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--- |
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license: apache-2.0 |
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datasets: |
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- multi-train/coco_captions_1107 |
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- visual_genome |
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language: |
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- en |
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pipeline_tag: text-to-image |
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tags: |
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- scene_graph |
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- transformers |
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- laplacian |
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- autoregressive |
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- vqvae |
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--- |
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# trf-sg2im |
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Model card for the paper __"[Transformer-Based Image Generation from Scene Graphs](https://arxiv.org/abs/2303.04634)"__. |
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Original GitHub implementation [here](https://github.com/perceivelab/trf-sg2im). |
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## Model |
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This model is a two-stage scene-graph-to-image approach. It takes a scene graph as input and generates a layout using a transformer-based architecture with Laplacian Positional Encoding. |
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Then, it uses this estimated layout to condition an autoregressive GPT-like transformer to compose the image in the latent, discrete space, converted into the final image by a VQVAE. |
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## Usage |
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For usage instructions, please refer to the original [GitHub repo](https://github.com/perceivelab/trf-sg2im). |
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## Results |
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Comparison with other state-of-the-art approaches |
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