On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

5 Steps

1. 1

   Identify Typicality Bias in T2I Outputs: Analyze your current Text-to-Image model's outputs for a given prompt. Observe if it consistently produces visually similar results, indicating a 'typicality bias' that limits creative range.

2. 2

   Understand On-the-fly Repulsion Principle: Grasp the core concept of introducing a repulsion mechanism *during* the generation process. This mechanism aims to push model outputs away from each other in the latent space, actively promoting diversity.

3. 3

   Pinpoint Contextual Space for Intervention: Determine the specific 'contextual space' within your Diffusion Transformer (e.g., latent embeddings, attention mechanisms, or feature maps) where a repulsive force can be effectively applied to influence output variety.

4. 4

   Implement a Latent Repulsion Mechanism: Develop and integrate code that applies a repulsive force to latent representations or intermediate features during the diffusion process. This prevents the model from converging to overly typical or expected outputs.

5. 5

   Evaluate and Quantify Output Diversity: Generate multiple images with your modified model using the same prompt. Apply quantitative diversity metrics (e.g., FID, LPIPS, or perceptual distance between generated samples) to measure and confirm the improvement in visual variety.