1. Introduction
Recent advancements in generative AI have revolutionized image creation, yet achieving precise compositional control remains a significant challenge, often requiring tedious trial-and-error. This work addresses the problem of enabling intuitive and fine-grained image composition through diverse instructional modalities. We propose PhotoFramer, a system designed to interpret and combine textual descriptions, visual references, and spatial cues to guide image generation effectively. The primary models used in this article include PhotoFramer (our proposed multi-modal composition network), a modified latent diffusion model, and a vision-language encoder for input interpretation.
2. Related Work
Existing literature primarily focuses on text-to-image generation or image editing based on textual prompts, often struggling with complex compositional demands. Methods employing sketch-based or reference-image guidance have shown promise but lack the flexibility of combining multiple input types. While some multi-modal approaches exist for image understanding, their application to precise image generation instruction is less explored. Our work builds upon these foundations by integrating and harmonizing diverse input modalities for enhanced creative control.
3. Methodology
PhotoFramer's methodology involves a multi-stage process, starting with the encoding of various input modalities: text prompts are processed by a large language model, reference images by a pre-trained vision transformer, and spatial guidance via a segmentation network. These encoded features are then fused using a novel cross-attention mechanism within a U-Net architecture, adapted from a latent diffusion model. This fusion allows for the coherent integration of compositional instructions, guiding the iterative denoising process to synthesize an image that precisely adheres to all specified inputs. The training process involves a custom dataset of multi-modal instruction-image pairs.
4. Experimental Results
Experiments demonstrate that PhotoFramer significantly outperforms baseline methods in terms of compositional accuracy and image quality. User studies consistently rated PhotoFramer-generated images as more faithful to the instructions and aesthetically pleasing. Quantitative metrics, such as FID and CLIP score, further confirm the superior performance of our multi-modal approach compared to text-only or single-modal guidance systems. The following table summarizes key performance metrics across different approaches, showcasing PhotoFramer's robust capabilities.
| Method | FID Score ↓ | CLIP Score ↑ | Compositional Accuracy (User Study %) ↑ |
|---|---|---|---|
| Text-Only Baseline | 18.5 | 0.28 | 62.1 |
| Sketch-Guided Baseline | 16.2 | 0.31 | 75.4 |
| PhotoFramer (Ours) | 10.3 | 0.39 | 91.8 |
5. Discussion
The results underscore the critical importance of multi-modal input for achieving fine-grained control in generative image composition, demonstrating that PhotoFramer effectively bridges the gap between high-level creative intent and precise visual output. The fusion of diverse modalities enables unprecedented control, leading to more satisfying and controllable image generation experiences. Future work could explore incorporating temporal consistency for video generation or extending the framework to 3D scene composition, further expanding its creative applications.