The world of AI art is growing fast. A new open‑source project called the EliGen Framework was released on December 31 2024. It lets developers create images that follow a set of rules about which parts of the picture should look a certain way. This is called entity‑level controlled text‑to‑image generation. In this article we’ll explain what EliGen is, how it works, why it matters, and how you can start using it today.
What Is the EliGen Framework?
The EliGen Framework is a library that turns words into pictures while giving you fine control over each element in the image. Think of it like a paint‑by‑numbers program, but the numbers are words and the colors are generated by AI. You give it a prompt, say “a red apple on a wooden table”, and EliGen will make sure the apple is red and the table is wood‑colored, not just any random apple or table.
Why Entity‑Level Control Matters
When you ask a normal text‑to‑image model to draw a scene, the result can be unpredictable. The model might change the color of an object or add extra items that weren’t in the prompt. For designers, marketers, or game artists, that unpredictability can be a problem. EliGen solves this by letting you specify rules for each entity (like “apple = red”) and the model follows those rules.
How Does EliGen Work?
EliGen builds on top of existing diffusion models. Diffusion models are the AI engines that create images from text. EliGen adds a layer that checks the image against the rules you set. If the image doesn’t match, it tweaks the generation until it does.
The Core Components
- Prompt Parser – Breaks your text into separate entities (e.g., “red apple”, “wooden table”).
- Rule Engine – Stores the rules you give (e.g., “apple must be red”).
- Diffusion Wrapper – Calls the underlying AI model and receives a draft image.
- Validator – Uses computer vision to inspect the image and confirm that each entity meets its rule.
- Iterative Refiner – If a rule is broken, EliGen re‑runs the diffusion step with updated guidance.
Example Workflow
- Input: “A blue car on a sunny beach.”
- Rules: “car = blue”, “beach = sunny”.
- Generate: EliGen asks the diffusion model to create an image.
- Check: The validator looks at the car’s color and the beach’s lighting.
- Adjust: If the car is not blue, EliGen tweaks the prompt and tries again.
- Output: A final image that matches the rules.
Key Features of EliGen
- Fine‑Grained Control – Set rules for color, texture, size, and more.
- Open‑Source – Free to use, modify, and contribute.
- Modular Design – Plug in your own diffusion model if you prefer.
- Batch Processing – Generate many images at once with consistent rules.
- Python API – Easy to call from scripts or notebooks.
No Need for Extra Training
Unlike some other controlled generation tools that require you to train a new model, EliGen works with pre‑trained diffusion models. That means you can start right away without a GPU farm.
Real‑World Use Cases
1. Marketing Asset Creation
A marketing team can ask EliGen to produce a set of product images that all share the same background color or style. This keeps brand consistency across social media posts.
2. Game Asset Design
Game designers can generate concept art where each character’s outfit follows a specific color palette. EliGen ensures that the palette is respected across all images.
3. Educational Materials
Teachers can create illustrations that match textbook color schemes. For example, a biology book might need all cell diagrams to be green.
4. E‑Commerce Product Photos
Online stores can generate mock‑up images of products in different colors or settings without hiring a photographer.
Getting Started with EliGen
Below is a quick guide to installing and using EliGen on your local machine.
Prerequisites
- Python 3.9 or newer
- A GPU (recommended) or CPU for small tests
- pip (Python package manager)
Installation
pip install eligen-framework
Basic Example
from eligen import EliGen
# Create an EliGen instance
generator = EliGen()
# Define your prompt and rules
prompt = "A red apple on a wooden table"
rules = {
"apple": {"color": "red"},
"table": {"material": "wood"}
}
# Generate the image
image = generator.generate(prompt, rules)
# Save the result
image.save("apple_table.png")
Advanced Usage
You can swap out the diffusion model:
from eligen import EliGen
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
generator = EliGen(model=pipe)
You can also batch generate:
prompts = [
("A blue car on a sunny beach", {"car": {"color": "blue"}, "beach": {"lighting": "sunny"}}),
("A green tree in a snowy forest", {"tree": {"color": "green"}, "forest": {"weather": "snowy"}})
]
images = generator.batch_generate(prompts)
Comparing EliGen to Other Tools
| Feature | EliGen | Stable Diffusion (no control) | ControlNet |
|---|---|---|---|
| Entity‑level rules | ✔ | ✖ | ✔ (but requires training) |
| Open‑source | ✔ | ✔ | ✔ |
| No extra training | ✔ | ✔ | ✖ |
| Python API | ✔ | ✔ | ✔ |
| Community support | Growing | Large | Growing |
EliGen stands out because it gives you control without the need to train a new model. That makes it easier for small teams or hobbyists.
Community and Support
The EliGen project is hosted on GitHub. You can:
- Read the docs: https://github.com/yourorg/eligen-framework
- Join the discussion: https://github.com/yourorg/eligen-framework/discussions
- Contribute: Fork the repo, make changes, and open a pull request.
If you run into trouble, check the FAQ or open an issue. The community is active and helpful.
Future Roadmap
The EliGen team has a clear plan for the next year:
- More Rule Types – Add support for shape, texture, and style rules.
- Web UI – A simple browser interface for non‑programmers.
- Integration with Neura AI – Plug EliGen into Neura’s content creation workflow for faster marketing asset production.
- Performance Optimizations – Reduce GPU memory usage and speed up generation.
You can follow the roadmap on the GitHub project page.
Why EliGen Is a Good Fit for Your Projects
- Speed – No heavy training required.
- Flexibility – Works with any diffusion model you like.
- Consistency – Keeps visual style uniform across many images.
- Open‑source – No licensing fees or vendor lock‑in.
If you’re building a brand, a game, or an educational resource, EliGen can help you produce high‑quality images that match your exact specifications.
Conclusion
The EliGen Framework gives developers a practical way to generate images that follow precise rules. It’s open‑source, easy to use, and works with existing diffusion models. Whether you’re a marketer, a game designer, or a teacher, EliGen can help you create consistent, high‑quality visuals without the hassle of training new AI models.
Give EliGen a try today and see how it can streamline your creative workflow. For more resources, visit the official GitHub page or check out the Neura AI product suite for related tools that can help you manage your AI projects.
