MLX-Gen

MLX-Gen is a local image and video generation runtime for Apple Silicon and MLX. It exposes mlxgen for text-to-image, image-to-image, text-to-video, image-to-video, model download, model preparation, SeedVR2 image upscaling, optimized quantized model variants, and application progress callbacks.

Important

MLX-Gen started as a fork of mflux. Most credit for the current codebase goes to Filip Strand and the original mflux contributors. MLX-Gen keeps that foundation and focuses on making local generation predictable for real applications: users download or prepare models before running a job, then use one mlxgen command to generate, inspect supported modes, and compare generated examples and measured results. MLX-Gen contributes by adding tested T2I/I2I routes, Qwen Image Edit 2509/2511 routing and parity fixes, Bonsai Image support, Wan2.2 text-to-video and image-to-video support, model-specific mixed quantization policies, published quantized Hugging Face repos optimized for MLX-Gen, and progress callbacks for apps. The fork exists so AbstractVision and related AbstractFramework projects can move quickly without losing the option to merge useful changes back upstream if that becomes valuable for the wider mflux community.

This screenshot shows AbstractFlow, a visual workflow authoring tool in the AbstractFramework ecosystem. AbstractFlow can orchestrate generative image/video capabilities exposed through AbstractVision and AbstractCore; the same MLX-Gen steps can also be run directly with the command-line examples below.

What It Does

MLX-Gen runs supported Hugging Face source models after you download them locally. It also runs quantized model variants that are published on Hugging Face for MLX-Gen. Some of those variants keep precision-sensitive layers at 8-bit or BF16 instead of blindly quantizing every layer, which is why they are published as MLX-Gen-specific repos. You explicitly download or prepare models first, then generation uses only files already on disk, which suits desktop apps, workflow engines, and long-running local jobs.

The main capabilities are:

• text-to-image generation with Qwen Image, FLUX.2 Klein, Z-Image, ERNIE Image Turbo, Bonsai Image, FIBO, and their optimized quantized variants where available;
• image-to-image modes, including latent img2img, instruction/reference edits, multi-reference edits, and experimental reframe/outpaint workflows where the selected model supports them;
• Wan2.2 text-to-video and image-to-video, including TI2V-5B BF16/q8 packages plus A14B T2V/I2V BF16 and mixed q8/BF16 packages; Wan I2V resolves output size from the source image aspect ratio so inputs are not stretched into a mismatched canvas;
• SeedVR2 image super-resolution through mlxgen upscale, with official 3B/7B source support, published q8/q4 packages, shortest-edge target sizing, and explicit scale factors such as 2x and 3x;
• explicit download and prepare workflows for local MLX-Gen model packages;
• JSON model capability inspection before starting a heavy run;
• experimental LoRA routing and strict adapter application checks, with model-card compatibility preflight when cached adapter metadata is available and exact q8 proof rows for Qwen Image Edit original/2509/2511, Qwen Image 2512, Z-Image Turbo, FLUX.2 Klein 9B edit, ERNIE Image Turbo text-to-image, and all current Wan q8 video routes; the LoRA guide now includes the documented 720p Wan q8-vs-BF16 LightX2V keyframe comparison, readable 41-frame M5 Max progress matrices, same-seed no-LoRA-versus-Lightning A/B sheets, a 240p-versus-480p T2V sweep, and time/RSS tables for T2V and I2V; base Qwen Image remains experimental, and Bonsai LoRA stays fail-closed;
• shared progress events for applications embedding MLX-Gen.

Use mlxgen capabilities --model ... before long image-edit runs. Capability output describes the available route; validation reports and contact sheets describe whether an exact source handle or MLX-Gen optimized package passed a visual release gate. Release evidence should use true handles such as briaai/Fibo-Edit or AbstractFramework/flux.2-klein-9b-8bit, not short aliases.

Install

Install with uv:

uv tool install --upgrade mlx-gen

Or install into an environment:

python -m pip install -U mlx-gen

Check the command surface:

mlxgen --help

First Commands

Download model files explicitly:

mlxgen download --model AbstractFramework/flux.2-klein-9b-8bit

Generate an image:

mlxgen generate \
  --model AbstractFramework/flux.2-klein-9b-8bit \
  --prompt "A cinematic wide shot of a compact sci-fi spaceship resting in deep snow on a frozen alien planet" \
  --width 768 \
  --height 432 \
  --steps 24 \
  --guidance 1.0 \
  --seed 6107 \
  --output spaceship.png

Upscale an image with SeedVR2:

mlxgen download --model AbstractFramework/seedvr2-3b-8bit

mlxgen upscale \
  --model AbstractFramework/seedvr2-3b-8bit \
  --image-path input.png \
  --resolution 2x \
  --softness 0.25 \
  --metadata \
  --output input_2x.png

For SeedVR2, an integer --resolution is the target shorter edge while values such as 2x and 3x are scale factors. Both modes preserve the source aspect ratio. Use --softness 0.25 to 0.5 when the source has visible grain in smooth areas. Small outputs use the untiled VAE path; large outputs automatically use tiled VAE decode, and --vae-tiling also forces tiled encode. See docs/upscaling.md for a reproducible 5x SeedVR2 comparison.

