DeciDiffusion-v1-0
Maintainer: Deci
138
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
DeciDiffusion-v1-0 is an 820 million parameter text-to-image latent diffusion model developed by Deci. It was trained on the LAION-v2 dataset and fine-tuned on the LAION-ART dataset. Advanced training techniques were used to speed up training, improve performance, and achieve better inference quality compared to similar models like Stable Diffusion v1-4 and Stable Diffusion 2.1.
Model inputs and outputs
DeciDiffusion-v1-0 is a diffusion-based text-to-image generation model. It takes text prompts as input and generates corresponding images as output. The model uses a Variational Autoencoder (VAE) and CLIP's pre-trained Text Encoder as core architectural components, along with a more efficient U-Net-NAS module developed by Deci.
Inputs
- Text prompt: A text description of the desired image
Outputs
- Generated image: The model outputs a corresponding image based on the input text prompt
Capabilities
DeciDiffusion-v1-0 is capable of generating high-quality photorealistic images from text prompts. It was trained using advanced techniques like V-prediction and enforcing zero terminal SNR during training to improve sample efficiency and inference quality. The model is able to generate a wide variety of image types, including landscapes, objects, and scenes.
What can I use it for?
The DeciDiffusion-v1-0 model is intended for research purposes. Potential use cases include generating artworks, exploring the capabilities and limitations of generative models, and developing educational or creative tools. However, the model should not be used to create harmful, discriminatory, or otherwise problematic content.
Things to try
One key feature of DeciDiffusion-v1-0 is its improved computational efficiency compared to similar models. Developers can experiment with using the model for faster text-to-image generation, or explore ways to leverage the more efficient U-Net-NAS architecture in their own projects. Additionally, the model's strong performance on the LAION-ART dataset suggests it may be well-suited for artistic and creative applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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