SenseNova-U1.5-8B-MoT-8step-4bit

Apple Silicon (MLX) artifact of SenseNova-U1.5-8B-MoT — SenseTime's unified T2I + editing + VQA flagship (NEO-unify Mixture-of-Transformers, pixel-space rectified flow, no VAE).

This artifact is the official 8-step distillation LoRA (merged as above) plus 4-bit (group 64) stream quantization. Quantization: group size 64, 4-bit (mlx convention quantization block in config.json).

Runtime: sensenova-u1-swift (MLX-Swift). Weights are stored in the runtime's key layout (NHWC convs; quantized linears as weight/scales/biases) and load with zero conversion transient — peak load memory ≈ resident.

Performance (M5 Max)

peak 14.8 GB · 1024² in 3.2 s · 2048² in 15.8 s

Use it for: the smallest, fastest tier — fits 16 GB-class Macs. Note: 4-bit weight quantization on a diffusion path measurably perturbs the denoise trajectory: at a fixed seed it produces a different but equally valid image (cos ~0.92 vs bf16). Prefer the 8-bit tier when bf16-faithful output matters.

Quick start

git clone https://github.com/xocialize/sensenova-u1-swift && cd sensenova-u1-swift && swift build -c release
hf download mlx-community/SenseNova-U1.5-8B-MoT-8step-4bit --local-dir SenseNova-U1.5-8B-MoT-8step-4bit
.build/release/sensenova-cli --weights SenseNova-U1.5-8B-MoT-8step-4bit \
  --prompt "A cinematic mountain lake at sunrise, realistic photography." \
  --width 1024 --height 1024 --steps 8 --cfg 1.0 --out out.npy

The runtime also does instruction editing (--edit-image), VQA (--vqa), and <think> reasoning mode (--think) — see the repository README for the full surface and the parity report (component parity < 1e-4; e2e per-pass cosine 0.999+ vs the reference PyTorch implementation).

Provenance & license

• Upstream: sensenova/SenseNova-U1.5-8B-MoT @ 07d76f6, Apache-2.0, by SenseTime / SenseNova — paper · reference implementation.
• This repository is a format conversion (official-LoRA merge + bf16 cast + 4-bit group-64 quantization) of the upstream checkpoint, redistributed under the same Apache-2.0 license with modifications noted here. All credit for the model to the SenseNova team.
• tokenizer.json is generated from the upstream vocab.json/merges.txt (byte-identical tokenization, verified against reference ids).