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2026-02-26 09:48:04
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Sketch Guidance Sketch-Guided Text-to-Image Diffusion Models SIGGRAPH 2023 Andrey Voynov 1 Kfir Aberman 1 Daniel Cohen-Or 1,2 1 Google Research 2 Tel Aviv University Paper Code (Coming Soon) Abstract Text-to-Image models have introduced a remarkable leap in the evolution of machine learning, demonstrating high-quality synthesis of images from a given text-prompt. However, these powerful pretrained models still lack control handles that can guide spatial properties of the synthesized images. In this work, we introduce a universal approach to guide a pretrained text-to-image diffusion model, with a spatial map from another domain (e.g., sketch) during inference time. Unlike previous works, our method does not require to train a dedicated model or a specialized encoder for the task. Our key idea is to train a Latent Guidance Predictor (LGP) - a small, per-pixel, Multi-Layer Perceptron (MLP) that maps latent features of noisy images to spatial maps...
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