coreml-stable-diffusion-2-1-base
Maintainer: apple
42
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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
The coreml-stable-diffusion-2-1-base model is a text-to-image generation model developed by Apple using the Stable Diffusion v2-1 base model. It builds upon the stable-diffusion-2-base model by fine-tuning it with an additional 220k steps on the same dataset. This model can be used to generate and modify images based on text prompts.
Model inputs and outputs
The coreml-stable-diffusion-2-1-base model takes text prompts as input and generates corresponding images as output. The model uses a Latent Diffusion Model architecture that combines an autoencoder with a diffusion model trained in the latent space.
Inputs
- Text prompt: A natural language description of the desired image to generate.
Outputs
- Generated image: An image corresponding to the input text prompt, generated by the model.
Capabilities
The coreml-stable-diffusion-2-1-base model can generate a wide variety of photorealistic images from text prompts, including scenes, objects, and abstract concepts. However, it has limitations in rendering legible text, handling complex compositions, and generating accurate representations of faces and people.
What can I use it for?
The coreml-stable-diffusion-2-1-base model is intended for research purposes, such as safe deployment of generative models, probing model limitations and biases, and generating artwork or creative content. It should not be used to create harmful, offensive, or dehumanizing content, or to impersonate individuals without consent.
Things to try
Experiment with different text prompts to see the range of images the model can generate. Try prompts that combine multiple concepts or require complex compositions to better understand the model's limitations. Additionally, you can explore using the model in artistic or educational applications, while being mindful of the potential for bias and misuse.
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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