stable-diffusion-2
Maintainer: stabilityai
1.8K
🏋️
| 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 model is a diffusion-based text-to-image generation model developed by Stability AI. It is an improved version of the original Stable Diffusion model, trained for 150k steps using a v-objective on the same dataset as the base model. The model is capable of generating high-resolution images (768x768) from text prompts, and can be used with the stablediffusion repository or the diffusers library.
Similar models include the SDXL-Turbo and Stable Cascade models, which are also developed by Stability AI. The SDXL-Turbo model is a distilled version of the SDXL 1.0 model, optimized for real-time synthesis, while the Stable Cascade model uses a novel multi-stage architecture to achieve high-quality image generation with a smaller latent space.
Model inputs and outputs
Inputs
- Text prompt: A text description of the desired image, which the model uses to generate the corresponding image.
Outputs
- Image: The generated image based on the input text prompt, with a resolution of 768x768 pixels.
Capabilities
The stable-diffusion-2 model can be used to generate a wide variety of images from text prompts, including photorealistic scenes, imaginative concepts, and abstract compositions. The model has been trained on a large and diverse dataset, allowing it to handle a broad range of subject matter and styles.
Some example use cases for the model include:
- Creating original artwork and illustrations
- Generating concept art for games, films, or other media
- Experimenting with different visual styles and aesthetics
- Assisting with visual brainstorming and ideation
What can I use it for?
The stable-diffusion-2 model is intended for both non-commercial and commercial usage. For non-commercial or research purposes, you can use the model under the CreativeML Open RAIL++-M License. Possible research areas and tasks include:
- Research on generative models
- Research on the impact of real-time generative models
- Probing and understanding the limitations and biases of generative models
- Generation of artworks and use in design and other artistic processes
- Applications in educational or creative tools
For commercial use, please refer to https://stability.ai/membership.
Things to try
One interesting aspect of the stable-diffusion-2 model is its ability to generate highly detailed and photorealistic images, even for complex scenes and concepts. Try experimenting with detailed prompts that describe intricate settings, characters, or objects, and see the model's ability to bring those visions to life.
Additionally, you can explore the model's versatility by generating images in a variety of styles, from realism to surrealism, impressionism to expressionism. Experiment with different artistic styles and see how the model interprets and renders them.
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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