Inspect model capabilities before a run:

mlxgen capabilities --model AbstractFramework/flux.2-klein-9b-8bit

Capabilities are route contracts: they show which tasks, I2I modes, image counts, and options the selected model can dispatch. For release QA evidence on exact packages, use:

mlxgen validation --model AbstractFramework/qwen-image-edit-2509-8bit

LoRA support is experimental. For LoRA work, inspect supports_lora and lora_status in mlxgen capabilities, download the adapter explicitly with mlxgen download, and use an adapter trained for the selected model family. Current exact proof rows cover original Qwen Image Edit, Qwen Image Edit 2509/2511, Qwen Image 2512, Z-Image Turbo, FLUX.2 Klein 9B edit, and ERNIE Image Turbo text-to-image, plus the current Wan q8 public video routes. The LoRA guide also includes the current LightX2V 4-step A14B timing comparison against the practical original Wan profiles, same-seed no-LoRA-versus-Lightning A/B sheets, a 240p-versus-480p T2V sweep, and copy-paste download and T2V/I2V commands for lightx2v/Wan2.2-Lightning, including the stable repo:subdir/file.safetensors form and equivalent absolute local-file usage after download. For example, a FLUX.2-dev LoRA is not accepted for FLUX.2 Klein. See docs/lora.md for the A/B validation method.

Create a local MLX-Gen model package, for example an 8-bit Qwen Image package:

mlxgen prepare \
  --model Qwen/Qwen-Image \
  --path ./models/qwen-image-8bit \
  --quantize 8

mlxgen generate does not download missing files. If something is not cached, MLX-Gen raises a clear DownloadRequiredError with the command to run. A complete local MLX-Gen package at ./models/<repo-name> can also satisfy a matching Hugging Face handle such as AbstractFramework/qwen-image-edit-2511-8bit.

Reproducible Example

The docs include a complete model-backed spaceship workflow:

• T2I: generate a spaceship in the snow.
• I2I edit: turn it into a pencil sketch.
• I2I edit: crash the same spaceship in the snow.
• I2I multi-reference: combine the crash layout and pencil-sketch style.
• T2V A14B: generate a spaceship taking off from a snow planet.
• I2V A14B: animate the generated spaceship taking off from the source image.

See docs/examples/spaceship-snow.md for the exact commands and included assets.

For current image-edit contact sheets, command logs, and model/package status across Qwen Image Edit, Qwen Image Edit 2509/2511, FLUX.2 Klein, and latent I2I models, see docs/edit-capabilities.md. For a plain-language guide to latent img2img, instruction edit, multi-reference composition, generative reframe, and outpaint, see docs/image-edit-modes.md.

Published Models

Quantized and BF16 model variants optimized for MLX-Gen are published under the AbstractFramework organization on Hugging Face. Current published packages include:

FLUX.2 Klein:

• AbstractFramework/flux.2-klein-4b-4bit
• AbstractFramework/flux.2-klein-4b-8bit
• AbstractFramework/flux.2-klein-9b-4bit
• AbstractFramework/flux.2-klein-9b-8bit
• AbstractFramework/flux.2-klein-base-4b-4bit
• AbstractFramework/flux.2-klein-base-4b-8bit
• AbstractFramework/flux.2-klein-base-9b-4bit
• AbstractFramework/flux.2-klein-base-9b-8bit

Qwen Image and Qwen Image Edit:

• AbstractFramework/qwen-image-4bit
• AbstractFramework/qwen-image-8bit
• AbstractFramework/qwen-image-2512-4bit
• AbstractFramework/qwen-image-2512-8bit
• AbstractFramework/qwen-image-edit-4bit
• AbstractFramework/qwen-image-edit-8bit
• AbstractFramework/qwen-image-edit-2509-4bit
• AbstractFramework/qwen-image-edit-2509-8bit
• AbstractFramework/qwen-image-edit-2511-4bit
• AbstractFramework/qwen-image-edit-2511-8bit

Z-Image, ERNIE, and FIBO:

• AbstractFramework/z-image-4bit
• AbstractFramework/z-image-8bit
• AbstractFramework/z-image-turbo-4bit
• AbstractFramework/z-image-turbo-8bit
• AbstractFramework/ernie-image-turbo-4bit
• AbstractFramework/ernie-image-turbo-8bit
• AbstractFramework/fibo-4bit
• AbstractFramework/fibo-8bit

SeedVR2 upscaling:

• AbstractFramework/seedvr2-3b-4bit
• AbstractFramework/seedvr2-3b-8bit
• AbstractFramework/seedvr2-7b-4bit
• AbstractFramework/seedvr2-7b-8bit

SeedVR2 7B can also run from the official ByteDance-Seed/SeedVR2-7B source model or from the published q8/q4 package handles above. See docs/upscaling.md and docs/quantization.md for the validated 7B source/q8/q4 profile.

