Transparent Image Layer Diffusion using Latent Transparency (2402.17113v4)

Abstract: We present LayerDiffuse, an approach enabling large-scale pretrained latent diffusion models to generate transparent images. The method allows generation of single transparent images or of multiple transparent layers. The method learns a "latent transparency" that encodes alpha channel transparency into the latent manifold of a pretrained latent diffusion model. It preserves the production-ready quality of the large diffusion model by regulating the added transparency as a latent offset with minimal changes to the original latent distribution of the pretrained model. In this way, any latent diffusion model can be converted into a transparent image generator by finetuning it with the adjusted latent space. We train the model with 1M transparent image layer pairs collected using a human-in-the-loop collection scheme. We show that latent transparency can be applied to different open source image generators, or be adapted to various conditional control systems to achieve applications like foreground/background-conditioned layer generation, joint layer generation, structural control of layer contents, etc. A user study finds that in most cases (97%) users prefer our natively generated transparent content over previous ad-hoc solutions such as generating and then matting. Users also report the quality of our generated transparent images is comparable to real commercial transparent assets like Adobe Stock.

• The paper introduces latent transparency to diffusion models, enabling native generation of transparent images without compromising quality.
• It employs a latent offset to encode alpha channels and fine-tunes pre-trained models for creating coherent transparent layers.
• Experiments reveal a 97% user preference, demonstrating its superiority over traditional generate-then-matte techniques.

Enabling Transparent Image Generation with Latent Diffusion Models

Introduction to Latent Transparency in Image Generation

The field of computer vision and graphics has seen significant advancements with the advent of latent diffusion models, mainly focusing on opaque image generation. However, the niche yet crucial domain of transparent image generation has not been explored extensively, despite its apparent demand in various applications such as digital content creation, graphic design, and augmented reality. Addressing this gap, the paper introduces "LayerDiffusion," a method that innovatively incorporates "latent transparency" into pre-existing latent diffusion frameworks to generate high-quality transparent images and layers. This capability not only opens new avenues in image generation but also preserves the integrity and quality associated with state-of-the-art diffusion models.

Methodological Insights

Latent Transparency: A Novel Approach

The essence of LayerDiffusion revolves around the concept of latent transparency, which cleverly encodes transparency information (alpha channel) into the latent space of a diffusion model without distorting its original latent distribution. This is achieved by introducing a latent offset, which is carefully regulated to ensure that the model's ability to generate high-quality outputs remains unaffected. The approach stands out for its simplicity and effectiveness, allowing any pre-trained latent diffusion model to generate transparent images through fine-tuning with the adjusted latent space.

Unified Framework for Transparent Image and Layer Generation

The paper presents a comprehensive framework that not only facilitates the generation of individual transparent images but also extends to produce multiple coherent transparent layers. This versatility is particularly important for applications requiring depth and compositional detail, such as image editing and graphic design. A shared attention mechanism ensures consistent and harmonious blending between layers, while the introduction of LoRAs (Low-Rank Adaptations) seamlessly adapts the models to diverse layer conditions.

Experimental Findings and User Studies

Extensive experiments demonstrate the effectiveness of the proposed method. Particularly noteworthy is the high preference rate (97%) from users for the transparent content generated natively by the method compared to traditional techniques like generating-then-matting. Additionally, the quality of the generated images was found to be on par with real commercial transparent assets, such as those from Adobe Stock, underscoring the method's potential to produce industry-standard outputs.

Implications and Future Directions

The introduction of latent transparency heralds a new era in image generation, specifically for producing transparent content. This method provides a scalable solution that can leverage the full potential of latent diffusion models for transparent image generation, a capability that has been notably lacking in current generative models. The promising results and high user satisfaction indicate a significant step forward in meeting the demand for high-quality transparent imagery in professional domains.

Looking forward, the paper opens several avenues for further research, including enhancing the method's efficiency, exploring its integration into real-time applications, and extending its capabilities to generate images with varying degrees of transparency dynamically. The commendable results achieved lay a strong foundation for future explorations and innovations in the field of transparent image generation.

Conclusion

The research presents a significant advancement in the field of generative AI, introducing a novel method that elegantly solves the challenge of generating high-quality transparent images using latent diffusion models. The proposed framework, with its ability to maintain the integrity of the original latent distribution while incorporating transparency, sets a new benchmark for image generation technologies. As the demand for sophisticated visual content creation tools continues to grow, such innovative approaches will play a pivotal role in driving the evolution of digital graphics and beyond.