We introduce Diffusion Controller, a lightweight "steering damper" network that precisely steers image generation to achieve significantly better prompt alignment.

It seamlessly attaches to even access-restricted, closed-source models, boosting image quality without breaking baseline stability.

Quick links The rapid advancement of text-to-image AI models, such as Nano Banana , Stable Diffusion and Flux , has fundamentally transformed creative design, allowing anyone to synthesize photorealistic, high-fidelity images from textual descriptions.

However, steering these massive models to meet precise user intent, downstream goals, or strict visual constraints remains a delicate and unpredictable balancing act.

For example, imagine prompting a model for "a lizard wearing sunglasses".

The model might generate a realistic lizard that's not wearing sunglasses.

Alternatively, forcing the model to include the sunglasses might distort the lizard's face, ruining the image quality.

Existing methodologies that guide or fine-tune image generation are very disconnected.

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