Qwen & Flux Image Editing Benchmark Tool

A Python-based benchmarking and inference framework for testing AI-powered image editing pipelines built with 🤗 Hugging Face Diffusers.
Supports both Qwen Image Edit Plus and Flux Kontext pipelines, including vanilla and GGUF quantized models.

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Overview

This project provides a command-line tool (main.py) and a structured Makefile to:

• Run reproducible image editing experiments
• Compare different models and quantization levels
• Collect step-level timing metrics
• Save output images and performance data

It is designed for developers, ML researchers, and performance testers who want to benchmark or visually evaluate model variations in Qwen and Flux pipelines.

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Features

• Supports multiple pipelines

  • QwenImageEditPlusPipeline
  • FluxKontextPipeline

• Flexible configuration via CLI arguments

  • Model path, type (vanilla / GGUF), steps, CFG, seed, etc.

• Automatic image preprocessing

  • Loads and pads images to target resolution.

• Per-step timing metrics

  • Logs execution time for each inference step to a CSV file.

• Structured output

  • Images in output_images/
  • Metrics in output_metrics/

Project Structure

.
├── main.py              # Core benchmarking script
├── Makefile             # Automation for running multiple tests
├── output_images/       # Generated output images (auto-created)
├── output_metrics/      # CSV performance logs (auto-created)
└── README.md            # Project documentation

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Installation

1. Clone the repository

git clone https://github.com/aws-samples/aws-hf-diffusers-ec2-test-tool
cd aws-hf-diffusers-ec2-test-tool

2. Create virtual enviroment and install dependencies

python -m venv .venv
source .venv/bin/activate
pip -r install requirements.txt

3. (Optional) Authenticate with Hugging Face

Some models require authentication and TOS acceptance:

hf auth login

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Usage

Run directly via Python

Qwen (Vanilla)

python main.py   --pipeline_vendor QwenImageEditPlusPipeline   --model_type vanilla   --steps 10   --cfg 4

Flux (GGUF)

python main.py   --pipeline_vendor FluxKontextPipeline   --model_type GGUF   --steps 20   --cfg 1   --model_path "https://huggingface.co/calcuis/kontext-gguf/blob/main/flux-kontext-lite-q4_0.gguf"

Example Output

output_images/
  ├── 18c2d3b8-...png
output_metrics/
  ├── 18c2d3b8-...csv

Each CSV file includes per-step performance data:

run_id,model_type,model_path,step,timestamp,time_sec
b91e...,GGUF,https://huggingface.co/...q4_0.gguf,1,1728954345.123,0.284321

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Makefile Usage

The included Makefile lets you easily run preconfigured tests.

Run all tests

make all

Run all Qwen models

make qwen_all

Run all Flux models

make flux_all

Run individual tests

make qwen_vanilla
make flux_gguf_q4

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Makefile Targets

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Output Example

Console Output

Building QwenImageEditPlusPipeline.
Loading GGUF model.
Image saved to: output_images/aa58f...png
Metrics saved to: output_metrics/aa58f...csv
Step timings:
run_id, model_type, model_path, step, timestamp, time_sec
aa58f..., GGUF, qwen-image-edit-plus-v2-iq4_nl.gguf, 1, 1728954345.123, 0.284321

CSV Metrics Example

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Notes

• Some models (e.g., Flux Kontext) require accepting their Hugging Face TOS.
• GGUF quantized models are optimized for efficiency — expect lower memory usage but slightly reduced accuracy.

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License

This project is open-source and released under the MIT License.
Feel free to modify and extend it for your own benchmarking needs.

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Disclaimer

This repository is provided for research, benchmarking, and educational purposes only. It automates interactions with various open-source diffusion models (e.g., FluxContext, QuenImageEdit, etc.) but does not include or redistribute any model weights. Model downloads are handled automatically through their respective sources.

The models referenced here are each subject to their own licenses and terms of use. You are solely responsible for reviewing, understanding, and complying with the applicable licenses and any related intellectual property or usage restrictions before using these models in production or commercial environments.

This project is not affiliated with or endorsed by the authors, organizations, or license holders of any of the referenced models.

Use at your own discretion and in accordance with all applicable licenses and laws.

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Contributing

Pull requests and issues are welcome!
If you add new pipelines, models, or benchmarking modes, please update both:

• Makefile
• README.md (targets + usage examples)