https://github.com/Vinicius268/Qwen-Image-Diffusion/releases

Qwen-Image-Diffusion: High-Quality Text-to-Image with Gradio UI

Qwen-Image-Diffusion is a compact, powerful tool for turning text prompts into vivid images. It uses the Qwen diffusion model and wraps it in a clean Gradio web interface. The goal is to make image generation approachable without sacrificing control or quality. This project blends state-of-the-art diffusion techniques with a friendly browser UI, so artists, researchers, and hobbyists can experiment with prompt syntax, sampling strategies, and model variants in a single place.

What you’ll find here

• A streamlined text-to-image pipeline powered by Qwen diffusion
• An intuitive Gradio-based interface that runs locally or on a server
• Clear guidance for installing, running, and extending the system
• Practical prompts, tips for achieving better results, and performance notes
• A path to explore related diffusion models via the HuggingFace ecosystem

This README uses a practical, no-nonsense style. It aims to be easy to follow, even if you are new to diffusion or Gradio. It also includes concrete steps, example prompts, and troubleshooting hints to help you get results quickly and reliably.

Table of contents

• Overview and goals
• What makes this project different
• How the system works
• System requirements
• Quick start
• Installation and setup
• Running locally
• Using the Gradio interface
• Prompt design and examples
• Advanced usage and customization
• Performance and optimization
• Model and data considerations
• Security, safety, and ethics
• Troubleshooting
• Project governance and contributions
• Release history and keeping up to date
• Community and references

Overview and goals Qwen-Image-Diffusion aims to provide a robust, user-friendly way to generate high-quality images from text prompts. It combines the Qwen diffusion model with a responsive Gradio front end. The setup focuses on clarity, speed, and flexibility. You can run it on a personal machine, a workstation, or a server. The interface makes it easy to save, compare, and iterate on prompts and results.

The project centers on accessibility, repeatability, and experimentation. You can modify the prompt, adjust sampling steps, tweak guidance scales, and switch model variants to compare styles and outputs. The Gradio UI is designed to help you explore the creative space without getting bogged down in code.

What makes this project different

• Direct integration with the Qwen diffusion model, tuned for speed and quality
• A polished Gradio UI that exposes the core controls you need without overwhelming you
• Clear prompts, presets, and examples to help you start generating quickly
• An emphasis on reproducibility: fixed seeds, deterministic sampling modes, and easy saving of results
• A path to extend: the architecture is modular, so you can swap models or UI components with minimal fuss

How the system works

• Prompt parsing: The UI collects a text prompt and optional seed. The prompt is parsed and prepared for the diffusion model.
• Diffusion engine: The Qwen-based diffusion model runs a denoising process guided by the prompt. You can adjust sampling steps, guidance scale, and seed to search for different styles and levels of detail.
• Image generation: The pipeline outputs a high-resolution image. You can save the result, apply post-processing, or stash it for comparison.
• UI layer: Gradio provides a simple, responsive web interface. The UI includes fields for prompt, seed, guidance, steps, and a preview area. It also has a gallery of generated images and quick-export options.

System requirements

• A modern GPU with a minimum of 6 GB VRAM for basic usage; 12 GB or more for higher resolution outputs or fast sampling
• Python 3.10 or newer
• CUDA-enabled driver for GPUs that support CUDA for acceleration (if you plan to run on a local machine with an NVIDIA GPU)
• Adequate disk space: model weights and assets can take several gigabytes
• Reasonable memory: diffusion pipelines can be memory-hungry; ensure you have enough RAM for your chosen batch size and image resolution
• Stable internet during initial setup to fetch dependencies and model weights (or have offline resources ready)

