pixtreme-upscale 0.6.0

Multi-backend deep learning upscalers for pixtreme

pixtreme-upscale

Multi-backend deep learning upscalers for pixtreme

Overview

pixtreme-upscale provides GPU-accelerated deep learning super-resolution with support for ONNX, PyTorch, and TensorRT backends. Integrates with Spandrel for automatic model architecture detection.

Features

• Multi-Backend Support: ONNX Runtime, PyTorch, TensorRT
• Automatic Tiling: Handle large images with automatic tiling workflow
• Model Conversion: PyTorch → ONNX → TensorRT pipeline
• Spandrel Integration: Automatic architecture detection for 100+ models
• Zero-Copy: Direct GPU memory operations

Installation

# Base installation (ONNX + PyTorch)
pip install pixtreme-upscale

# With TensorRT support
pip install pixtreme-upscale[tensorrt]

Requires pixtreme-core, PyTorch, ONNX Runtime, and CUDA Toolkit 12.x.

Quick Start

import pixtreme_upscale as pu
import pixtreme_core as px

# Read image
img = px.imread("input.jpg")

# ONNX backend (fastest compatibility)
upscaler = pu.OnnxUpscaler("model.onnx")
upscaled = upscaler.get(img)

# PyTorch backend (most flexible)
upscaler = pu.TorchUpscaler("model.pth")
upscaled = upscaler.get(img)

# TensorRT backend (fastest performance)
upscaler = pu.TrtUpscaler("model.onnx")  # Auto-converts to TRT
upscaled = upscaler.get(img)

# Save result
px.imwrite("output.jpg", upscaled)

Automatic Tiling

All upscalers automatically handle large images via tiling:

# Automatically tiles large images
large_img = px.imread("8k_image.jpg")
upscaled = upscaler.get(large_img)  # Handles tiling internally

Model Conversion

from pixtreme_upscale.utils import convert_pytorch_to_onnx, convert_onnx_to_tensorrt

# PyTorch → ONNX
convert_pytorch_to_onnx("model.pth", "model.onnx")

# ONNX → TensorRT (requires tensorrt extra)
convert_onnx_to_tensorrt("model.onnx", "model.trt", fp16=True)

Supported Models

Works with Spandrel-supported architectures:

• ESRGAN, Real-ESRGAN, RealESRGAN+
• SwinIR, HAT, OmniSR
• And 100+ more architectures

License

MIT License - see LICENSE file for details.

Links

• Repository: https://github.com/sync-dev-org/pixtreme