When it comes to generating images, large-scale AI models have become essential in the fields of computer vision and graphics. However, there has been limited research focused on layered content generation or transparent image generation. Surprisingly, considering the high demand for such capabilities in the market, Most visual content editing software relies heavily on transparent or layered elements to compose and create content. The lack of training data and the complexity of manipulating existing large-scale image generators have contributed to this research gap. A new technique called LayerDiffusion can help address this challenge by using the latent transparency approach. This could be a game changer for creative workflows.
Table of contents
Introducing LayerDiffusion and Latent Transparency
Researchers at Stanford University have introduced a novel approach called LayerDiffusion. This approach allows large-scale pretrained latent diffusion models to generate transparent images and multiple transparent layers.
LayerDiffusion introduces “latent transparency”, which encodes image transparency into latent offsets while preserving the original latent distribution. It trains an encoder to convert pixel-level RGBA channels into a latent offset. Another decoder reconstructs the transparent image from the adjusted latent.
Attention sharing and Layer Rigidity Adaptors allow the generation of multiple transparent layers jointly with harmonic compositions.Thus, The pre-trained diffusion model, like Stable Diffusion, gets fine-tuned with transparency-enabled patients, teaching it to render transparent images.
Performance and Quality of LayerDiffusion
In user studies conducted, it was found that in the majority of cases (97%), users preferred the transparent content generated natively by the LayerDiffusion method compared to previous ad-hoc solutions like generating-then-matting. The quality of the generated transparent images was comparable to real commercial transparent assets, such as those found on Adobe Stock.
Transparent Image Generation Capabilities with LayerDiffusion
The new LayerDiffusion technique brings powerful transparent image generation abilities to AI systems like Stable Diffusion. There are as follows:
1. Single Image Generation
For single-image generation, you just need to provide a text prompt like “man” or “animal.” Then, LayerDiffusion can generate a transparent PNG with the hair, face, and background separated into RGBA channels.
2. Multi-Layer Generation
For this, you need to provide a foreground, background, and complete scene. LayerDiffusion will output separate transparent layers that are composited seamlessly, keeping lighting and geometry coherent.
3. Conditional Layering
Foreground-Conditioned Background
LayerDiffusion can fix the foreground transparent image, generate a background image matched to it, and adapt lighting colour and geometry as needed.
Prompts: “Man sitting on chair”, “man sitting in forest”, “pots on wood table”, “parrot in room”, “parrot in forest”.
Background-Conditioned Foreground
Plus, it can fix the background and generate a matching foreground.
4. Iterative Layering
The model can compose multiple layers iteratively by repeating the background-conditioned foreground model to incrementally build up compositions with any number of transparent layers, which is useful for iterative conception.
5. Compatibility with Control Methods
Users can combine LayerDiffusion with existing control frameworks like ControlNet to guide layer generation, indicating desired layouts, object shapes, etc.
Training Details
To train the LayerDiffusion framework, a human-in-the-loop scheme is employed, collecting data simultaneously. The team collected 1 million layered transparent images with human assistance and GPT-powered prompting. The dataset used for training consists of 1 million transparent images and covers a diverse range of content topics and styles. This dataset enables the training of transparent image generators.
Conclusion
The layerDiffusion approach’s latent transparency brings AI image synthesis to the realm of layered image construction and compositing for the first time. And, it does so while retaining the impressive artistic capabilities models like Stable Diffusion possess. This combination of power and precision promises to significantly enhance creative workflows spanning digital art, VFX, graphic design, and more as capabilities continue advancing rapidly. Moreover, this approach paves the way for advanced visual content creation with transparency effects.
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