---
library_name: executorch
display_name: ESRGAN x4 INT8 — ExecuTorch + XNNPACK
license: apache-2.0
base_model: kadirnar/RRDB_PSNR_x4
base_model_relation: quantized
tags:
  - image-to-image
  - esrgan
  - int8
  - quantized
  - xnnpack
  - arm
  - executorch
  - edge-ai
  - urban100
  - super-resolution
pipeline_tag: image-to-image
datasets:
  - urban100
metrics:
  - psnr
model-index:
  - name: esrgan-int8-xnnpack-executorch-graviton-g4
    results:
      - task:
          type: image-to-image
          name: Super Resolution
        dataset:
          type: urban100
          name: Urban100
          split: validation
          args:
            evaluation_samples: 100
        metrics:
          - type: psnr
            value: 26.81
            name: PSNR (dB)
---

# ESRGAN x4 INT8 (ExecuTorch + XNNPACK)

This is an INT8-quantized version of [ESRGAN RRDBNet](https://github.com/xinntao/ESRGAN) optimized for edge deployment on ARM devices using [ExecuTorch](https://github.com/pytorch/executorch) with the XNNPACK backend. The model was quantized using PT2E static symmetric per-channel quantization and exported to the `.pte` format for efficient inference on ARM Cortex-A processors (AWS Graviton, mobile ARM, embedded).

The PSNR-oriented weights (`RRDB_PSNR_x4.pth`) from [kadirnar/RRDB_PSNR_x4](https://huggingface.co/kadirnar/RRDB_PSNR_x4) are used — the L1-trained high-PSNR variant whose metrics are directly comparable to standard SR benchmark figures.

## Key Highlights

Compared to the FP32 baseline:

- **2.76x smaller** — 64.07 MB to 23.18 MB
- **2.44x faster** — 1763 ms to 724 ms on AWS Graviton (Neoverse-V2)
- **Minimal quality loss** — −0.22 dB PSNR on Urban100

## Model Details

### Model Description

Quantized version of ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) with a 23-block RRDBNet backbone. The model performs 4x bicubic super-resolution on urban scene images, optimized for efficient edge inference via INT8 quantization and the ExecuTorch runtime.

- **Developed by:** Xintao Wang et al. (ESRGAN), Marvik AI (quantization & optimization)
- **Model type:** Super Resolution (4x upscaling)
- **License:** Apache-2.0
- **Base model:** [ESRGAN RRDBNet](https://github.com/xinntao/ESRGAN) — quantized, not finetuned

### Model Sources

- **Repository:** https://github.com/xinntao/ESRGAN
- **Pretrained weights:** https://huggingface.co/kadirnar/RRDB_PSNR_x4

## How to Get Started with the Model

### Install dependencies

```bash
pip install executorch torch torchvision pillow numpy
```

### Download the model

```python
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Arm/esrgan-int8-xnnpack-executorch-graviton-g4",
    filename="esrgan-x4-int8-executorch.pte",
)
```

### Run inference

```bash
python example.py
```

Or use the core inference loop directly:

```python
from executorch.runtime import Runtime
from PIL import Image
from torchvision import transforms

# Load model
runtime = Runtime.get()
program = runtime.load_program("esrgan-x4-int8-executorch.pte")
method = program.load_method("forward")

# Preprocess (no normalization — model expects raw [0, 1] values)
image = Image.open("input.jpg").convert("RGB")
to_tensor = transforms.ToTensor()
input_tensor = to_tensor(image).unsqueeze(0)  # [1, 3, H, W]

# Run a single 128x128 tile
tile = input_tensor[:, :, :128, :128]
outputs = method.execute([tile])
# outputs[0] shape: [1, 3, 512, 512] — the 4x super-resolved tile
# See example.py for full tiled inference on arbitrary-size images
```

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

Evaluated on 100 images from Urban100 (validation split). Urban100 is a standard super-resolution benchmark consisting of urban scenes with repetitive structures, specifically chosen to challenge SR models on fine detail reconstruction.

