stable-diffusion-2-1-base
Maintainer: stabilityai
583
๐งช
| 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
The stable-diffusion-2-1-base model is a diffusion-based text-to-image generation model developed by Stability AI. It is a fine-tuned version of the stable-diffusion-2-base model, taking an additional 220k training steps with a punsafe=0.98 on the same dataset. This model can be used to generate and modify images based on text prompts, leveraging a fixed, pretrained text encoder (OpenCLIP-ViT/H).
Model inputs and outputs
The stable-diffusion-2-1-base model takes text prompts as input and generates corresponding images as output. The model can be used with the [object Object] repository or the [object Object] library.
Inputs
- Text prompt: A natural language description of the desired image.
Outputs
- Generated image: An image corresponding to the input text prompt, generated by the model.
Capabilities
The stable-diffusion-2-1-base model is capable of generating a wide variety of photorealistic images based on text prompts. It can create images of people, animals, landscapes, and more. The model has been fine-tuned to improve the quality and safety of the generated images compared to the original stable-diffusion-2-base model.
What can I use it for?
The stable-diffusion-2-1-base model is intended for research purposes, such as:
- Generating artworks and using them in design or other creative processes
- Developing educational or creative tools that leverage text-to-image generation
- Researching the capabilities and limitations of generative models
- Probing and understanding the biases of the model
The model should not be used to intentionally create or disseminate images that could be harmful or offensive to people.
Things to try
One interesting aspect of the stable-diffusion-2-1-base model is its ability to generate diverse and detailed images from a wide range of text prompts. Try experimenting with different types of prompts, such as describing specific scenes, objects, or characters, and see the variety of outputs the model can produce. You can also try using the model in combination with other tools or techniques, like image-to-image generation, to explore its versatility and potential 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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