Wan2.2 video:

• AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16
• AbstractFramework/wan2.2-ti2v-5b-diffusers-8bit
• AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16
• AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit
• AbstractFramework/wan2.2-i2v-a14b-diffusers-bf16
• AbstractFramework/wan2.2-i2v-a14b-diffusers-8bit

Use mlxgen download --model <repo-id> to cache a published model, then pass the repository id to the relevant command: mlxgen generate for image/video generation or mlxgen upscale for SeedVR2 upscaling. See docs/quantization.md for the complete current package matrix with source sizes, optimized package sizes, task coverage, and quantization notes.

For Wan2.2 TI2V-5B, the published BF16 MLX-Gen package is 21.2 GiB versus 31.9 GiB for the upstream source snapshot. It is mainly a smaller reusable source-equivalent package because MLX-Gen already loads Wan transformer/VAE weights at BF16 runtime precision. The published q8 package is 16.9 GiB. In the documented 1280x704 benchmark profile, q8 reduced logical model footprint and MLX allocator peak but did not reduce full-process physical peak memory. Wan TI2V-5B q4 or mixed q4/q8 is not published as a supported package. See the exact benchmark profile in docs/quantization.md.

Wan A14B Measurements

Wan A14B was measured on an Apple M5 Max with 128 GB unified memory. The published-card benchmark uses small, repeatable low-RAM runs and records full-process Darwin physical footprint, RSS, MLX allocator peak, and generation time. Use these values for the listed profiles; memory and runtime scale with resolution, frame count, step count, cache settings, and image-to-video conditioning.

Model	Package	Disk	Physical Peak	Max RSS	MLX Peak	Time	Profile
Wan2.2 T2V-A14B	BF16	64.1 GiB	33.0 GiB	31.8 GiB	27.7 GiB	152.7 s	384x224, 33 frames, 12 steps, 8 fps
Wan2.2 T2V-A14B	mixed q8/BF16	39.5 GiB	20.7 GiB	19.5 GiB	15.5 GiB	154.8 s	384x224, 33 frames, 12 steps, 8 fps
Wan2.2 I2V-A14B	BF16	64.1 GiB	33.7 GiB	31.8 GiB	28.2 GiB	228.2 s	384x384, 33 frames, 12 steps, 8 fps
Wan2.2 I2V-A14B	mixed q8/BF16	39.5 GiB	21.5 GiB	19.6 GiB	15.9 GiB	242.2 s	384x384, 33 frames, 12 steps, 8 fps

In these runs, mixed q8/BF16 reduces disk usage by about 38% versus BF16 MLX-Gen packages and reduces full-process physical peak memory by about 36-37%. It is not documented as a speed improvement. See docs/quantization.md for model-family quantization details and metrics JSON. The 0.18.11 release also validates the published A14B q8 T2V/I2V handles on a 41-frame, 15-step, 480x240-target profile with saved MP4/contact-sheet evidence in the quantization docs.

Ecosystem

MLX-Gen is used as the local Apple Silicon generation backend for:

• AbstractVision, the direct integration layer for abstracting generative image and video capabilities across local and hosted providers;
• AbstractCore, which can expose OpenAI-compatible endpoints backed by AbstractVision providers, including image and video capabilities;
• AbstractFlow, a visual workflow authoring layer that can compose generative image/video nodes alongside other media, text, and agent workflows.

MLX-Gen remains useful as a standalone CLI package, and it is also designed for applications: jobs run from model files already on disk, apps can inspect supported modes before loading weights, and progress callbacks make long runs observable.

Documentation

• Getting started: installation, first runs, SeedVR2 upscaling, and Wan video.
• API and CLI: command surface, router behavior, image-to-image modes, experimental generative reframe, backend-specific outpaint, SeedVR2 sizing, Wan video sizes, capabilities, and Python entry points.
• Image edit modes: what latent img2img, edit-reference, multi-reference, generative reframe, and outpaint mean in practice, with examples.
• Wan video: practical Wan2.2 T2V/I2V sizing and 5-second M5 Max comparison clips.
• Example workflow: reproducible image and video commands.
• Image upscaling: SeedVR2 sizing, published 3B/7B q8/q4 package usage, quality controls, and 5x source/output comparisons.
• Image edit capabilities: image-edit contact sheets, exact model/package status, and command logs.
• Reframe and outpaint: experimental --reframe-padding and --outpaint-padding routes with the mixed June 8 profile plus the current FLUX.2 Klein base source-model proof.
• Model management: download, prepare, and run from local model files.
• Quantization: q8/q4/BF16 policies and measurements.
• Python integration: embedding, progress callbacks, and AbstractVision/AbstractCore notes.
• FAQ: recurring questions, image-to-image mode selection, SeedVR2 sizing, Qwen edit variants, negative prompts, experimental generative reframe, experimental canvas outpaint, Wan resolutions, and usage limits.
• Troubleshooting: common setup and runtime failures.
• Acknowledgements: upstream mflux and model-community credits.

License

MLX-Gen is MIT licensed. Model weights remain governed by their original licenses and access terms.