Sketch-Guided Text-to-Image Diffusion Models (2211.13752v1)

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, where the deep features are extracted from the core Denoising Diffusion Probabilistic Model (DDPM) network. The LGP is trained only on a few thousand images and constitutes a differential guiding map predictor, over which the loss is computed and propagated back to push the intermediate images to agree with the spatial map. The per-pixel training offers flexibility and locality which allows the technique to perform well on out-of-domain sketches, including free-hand style drawings. We take a particular focus on the sketch-to-image translation task, revealing a robust and expressive way to generate images that follow the guidance of a sketch of arbitrary style or domain. Project page: sketch-guided-diffusion.github.io

Sketch-Guided Text-to-Image Diffusion Models: A Methodological Contribution to Image Synthesis

The paper entitled "Sketch-Guided Text-to-Image Diffusion Models" presents a sophisticated method to enhance the capabilities of pretrained text-to-image diffusion models by integrating sketch-based guidance. The authors, Voynov et al., address a notable gap in the capacity of text-to-image models to manage spatial attributes of images without requiring elaborate additional training.

Key Contributions

The core contribution of the paper lies in the development of a Latent Guidance Predictor (LGP), a lightweight Multi-Layer Perceptron (MLP), which augments the traditional text-to-image diffusion models such as those based on Denoising Diffusion Probabilistic Models (DDPM). The LGP effectively maps latent image features to spatial maps, allowing these diffusion models to be guided by sketches without the necessity of training new encoders.

1. Framework Design: The LGP is designed to work efficiently with pretrained models without modifying them extensively. It leverages noise-filled latent features at varying levels to map them to sketch-based spatial maps, offering flexibility and adaptability to diverse and out-of-domain sketches.
2. Training Complexity: The system is trained on a relatively small dataset of a few thousand images, significantly lowering the typical computational resources and data requirements. This is achieved through a per-pixel learning strategy, which enhances flexibility across different sketch domains.
3. Generative Robustness: By focusing on sketch-to-image translation, the approach demonstrates robustness over varied sketch styles, ensuring outputs align closely with both the structure of input sketches and textual descriptions. The synthesis retains high fidelity to user-provided sketches, which is critical for generating actionable and realistic images.

Experimental Insights

Across a series of experiments, the method showed strong performance in sketch-guided image synthesis, even with out-of-domain sketches. Comparative analysis with other translation methods such as SDEdit and pix2pix reveals that this approach produces high-quality and domain-agnostic images with minimal artifacts. The paper successfully argues against the necessity for training specialized encoders for sketch input, highlighting the efficacy of the LGP.

1. Performance Analysis: The experiments included qualitative evaluations against existing generative adversarial network (GAN) models, and diffusion-based models, where this novel approach displayed significant improvements in terms of adaptability and output quality.
2. Parameter Flexibility: The researchers carried out extensive parametric studies to balance realism and edge fidelity, concluding that the LGP can adapt its guidance strength (via the parameter $\beta$) depending on specific use-cases, thus enhancing user control over the generation process.

Implications and Future Directions

The paper outlines implications at both practical and theoretical levels. Practically, this work suggests a feasible route to introduce sketch control in powerful diffusion systems, adding value to content creation tools where spatial coherence is pivotal. Theoretically, it advances understanding of how latent spaces can be manipulated to intersperse new domains without retraining the entire system from scratch.

1. Practical Applications: Potential uses span from artistic creation and virtual prototyping to personalized content generation. It directly benefits domains where tailored spatial configuration of content is desired without investing heavily in new model development.
2. Research Extensions: Future studies could explore integrating additional modalities and domain-specific priors to further extend the capabilities of the model. Understanding how this approach interacts with state-of-the-art LLMs could unlock further advancements in multi-modal generative tasks.

Overall, the work of Voynov et al. proposes a robust mechanism for enriching pretrained diffusion models with spatial control via sketch inputs. It stands as a progressive stride towards democratizing high-fidelity image generation technology and encourages continued innovation at the intersection of textual and visual modalities in AI systems.