---
license: creativeml-openrail-m
base_model: Uminosachi/realisticVisionV51_v51VAE-inpainting
tags:
  - stable-diffusion
  - onnx
  - int8
  - quantized
  - text-to-image
  - mobile
library_name: onnx
pipeline_tag: text-to-image
---

# realvis51-inpaint-int8-onnx

int8-quantized ONNX export of [Uminosachi/realisticVisionV51_v51VAE-inpainting](https://huggingface.co/Uminosachi/realisticVisionV51_v51VAE-inpainting),
built to run **fully on-device** (CPU) on Android phones.

**Category:** Edit — Photoreal inpainting -- fills masked regions cleanly. The first conv is kept in fp32: the 9-channel input mixes a large-range noisy latent with a 0/1 mask, and one shared int8 scale would crush the mask.

## What was changed

This is a **derivative work**. The original weights were:

1. exported from PyTorch to ONNX (via `optimum`), then
2. **dynamically quantized to int8** (`quantize_dynamic`, QInt8 weights) for the
   UNet and text encoder. The VAE is left in fp32 for output quality.

Nothing else was altered — no retraining, no merging. Shrinks a ~4.1 GB fp16
pipeline to ~1.3 GB so it fits comfortably in phone RAM.

## Layout

```
unet/model.onnx (+ .onnx.data)
text_encoder/model.onnx (+ .onnx.data)
vae_decoder/model.onnx
vae_encoder/model.onnx
tokenizer/{vocab.json, merges.txt}
```

## License

Inherited from the source model: **creativeml-openrail-m**. If that is
CreativeML OpenRAIL-M, the Attachment A use-restrictions apply to you as well —
they travel with the weights and cannot be removed by downstream redistribution.
Please read them before use.

Credit for the underlying model belongs entirely to the original author.