qwen-image-edit-2511-mlx-int4

Pre-quantized MLX weights for Qwen-Image-Edit-2511, for the Swift xocialize/qwen-image-edit-swift package's QwenImageEdit turbo tier. They let the engine load the int4 tier directly instead of loading bf16 (~40 GB peak) and quantizing in-memory.

⚠️ Non-standard format — for the MLXEngine Swift loaders only. These are not standard mlx_lm / mlx_vlm / diffusers-layout repos and will not load with generic MLX tooling. Tensors use the engine's internal module key names and carry custom quantization metadata; they load only via QwenImageEditWeights.loadQuantizedDiT and QwenVLPromptEncoder.load(quantizedTextModelPath:). For a standard, tool-loadable conversion see mlx-community/qwen-image-edit-2511-8bit.

Files

file size contents
qie-2511-dit-int4-mod8.safetensors ~15 GB 20B DiT transformer — attention + feed-forward Linears int4 (group 64), modulation linears int8 (conditioning-critical), top-level projections bf16. Per-layer bits + group size travel in the safetensors metadata.
qie-2511-vl7b-int4.safetensors ~4 GB Qwen2.5-VL-7B text model, int4 (group 64). The (small) vision tower is not included — it loads bf16 from the base snapshot.

Measured footprint (Swift MLXEngine turbo tier, 1024², 4-step DMD)

  • Load peak ~21 GB (no bf16 ever materialized) · resident ~21 GB · inference peak ~25.5 GB (with bf16 VAE).
  • vs ~57 GB bf16 / ~41 GB quantize-after-load peak.

Provenance / license

Derived from Qwen/Qwen-Image-Edit-2511 (Apache-2.0). Quantization: MLX affine, group size 64. The Lightning 4-step DMD LoRA is applied separately at runtime and is not included here.

Downloads last month

-

Downloads are not tracked for this model. How to track
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for xocialize/qwen-image-edit-2511-mlx-int4

Finetuned
(77)
this model