Diffusion models are a powerful class of generative models capable of producing high-quality images from pure noise. In particular, conditional diffusion models allow one to specify the contents of the desired image using a simple text prompt. Conditioning on a text prompt alone, however, does not allow for fine-grained control over the composition and layout of the final image, which instead depends closely on the initial noise distribution. While most methods which introduce spatial constraints (e.g., bounding boxes) require fine-tuning, a smaller and more recent subset of these methods are training-free. They are applicable whenever the prompt influences the model through an attention mechanism, and generally fall into one of two categories. The first entails modifying the cross-attention maps of specific tokens directly to enhance the signal in certain regions of the image. The second works by defining a loss function over the cross-attention maps, and using the gradient of this loss to guide the latent. While previous work explores these as alternative strategies, we provide an interpretation for these methods which highlights their complimentary features, and demonstrate that it is possible to obtain superior performance when both methods are used in concert.

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