coreml-stable-diffusion-2-1-base
Maintainer: coreml-community
74
🤯
| 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 |
Create account to get full access
Model overview
The coreml-stable-diffusion-2-1-base model is a Core ML converted version of the Stable Diffusion v2-1-base model developed by Stability AI. It is a latent diffusion model that can be used to generate and modify images based on text prompts. The model was fine-tuned from the stable-diffusion-2-base model with an additional 220k steps, and has improved performance compared to the base model.
Model inputs and outputs
The coreml-stable-diffusion-2-1-base model takes text prompts as input and generates corresponding images as output. The text prompts are encoded using a fixed, pretrained text encoder (OpenCLIP-ViT/H), and the generated images are produced in the latent space of the model.
Inputs
- Text prompts: Short text descriptions that describe the desired image to generate.
Outputs
- Generated images: The model outputs images that correspond to the provided text prompts.
Capabilities
The coreml-stable-diffusion-2-1-base model can be used to generate a wide variety of images based on text prompts, including scenes, objects, and abstract concepts. The model has been fine-tuned to improve its performance compared to the base Stable Diffusion v2 model, and can produce higher-quality and more detailed images.
What can I use it for?
The coreml-stable-diffusion-2-1-base model is intended for research purposes, such as understanding the limitations and biases of generative models, generating artworks, and developing creative tools. It could also be used in educational settings or for personal creative projects. However, the model should not be used to intentionally create or disseminate images that are harmful, offensive, or propagate stereotypes.
Things to try
One interesting thing to try with the coreml-stable-diffusion-2-1-base model is to experiment with different text prompts and see how the generated images vary. You could also try using the model's capabilities to assist with creative tasks, such as designing album covers or exploring new artistic styles. Additionally, you could investigate the model's limitations, such as its inability to render legible text or accurately depict faces and people.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🧪
stable-diffusion-2-1-base
583
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 stablediffusion repository or the diffusers 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.
Updated Invalid Date
🤯
coreml-stable-diffusion-2-1-base
42
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.
Updated Invalid Date
⚙️
stable-diffusion-2-1
3.7K
The stable-diffusion-2-1 model is a text-to-image generation model developed by Stability AI. It is a fine-tuned version of the stable-diffusion-2 model, with an additional 55k steps on the same dataset and then a further 155k steps with adjusted "unsafety" settings. Similar models include the stable-diffusion-2-1-base which fine-tunes the stable-diffusion-2-base model. Model inputs and outputs The stable-diffusion-2-1 model is a diffusion-based text-to-image generation model that takes text prompts as input and generates corresponding images as output. The text prompts are encoded using a fixed, pre-trained text encoder, and the generated images are 768x768 pixels in size. Inputs Text prompt**: A natural language description of the desired image. Outputs Image**: A 768x768 pixel image generated based on the input text prompt. Capabilities The stable-diffusion-2-1 model can generate a wide variety of images based on text prompts, from realistic scenes to fantastical creations. It demonstrates impressive capabilities in areas like generating detailed and complex images, rendering different styles and artistic mediums, and combining diverse visual elements. However, the model still has limitations in terms of generating fully photorealistic images, rendering legible text, and handling more complex compositional tasks. What can I use it for? The stable-diffusion-2-1 model is intended for research purposes only. Possible use cases include generating artworks and designs, creating educational or creative tools, and probing the limitations and biases of generative models. The model should not be used to intentionally create or disseminate images that could be harmful, offensive, or propagate stereotypes. Things to try One interesting aspect of the stable-diffusion-2-1 model is its ability to generate images with different styles and artistic mediums based on the text prompt. For example, you could try prompts that combine realistic elements with more fantastical or stylized components, or experiment with prompts that evoke specific artistic movements or genres. The model's performance may also vary depending on the language and cultural context of the prompt, so exploring prompts in different languages could yield interesting results.
Updated Invalid Date
↗️
stable-diffusion-2-base
329
The stable-diffusion-2-base model is a diffusion-based text-to-image generation model developed by Stability AI. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H). The model was trained from scratch on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier. This base model can be used to generate and modify images based on text prompts. Similar models include the stable-diffusion-2-1-base and the stable-diffusion-2 models, which build upon this base model with additional training and modifications. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image. Outputs Image**: The generated image based on the provided text prompt. Capabilities The stable-diffusion-2-base model can generate a wide range of photorealistic images from text prompts. For example, it can create images of landscapes, animals, people, and fantastical scenes. However, the model does have some limitations, such as difficulty rendering legible text and accurately depicting complex compositions. What can I use it for? The stable-diffusion-2-base model is intended for research purposes only. Potential use cases include the generation of artworks and designs, the creation of educational or creative tools, and the study of the limitations and biases of generative models. The model should not be used to intentionally create or disseminate images that are harmful or offensive. Things to try One interesting aspect of the stable-diffusion-2-base model is its ability to generate high-resolution images up to 512x512 pixels. Experimenting with different text prompts and exploring the model's capabilities at this resolution can yield some fascinating results. Additionally, comparing the outputs of this model to those of similar models, such as stable-diffusion-2-1-base and stable-diffusion-2, can provide insights into the unique strengths and limitations of each model.
Updated Invalid Date