TraDiffusion: Trajectory-Based Training-Free Image Generation (2408.09739v1)

Abstract: In this work, we propose a training-free, trajectory-based controllable T2I approach, termed TraDiffusion. This novel method allows users to effortlessly guide image generation via mouse trajectories. To achieve precise control, we design a distance awareness energy function to effectively guide latent variables, ensuring that the focus of generation is within the areas defined by the trajectory. The energy function encompasses a control function to draw the generation closer to the specified trajectory and a movement function to diminish activity in areas distant from the trajectory. Through extensive experiments and qualitative assessments on the COCO dataset, the results reveal that TraDiffusion facilitates simpler, more natural image control. Moreover, it showcases the ability to manipulate salient regions, attributes, and relationships within the generated images, alongside visual input based on arbitrary or enhanced trajectories.

• The paper introduces a novel trajectory-based, training-free method for precise control in image generation using Stable Diffusion.
• It leverages a distance awareness energy function to steer image elements along user-defined trajectories without additional training.
• User studies demonstrate improved natural layouts and enhanced control over salient regions and object relationships.

TraDiffusion: Trajectory-Based Training-Free Image Generation

The paper, "TraDiffusion: Trajectory-Based Training-Free Image Generation," elaborates on a novel method for enhancing image generation control via user-defined trajectories. This method bridges the gap between fine and coarse layout controls, providing a middle ground with enhanced user-friendliness. The new approach offers novel ways to manipulate visually distinct regions, attributes, and relationships of objects in generated images.

Introduction

The exploration of improving radiance in image generation touches upon various models that excel in creating high-quality images aligned with textual descriptions. While text-based control has proven effective, it lacks the precision needed for fine-grained details. Existing layout control methods, such as those based on masks and bounding boxes, offer a degree of precision but have their limitations in regards to granularity and user experience. TraDiffusion proposes a trajectory-based method that aligns more naturally with human attention and offers detailed control without the burden of additional training.

Methodology

Problem Definition

The central problem addressed involves controlling image elements using trajectories, which guide the positions of objects during image generation. This is achieved without further training or fine-tuning of the underlying Stable Diffusion (SD) model.

Stable Diffusion

The core framework employed in this paper is the stable diffusion model, which utilizes iterative denoising from a random noise map within the latent space of a Variational AutoEncoder (VAE). The cross-attention mechanism of SD plays a key role in linking text conditions with the generated image content.

Trajectory-Based Control

Previous methods predominantly use masks or boxes for spatial conditioning, each with its drawbacks. Masks require precise specification while boxes offer coarse control. To bridge this gap, TraDiffusion introduces trajectory-based control, wherein users specify a trajectory for target words or phrases in the text prompt. The model then generates an image that places these elements according to the given trajectory, allowing for a more granular yet intuitive control.

Distance Awareness Guidance

Two key components underpin the trajectory-based control:

1. Control Function: This steers the object towards a given trajectory, calculated using the distance matrix and attention map.
2. Movement Function: This suppresses the attention responses in regions distant from the specified trajectory, ensuring focused and relevant generation.

The combination, dubbed the Distance Awareness Energy Function, is computed during the denoising steps of the diffusion process, and is used to update the latent variables during backpropagation.

Experimental Results

Applications

The paper showcases several applications of trajectory-controlled image generation:

• Salient Areas: Adjusting salient regions of objects via enhanced local trajectories.
• Arbitrary Trajectories: Defining object postures or shapes accurately through varied trajectory inputs.
• Attributes and Relationships: Mitigating attribute confusion and defining interactions between objects.
• Visual Input: Controlling the orientations and positioning of visual elements.

Comparison with Prior Work

User studies highlighted TraDiffusion's superior performance in generating natural images with controlled layouts, compared to existing mask and box-based methods such as DenseDiffusion, ControlNet, BoxDiff, and Backward Guidance. Users rated trajectories as more user-friendly and adaptable across various semantic inputs.

Quantitative Metrics

Quantitatively, TraDiffusion has shown significantly better Distance To Line (DTL) scores, indicating higher fidelity in aligning objects with specified trajectories. Although a slight compromise in FID score is noted with the inclusion of the movement function, the overall image quality remains competitive.

Conclusions and Future Work

TraDiffusion demonstrates an effective training-free method for achieving detailed control in text-to-image generation by leveraging trajectories. This development opens new practical and theoretical avenues by simplifying user interaction and expanding the functional capabilities of diffusion models. Future research could explore more complex manipulations and further optimizations to accommodate finer adjustments in object shapes. Potential applications might extend to diverse domains like graphic design, interactive art, and automated content creation, potentially revolutionizing user interfaces in AI-driven image synthesis.