Quick start

• Get the latest release
  • The quickest path is to download the latest release from the releases page. Start by visiting the releases section and grabbing the packaged app. From the Releases page, download the release asset, extract it, and run the included startup script. The link to the releases page is https://github.com/Vinicius268/Qwen-Image-Diffusion/releases.
• Install prerequisites
  • Install Python 3.10+ on your system
  • Create a virtual environment
  • Install core dependencies (Gradio, diffusers, transformers, peft, safetensors)
• Run the UI
  • Start via the provided script or Python entry point
  • Open your browser at http://localhost:7860 (default)
• Generate and refine
  • Enter prompts, adjust sampling steps, and tweak the guidance scale
  • Save preferred results for comparison
• Explore presets
  • Use built-in prompts and presets to spark ideas
  • Customize prompts with adjectives, nouns, and scene descriptors for different styles

Installation and setup

• Prerequisites you should install first
  • Python 3.10 or newer
  • Git (for cloning if you prefer)
  • CUDA drivers for NVIDIA GPUs if you want GPU acceleration
  • Optional: Docker if you want containerized runs
• Cloning the repository (optional if you are using a release)
  • git clone https://github.com/Vinicius268/Qwen-Image-Diffusion.git
  • cd Qwen-Image-Diffusion
• Installing dependencies (local development)
  • Create a virtual environment:
    • python -m venv venv
    • source venv/bin/activate (Linux/macOS)
    • venv\Scripts\activate (Windows)
  • Install packages:
    • pip install gradio diffusers transformers peft safetensors
  • Optional extras depending on your use case:
    • pip install accelerate
• Using the release package
  • The releases page contains a pre-built bundle. Download the asset, extract, and run the startup script included in the bundle.
  • After extraction, locate the startup script (for example, start_qwen_diffusion.sh or start_qwen_diffusion.bat) and run it.
  • The bundle typically ships with a Python environment or an isolated runtime. Use the provided script to ensure dependencies align with the published configuration.

Running locally

• Desktop or laptop with a modern GPU
  • Start the UI with: python -m gradio_qwen.run or the provided startup script
  • Open the local URL shown in the terminal, usually http://127.0.0.1:7860
• Headless server or remote machine
  • Run with a specific host and port: python -m gradio_qwen.run --host 0.0.0.0 --port 7860
  • If you use a reverse proxy or a domain, configure your firewall and TLS as needed
• Using a Docker container (optional)
  • Docker can simplify environment management
  • Build and run with docker compose or a simple docker run command
  • Ensure GPU access is enabled if you plan to use hardware acceleration

Using the Gradio interface

• Prompt field
  • Enter a text prompt describing the scene you want
  • Combine style cues with scene descriptors, lighting, camera angles, and color palettes
• Model controls
  • Guidance scale: Higher values push the image to align with the prompt; lower values allow more creative freedom
  • Steps: The number of diffusion steps affects detail and speed
  • Seed: Reproduce exact results by fixing seeds
• Image options
  • Resolution: Choose a resolution compatible with your hardware
  • Seed mixing and tiling: Optional features to explore variations
• Gallery and export
  • Save images to a local gallery
  • Export or download for sharing
  • Compare multiple generations side by side
• Workflow tips
  • Start with broad prompts and then refine with adjectives and modifiers
  • Save seeds and prompts for reproducibility
  • Use prompts that specify lighting, mood, and composition to guide the model

Prompt design and examples

• Prompt structure basics
  • Subject + action + style descriptor
  • Include lighting, mood, and camera perspective for more control
• Example prompts
  • "A tranquil mountain village at dawn, ultra-detailed, watercolor, soft lighting"
  • "A cyberpunk cityscape at night, neon lights, rain, wide-angle perspective, cinematic"
  • "Portrait of a medieval warrior, oil painting, dramatic chiaroscuro, 3:4 aspect ratio"
  • "Cute fantasy creature in a grassy field, vibrant colors, high detail, 8k"
• Style modifiers
  • Realism, impressionism, surrealism, vector art, anime, photorealistic
  • Fine-tune with descriptors like "aerial view," "close-up," "soft focus," "grain," "bokeh"
• Scene composition prompts
  • "Two astronauts on a distant planet, horizon glow, dramatic sky"
  • "A quiet library with floating books, warm light, cozy mood"
• Prompt hygiene
  • Avoid overly long prompts that confuse the model
  • Use consistent terms for the same style to improve reproducibility
• Iteration and exploration
  • Use seeds to explore variations of a prompt
  • Save multiple versions to compare composition and mood