#### Metrics

- **PSNR (Peak Signal-to-Noise Ratio, dB)** — measures pixel-level fidelity between the super-resolved output and the ground-truth high-resolution image; higher is better
- **SSIM (Structural Similarity Index)** — measures perceived structural similarity; higher is better

### Results

#### Quality

| Metric | FP32 (Original) | INT8 (Optimized) | Delta |
|:---|:---:|:---:|:---:|
| PSNR (dB) | 27.03 | 26.81 | −0.22 dB |
| SSIM | 0.8176 | 0.8109 | −0.007 |

#### Efficiency

| Metric | FP32 (Original) | INT8 (Optimized) | Improvement |
|:---|:---:|:---:|:---:|
| Model Size (.pte) | 64.07 MB | 23.18 MB | **2.76x smaller** |
| Graviton Latency (mean) | 1763 ms | 724 ms | **2.44x faster** |
| Graviton Latency (p50) | 1763 ms | 704 ms | **2.50x faster** |
| Graviton Latency (p90) | 1765 ms | 780 ms | **2.26x faster** |
| Graviton Cold Start | 66.3 ms | 36.7 ms | **1.81x faster** |

*Latency measured on AWS Graviton G4 (Neoverse-V2, 16 cores) with ExecuTorch 1.1.0, XNNPACK + KleidiAI backend, batch size 1, 10 warmup runs + 100 measurement runs.*

## Technical Specifications

### Objective

4x single-image super-resolution from bicubic-degraded low-resolution inputs, targeting urban scenes with repetitive structures.

### Quantization

- **Method**: PT2E Static Quantization via XNNPACKQuantizer
- **Precision**: INT8 symmetric, per-channel
- **Backend**: XNNPACK
- **Layers kept in FP32**: upsampling layers (`upconv1`, `upconv2`, `HRconv`, `conv_last`) and the first 3 RRDB body blocks (`body.0`–`body.2`) to preserve output fidelity
- **Calibration**: 100 images from Urban100 (random subset, patch mode)

### Export Pipeline

1. Load pretrained FP32 RRDBNet with PSNR-oriented weights from HuggingFace (`kadirnar/RRDB_PSNR_x4`)
2. Capture model graph via `torch.export` at fixed 128×128 tile size
3. Insert quantization observers (XNNPACKQuantizer, skipping upsampling + first 3 body blocks)
4. Calibrate with 100 Urban100 patches
5. Convert observers to Q/DQ pairs
6. Post-quantization graph surgery (remove spurious Q/DQ pairs from `cat` nodes in FP32 body blocks)
7. Export to ExecuTorch `.pte` with XNNPACK backend

### Preprocessing

| Property | Value |
|---|---|
| Input shape | `[1, 3, 128, 128]` (BCHW, single tile) |
| Data type | float32 |
| Value range | [0.0, 1.0] |
| Color space | RGB |

**Steps**:
1. Convert PIL image to float32 tensor via `ToTensor()` (maps [0, 255] → [0, 1])
2. For images larger than 128×128: split into overlapping 128×128 tiles with 8-pixel overlap; each tile is processed independently

**Normalization**: None required — the model expects raw [0, 1] pixel values.

### Postprocessing

| Property | Value |
|---|---|
| Output shape | `[1, 3, 512, 512]` per tile (4x upscaled) |
| Output format | Float32 RGB tensor, values may exceed [0, 1] before clamping |

**Steps**:
1. Clamp output tensor to [0, 1]
2. For tiled inputs: accumulate tile outputs into a full-resolution canvas, averaging overlapping regions
3. Convert to uint8 for saving (`* 255`, round, cast)

## Known Limitations

- Evaluated on 100 images from Urban100 — a benchmark focused on urban/architectural scenes; performance on natural landscapes or faces may differ
- Fixed tile size of 128×128: images must be at least 128×128; the tiling inference handles arbitrary sizes automatically
- Input images are not aspect-ratio padded — tiles are extracted at stride `tile_size - overlap` with edge tiles placed at the image boundary
- XNNPACK operator coverage is approximately 49%: upsampling, LeakyReLU, and some transpose operations fall back to non-XNNPACK kernels; latency figures reflect this mixed execution