Concetti Chiave
Proposing CLIP-VQDiffusion for language-free training in text-to-image generation, outperforming state-of-the-art methods on FFHQ dataset.
Sintesi
The content introduces CLIP-VQDiffusion, a model leveraging CLIP and vector quantized diffusion for text-to-image generation without paired datasets. It highlights the challenges of creating text-image paired datasets, the model's architecture, training process, contributions, related works on diffusion models and language-free training, background on VAEs and diffusion models, experiments on COCO and FFHQ datasets with evaluation metrics like FID and IS scores, ablation studies on hyperparameters, prompts used for evaluation, and comparisons with other models like clip2latent, Lafite, and ClipGen.
Statistiche
On the FFHQ dataset, our model outperformed previous state-of-the-art methods by 4.4% in clipscore.
We used 64 text prompts from clip2latent for evaluating the model trained on FFHQ datasets.
Gaussian noise scale α = 0.25 achieved the best CLIP score in both COCO and FFHQ datasets.