Advanced usage and customization

• Working with diffusion models directly
  • Learn the basics of diffusers pipelines and how to switch between models
  • Experiment with different schedulers for sampling
• Peft and adapters
  • Use PEFT to tune prompts for style adaptation without retraining the whole model
  • Attach adapters to tailor prompts for specific subjects or environments
• Safetensors and model loading
  • Use safetensors for safe and fast model loading
  • Confirm that weights are compatible with your environment
• Mixing models
  • Combine a base diffusion model with a style-specific variant
  • Use prompt weighting to emphasize certain attributes
• Output post-processing
  • Simple image editing after generation to adjust color balance or sharpness
  • Lightweight upscaling using a diffusion-based upsampler if available

Performance and optimization

• Hardware considerations
  • GPU memory dictates resolution and batch size
  • For better speed, use FP16 precision where supported
• Software optimizations
  • Enable gradient checkpointing to reduce memory usage
  • Use a smaller batch size and incremental tiling for high-res outputs
  • Cache model weights to reduce load times between runs
• Observability
  • Log generation metadata such as prompt, seed, steps, and guidance
  • Save a brief note with each image to help you reproduce it later
• Troubleshooting performance
  • If you see low GPU utilization, verify the runtime is using the intended device
  • Check for memory fragmentation and reduce resolution or steps
  • Update drivers and libraries to maintain compatibility with newer diffusion features

Model and data considerations

• Model selection
  • The Qwen diffusion family offers different variants. Each variant has its strengths: detail, style, or speed
  • Compare multiple variants to find the best match for your task
• Weights and licensing
  • Ensure you have rights to use the model weights you load
  • Respect licensing constraints of the model and any integrated components
• Data usage
  • Treat prompts and outputs as synthetic data
  • If you plan to publish or share results, document the prompts and configurations used
• Reproducibility
  • Use fixed seeds for repeatable results
  • Save the exact pipeline configuration with each image

Security, safety, and ethics

• Content safety
  • The system is designed to avoid producing illegal or harmful content
  • Use prompt restrictions when needed
• Privacy
  • Do not upload sensitive data if you intend to keep outputs private
  • Be mindful of images that reveal personal information if you share results publicly
• Responsible use
  • Use this tool for creative and educational purposes
  • Respect intellectual property and avoid reproducing works without permission

Troubleshooting

• Common issues and fixes
  • No UI response: Ensure the server is running, check port usage, and verify dependencies
  • Low-quality outputs: Increase steps or adjust guidance scale; refine prompts with more specific descriptors
  • Memory errors: Reduce image size or batch size; enable memory-saving features
  • Model not loading: Confirm correct weights path, verify compatibility, and re-install dependencies if needed
• Logs and diagnostics
  • Check log files or console output for model initialization messages
  • Look for CUDA or device mismatch messages and adjust your environment
• Community help
  • Engage with the project community for tips and shared prompts
  • Share reproducible prompts, seeds, and configurations to help others reproduce results

Contributing

• How to contribute
  • Fork the repository and create a feature branch
  • Add tests for new functionality
  • Ensure code is well-documented and adheres to the project’s style
• Code quality
  • Write clear, concise functions
  • Use descriptive names for prompts and parameters
  • Minimize side effects and keep the UI responsive
• Documentation
  • Update the README with new features and usage notes
  • Provide examples that demonstrate new capabilities
• Testing
  • Run unit and integration tests
  • Verify that UI elements respond predictably under different scenarios
• Community guidelines
  • Be respectful and supportive
  • Share knowledge and help others learn

