---
library_name: onnx
license: apache-2.0
tags:
  - foundation
  - amd
  - rocm
  - image-to-image
pipeline_tag: image-to-image
---

![](https://huggingface.co/AMD-PAVS-AI/esrgan/resolve/main/esrgan.png)

# ESRGAN: Optimized for AMD ROCm

ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) reconstructs a high-resolution image from a low-resolution input, upscaling 4× (250×250 → 1000×1000). This repository packages inference for image super-resolution using **ONNX Runtime**, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs, CPUs, and NPUs.

This is based on the implementation of ESRGAN found [here](https://arxiv.org/abs/1809.00219).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [esrgan AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/esrgan) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline).

---

## Task Overview

**Task:** Image super-resolution (4× upscaling)

**Dataset:** DIV2K validation set (100 HR images; 20 center-cropped 1000×1000 HR / 250×250 LR pairs prepared by default)

**Output metrics:** PSNR (dB), SSIM, latency/throughput

> **Backend note:** The shipped model is an INT8 QDQ-quantized ONNX model with static NHWC shapes for cross-device compatibility; benchmark/profile precision flags select the execution/quantization path applied at run time.

---

## AMD ROCm Optimization

This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs and AMD Ryzen AI NPUs. Key points:

- Validated backends: **ONNX Runtime** across CPU (FP32/INT8), GPU (MIGraphX execution provider — FP32/FP16/INT8), and NPU (VitisAI execution provider, auto-quantized internally).
- No code changes required versus the upstream ESRGAN implementation — only environment/runtime configuration differs.
- GPU and NPU both degrade output quality relative to CPU on this particular model — see Accuracy Pipeline for measured results.

| Runtime | Precision | Backend | Hardware | Notes |
|---|---|---|---|---|
| ONNX Runtime | FP32 / INT8 | CPU Execution Provider | AMD CPU | Reference quality path |
| ONNX Runtime | FP32 / FP16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | Quality degrades vs. CPU on this model |
| ONNX Runtime | Auto | VitisAI Execution Provider | AMD Ryzen AI NPU | Auto-quantized internally; quality degrades further |

---

## Getting Started

For setup instructions, evaluation scripts, and custom configuration options, see the [esrgan on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/esrgan).

---

## Model Details

**Model Type:** Generative adversarial network for image super-resolution

**Base Model:** ESRGAN (INT8 QDQ-quantized ONNX export)

**Model Stats:**
- Input: `(1, 250, 250, 3)` float32, NHWC, normalized to [0, 1]
- Output: `(1, 1000, 1000, 3)` float32, NHWC (4× upscaled)
- Precision tested: FP32, FP16, INT8 (shipped model is INT8-QDQ)

---

## Accuracy Pipeline

Higher is better for both metrics — they measure how closely the upscaled image matches the ground truth. PSNR has no fixed ceiling (an exact match is infinite dB; typical super-resolution scores fall in the ~20–35 dB range), while SSIM is bounded in [0, 1] where 1.0 is a perfect structural match.

### Metrics Explained

| Metric | Description |
|--------|-------------|
| PSNR (dB) | Peak Signal-to-Noise Ratio — a pixel-level fidelity measure derived from mean squared error. Higher means less per-pixel distortion, but can rate a blurry-but-aligned image well even when fine texture is lost — which is why it is paired with SSIM. |
| SSIM | Structural Similarity Index — compares local luminance, contrast, and structure rather than raw pixel error. Higher means the output preserves edges and texture the way a human viewer perceives structure. |

### Accuracy Results

**Full Dataset Evaluation (DIV2K validation, 20 image pairs, INT8-QDQ model)** — filled from `evaluation_results/eval_results_<device>_int8.json`; run `make metrics` to refresh:

<!-- accuracy-table-start -->
| Device | Precision | PSNR (dB) | SSIM | Images |
|--------|-----------|-----------|------|--------|
| CPU | INT8 | 22.68 | 0.7152 | 20 |
| GPU | INT8 | 11.29 | 0.1701 | 20 |
| NPU | INT8 | 5.06 | 0.1775 | 20 |
<!-- accuracy-table-end -->

---

## Dig Deeper

Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?

📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/esrgan)**

The GitHub repository includes:
- Benchmark, profile, and evaluation Makefile targets for CPU, GPU, and NPU
- DIV2K dataset staging and PSNR/SSIM evaluation pipeline
- INT8-QDQ ONNX export used across all device targets
- Benchmarking and reproduction instructions