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license: openrail++
tags:
- stable-diffusion-xl
- realvisxl
- lightning
- qualcomm
- hexagon
- qnn
- npu
- text-to-image
- tokforge
library_name: qnn
pipeline_tag: text-to-image
inference: false
---
# TokForge — RealVisXL V4.0 Lightning (Qualcomm Hexagon NPU)
**RealVisXL V4.0 Lightning** image generation for the **Qualcomm Hexagon NPU (HTP)**, packaged for
**on-device** image generation in the **TokForge** Android app (`dev.tokforge`). This is the
higher-quality **1024×1024** "Faithful" tier alongside the SD1.5 NPU bundle.
vc410: this repo replaces the previous **SDXL-Turbo** bins (few-step distillation gave fused heads /
doubled bodies / extra limbs) with **RealVisXL V4.0 Lightning**, which is adversarially distilled for
**deterministic few-step Euler** and renders clean, photoreal people. The W8A16 NPU quant is
unchanged — the checkpoint was the cure.
The model is quantized to **W8A16** (8-bit weights, 16-bit activations) and compiled to **QNN HTP
context binaries** that run on the phone's Hexagon DSP. The pipeline uses **fp16 text encoders** and a
**TAESDXL** tiny-VAE decoder, runs **6-step EulerDiscrete**, and is **guidance-free** (one UNet pass
per step).
## Based on
[`SG161222/RealVisXL_V4.0_Lightning`](https://huggingface.co/SG161222/RealVisXL_V4.0_Lightning) —
CreativeML OpenRAIL++-M license (commercial use permitted, no revenue cap).
## Format
**QNN HTP context binaries** (W8A16), native **1024px** (128×128 latent). These are compiled for the
Hexagon DSP and are **not** a portable format like GGUF. The repo ships **native sets for V73 / V75 /
V79 / V81**; the app reads the device Hexagon arch (`dsp_arch`) and downloads the matching set.
Forward-compatibility (a lower-arch binary also runs on a higher-arch DSP) still applies as a
fallback. Device-verified clean on **V75** (Lenovo SM8650) and **V81** (RedMagic SM8850).
## Pipeline
| Stage | File |
|-------|------|
| CLIP-L text encoder (fp16) | `<arch>/text_encoder_1_fp16.bin` |
| OpenCLIP-bigG text encoder (fp16) | `<arch>/text_encoder_2_fp16.bin` |
| Combined-emb MLP (host CPU) | `<arch>/sdxl_emb_mlp.bin` |
| UNet (W8A16, DSP) | `<arch>/unet.bin` |
| TAESDXL tiny-VAE (CPU MNN) | `<arch>/taesdxl_decoder.mnn` |
| Dual CLIP BPE tokenizers | `<arch>/tokenizer/`, `<arch>/tokenizer_2/` |
Scheduler: **EulerDiscrete** (deterministic), trailing spacing, epsilon prediction, **6 steps**,
guidance_scale 0. Runs via the license-clean `libsdxl_qnn_driver` in the TokForge app.
See `manifest.json` for the per-arch file list, sizes, and md5 checksums.
|