https://github.com/arasgungore/latent-diffusion-ffhq256-dreambooth
An unconditional generative model trained on FFHQ face data set in 256×256 resolution and then fine-tuned using the Dreambooth method.
https://github.com/arasgungore/latent-diffusion-ffhq256-dreambooth
artificial-intelligence autoencoder diffusers diffusion-models dreambooth ffhq ffhq-dataset generative-adversarial-network generative-adversarial-networks generative-ai generative-model generative-models huggingface latent-diffusion latent-diffusion-models research research-project unconditional-generation unet-image-segmentation unet-segmentation
Last synced: 3 months ago
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An unconditional generative model trained on FFHQ face data set in 256×256 resolution and then fine-tuned using the Dreambooth method.
- Host: GitHub
- URL: https://github.com/arasgungore/latent-diffusion-ffhq256-dreambooth
- Owner: arasgungore
- License: mit
- Created: 2023-12-06T05:59:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-06T09:14:51.000Z (over 1 year ago)
- Last Synced: 2024-01-26T03:40:22.380Z (over 1 year ago)
- Topics: artificial-intelligence, autoencoder, diffusers, diffusion-models, dreambooth, ffhq, ffhq-dataset, generative-adversarial-network, generative-adversarial-networks, generative-ai, generative-model, generative-models, huggingface, latent-diffusion, latent-diffusion-models, research, research-project, unconditional-generation, unet-image-segmentation, unet-segmentation
- Language: Python
- Homepage:
- Size: 25.8 MB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE