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TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward
Recent advances in powerful text-to-image generation models have made it increasingly important to develop test-time methods that modify the sampling trajectory to produce images more faithful to complex compositional prompts. We present TILT, a training-free framework for compositional text-to-imag