int8

--compression takes a preset name or a JSON string, not a file path. Give each export its own --output-dir: the exporter skips any asset that already exists in its folder, so a second export there keeps the first one’s weights. The source was black-forest-labs/FLUX.2-klein-4B at e7b7dc27. The root files came from the same CLI with its defaults in 2026-07 (int4), plus the fork’s edit transformers (flux2-klein-4b-edit in recipe.toml).

Measured

M4 Max Mac Studio (128 GB), macOS 27.0 (26A428), apple/coreai-models 97a14be, GPU, 2026-10-10. Each export folder was opened in CoreAIImageGenMac with Local…: 1024×1024, 4 steps, guidance 1.0, seed 42, two prompts. “Image” is the median of 4 runs after a relaunch, with no other GPU work running.

Parity is checked per component against fp32 PyTorch (diffusers’ Flux2KleinPipeline through apple/coreai-models’ own export wrappers), on the inputs the Swift pipeline builds: (i) text encoder, mean cosine over the 512 tokens, 4 prompts; (ii) transformer, cosine of the first step’s output; (iii) VAE decoder, PSNR of the 8-bit image. The bar is 0.999 / 0.999 / 35 dB without quantization and 0.99 / 0.99 / 35 dB with it. The last column compares the seed-42 image with the fp32 diffusers image.

Weights	Image (s)	First load (s)	Load after relaunch (s)	Peak memory (GiB)	(i) / (ii) / (iii)	Parity	Image vs fp32 (dB)
fp16 (`macos-fp16/`)	10.88	27.9	2.1	9.2–9.4	0.99966–0.99995 / 0.999987 / 65.5 dB	PASS (0.999)	31.0
int8 per-block 32 (`macos-int8/`)	11.60	20.4	1.7–1.8	10.8–10.9	0.9928–0.9948 / 0.99981 / 65.5 dB	PASS (0.99)	23.3
fp16 text encoder + int8 transformer (not published)	11.59	11.2 ¹	1.9	10.7–10.8	0.99966–0.99995 / 0.99981 / 65.5 dB	PASS (0.99)	29.3
int4 per-block 32 (the exporter’s default for this model)	11.02	6.7	1.1	10.7–10.8	0.65–0.75 / 0.9675 / 65.5 dB	FAIL	15.9

¹ Its files were already in memory from a discarded run 70 s earlier; the other first loads read them from disk.

The int4 form draws a picture, but not the one fp32 draws. Its seed-42 image is 15.9 dB from the fp32 image, and the layout changes: the door and the bicycle move. The quantized text encoder loses the padding positions that the transformer reads (cosine 0.64–0.72 there). That is why the new folders are fp16 and int8.

fp16 is exported with --single-function. Without it, Core AI specializes the unquantized transformer once per entrypoint: 62.0 GB of cache and a 243 s first load. Everything runs on the GPU. Compiling the transformer, the text encoder or the VAE decoder for the Neural Engine fails (ANECCompileOffline() … CompilationFailure).

The text encoder

The text encoder is the Qwen3 model inside the FLUX.2 klein repo: hidden size 2560, 36 layers, the shape of Qwen3-4B. Earlier versions of this card said 8B; that was wrong. The pipeline reads hidden states 9, 18 and 27, so the export drops the last 9 layers.

In-context editing (revision `216f2841`, 2026-07-20)

Revision 216f2841 and earlier have Transformer_edit.aimodel, Transformer_edit_512.aimodel and Transformer_edit_2ref.aimodel for FLUX.2’s in-context editing. You give a reference image and an instruction — “add a red wizard hat, keep everything else the same” — and only the instructed change is applied while the subject, pose, and background are preserved. This is different from strength-based image-to-image (SDEdit), which re-renders the whole frame.

It is the same DiT graph exported at a longer sequence: the output latent (time index T=0) concatenated with the reference image’s latent tokens (T=10), so the transformer attends to the reference while denoising the output. Transformer_edit_2ref.aimodel takes two references (T=10, T=20). Running them needs a runtime that drives this path: apps/CoreAIImageGen ran the john-rocky/coreai-models fork (flux2-in-context-edit, bc48d3d) with Flux2Pipeline.editImages until 2026-10-10; its Edit tab now runs the stock one-reference image-to-image instead. These files are int4 and use the 2026-07-20 layout, so apple/coreai-models 97a14be does not load them. Mechanism: knowledge/flux2-in-context-editing.md.

int4, ~25 s for a 1024 edit and ~43 s for a two-reference edit on a Mac GPU (4 steps, guidance 1.0; numbers from the earlier card, not measured again).

License

Apache 2.0, inherited from the base model black-forest-labs/FLUX.2-klein-4B. The converted weights are redistributed under the same terms, with attribution to Black Forest Labs.

⬇️ Download: 🤗 mlboydaisuke/FLUX.2-klein-4B-CoreAI — the model page carries the same text. Reproduction: recipe.toml — flux2-klein-4b-fp16 and flux2-klein-4b-int8 are verified (the gate above); the root files’ flux2-klein-4b records the 2026-07 run, and flux2-klein-4b-edit is unverified (see ../_INVENTORY.md, “Needs owner input”).

Port notes: knowledge/flux2-klein-reexport.md.

This site is open source. Improve this page.