Release history and keeping up to date

• Release cadence
  • The project follows a regular release schedule to publish new features, improvements, and bug fixes
• How to find release notes
  • Release notes summarize changes, improvements, and known issues
  • The primary place to track releases is the Releases page
• Keeping up to date
  • Subscribe to notifications for new releases on the repository
  • If you use the release bundle, download the latest asset to stay current
• Re-releases and hotfixes
  • In case of critical fixes, a hotfix release may be published
  • Always verify the integrity of assets before use
• The key link to releases
  • Visit the releases page for downloads, changelog, and version history: https://github.com/Vinicius268/Qwen-Image-Diffusion/releases

Releases and asset guidance

• The releases page contains a packaged application suitable for quick setup
• You should download the latest release asset, extract it, and run the provided startup script
• If you need a fresh download, go to the releases page here: https://github.com/Vinicius268/Qwen-Image-Diffusion/releases
• The asset may come as a zip or tar.gz archive; extraction will produce a self-contained end-to-end setup
• After extraction, locate the startup script (for example, start_qwen_diffusion.sh or start_qwen_diffusion.bat) and execute it
• The script will set up the runtime environment if needed and launch the Gradio UI

Gallery, demos, and visuals

• Example results
  • A set of generated images from prompts spanning landscapes, portraits, sci-fi scenes, and abstract art
  • Screenshots from the Gradio UI showing control layouts, seed selection, and gallery
• Visuals in the README
  • Demo visuals help convey what the model can produce
  • The UI is designed to be intuitive and visually organized
• Suggested prompts
  • A curated list of prompts to help you begin testing the system
  • Each prompt is paired with notes about expected style and detail
• How to manage outputs
  • Save images to a local folder
  • Tag outputs with prompts and seeds
  • Create a small gallery to revisit favorite results

Community, references, and further reading

• Related models and ecosystems
  • The diffusion field includes many models and variants
  • Explore diffusers, transformers, and related libraries
• Tutorials and guides
  • Learn the basics of diffusion models, prompt engineering, and UI design for AI tools
  • Find walkthroughs describing how to fine-tune prompts for different subjects
• Acknowledgments
  • Thanks to the open-source community for tools, models, and ideas that make this project possible

Images and media

• Sample visuals
  • 
  • 
• How to interpret outputs
  • Look for sharpness, color saturation, and stylistic alignment with the prompt
  • Examine balance between subject detail and background texture
  • Compare multiple outputs to identify patterns and preferences

Notes on usage with the releases

• The starting point for acquiring the software is the releases page.
• The release asset contains a bundled environment that simplifies setup.
• From the releases page, download the release asset, extract, and run the included startup script to begin generating images with the Gradio UI.
• If you need a direct path to the download, you can navigate to the releases page at https://github.com/Vinicius268/Qwen-Image-Diffusion/releases and pick the asset labeled for your system.

Sustainability and maintenance

• Keeping dependencies aligned
  • Regularly check for updates to Gradio, diffusers, transformers, and supporting libraries
  • When updating, test prompts and pipelines to ensure compatibility
• Code health
  • Use linting and tests where available
  • Document any breaking changes or notable behavior shifts in the changelog
• Community input
  • Solicit feedback from users to improve prompts, UI, and performance
  • Consider feature requests that align with the core goals of accessibility and quality

Licensing and attribution

• The project includes components under MIT or similar licenses; refer to the LICENSE file for exact terms
• If you publish derived work or samples, provide attribution to the original project and its contributors
• Be mindful of model licenses when distributing pre-trained weights or derivatives

License and attribution note

• See LICENSE for the exact terms
• When sharing outputs or configurations, credit the core tools and models used

Call to action

• Try the UI and generate your first image from text prompts
• Explore different styles, subjects, and lighting
• Share your prompts and results with the community to inspire others

Revisit the releases page for updates and assets

• For the latest downloads, notes, and version history, visit the Releases page
• The page contains all official release assets and a changelog to help you track improvements
• Access the page again here: https://github.com/Vinicius268/Qwen-Image-Diffusion/releases

End of document

• The Readme above provides a thorough guide to getting started, using, and extending Qwen-Image-Diffusion
• Explore prompts, test configurations, and model variants to discover what this tool can do for your projects
• The project welcomes feedback, contributions, and experimentation from a curious community