stable-diffusion-v1-5
Maintainer: runwayml
10.8K
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| Property | Value |
|---|---|
| Model Link | View 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
stable-diffusion-v1-5 is a latent text-to-image diffusion model developed by runwayml that can generate photo-realistic images from text prompts. It was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and then fine-tuned on 595k steps at 512x512 resolution on the "laion-aesthetics v2 5+" dataset. This fine-tuning included a 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
Similar models include the Stable-Diffusion-v1-4 checkpoint, which was trained on 225k steps at 512x512 resolution on "laion-aesthetics v2 5+" with 10% text-conditioning dropping, as well as the coreml-stable-diffusion-v1-5 model, which is a version of the stable-diffusion-v1-5 model converted for use on Apple Silicon hardware.
Model inputs and outputs
Inputs
- Text prompt: A textual description of the desired image to generate.
Outputs
- Generated image: A photo-realistic image that matches the provided text prompt.
Capabilities
The stable-diffusion-v1-5 model can generate a wide variety of photo-realistic images from text prompts. For example, it can create images of imaginary scenes, like "a photo of an astronaut riding a horse on mars", as well as more realistic images, like "a photo of a yellow cat sitting on a park bench". The model is able to capture details like lighting, textures, and composition, resulting in highly convincing and visually appealing outputs.
What can I use it for?
The stable-diffusion-v1-5 model is intended for research purposes only. Potential use cases include:
- Generating artwork and creative content for design, education, or personal projects (using the Diffusers library)
- Probing the limitations and biases of generative models
- Developing safe deployment strategies for models with the potential to generate harmful content
The model should not be used to create content that is disturbing, offensive, or propagates harmful stereotypes. Excluded uses include generating demeaning representations, impersonating individuals without consent, or sharing copyrighted material.
Things to try
One interesting aspect of the stable-diffusion-v1-5 model is its ability to generate highly detailed and visually compelling images, even for complex or fantastical prompts. Try experimenting with prompts that combine multiple elements, like "a photo of a robot unicorn fighting a giant mushroom in a cyberpunk city". The model's strong grasp of composition and lighting can result in surprisingly coherent and imaginative outputs.
Another area to explore is the model's flexibility in handling different styles and artistic mediums. Try prompts that reference specific art movements, like "a Monet-style painting of a sunset over a lake" or "a cubist portrait of a person". The model's latent diffusion approach allows it to capture a wide range of visual styles and aesthetics.
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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