22h
Models by this creator
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vintedois-diffusion-v0-1
382
The vintedois-diffusion-v0-1 model, created by the Hugging Face user 22h, is a text-to-image diffusion model trained on a large amount of high quality images with simple prompts. The goal was to generate beautiful images without extensive prompt engineering. This model was trained by Predogl and piEsposito with open weights, configs, and prompts. Similar models include the mo-di-diffusion model, which is a fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio, and the Arcane-Diffusion model, which is a fine-tuned Stable Diffusion model trained on images from the TV show Arcane. Model inputs and outputs Inputs Text prompt**: A text description of the desired image. The model can generate images from a wide variety of prompts, from simple descriptions to more complex, stylized requests. Outputs Image**: The model generates a new image based on the input text prompt. The output images are 512x512 pixels in size. Capabilities The vintedois-diffusion-v0-1 model can generate a wide range of images from text prompts, from realistic scenes to fantastical creations. The model is particularly effective at producing beautiful, high-quality images without extensive prompt engineering. Users can enforce a specific style by prepending their prompt with "estilovintedois". What can I use it for? The vintedois-diffusion-v0-1 model can be used for a variety of creative and artistic projects. Its ability to generate high-quality images from text prompts makes it a useful tool for illustrators, designers, and artists who want to explore new ideas and concepts. The model can also be used to create images for use in publications, presentations, or other visual media. Things to try One interesting thing to try with the vintedois-diffusion-v0-1 model is to experiment with different prompts and styles. The model is highly flexible and can produce a wide range of visual outputs, so users can play around with different combinations of words and phrases to see what kind of images the model generates. Additionally, the ability to enforce a specific style by prepending the prompt with "estilovintedois" opens up interesting creative possibilities.
Updated 5/28/2024
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vintedois-diffusion-v0-2
78
The vintedois-diffusion-v0-2 model is a text-to-image diffusion model developed by 22h. It was trained on a large dataset of high-quality images with simple prompts to generate beautiful images without extensive prompt engineering. The model is similar to the earlier vintedois-diffusion-v0-1 model, but has been further fine-tuned to improve its capabilities. Model Inputs and Outputs Inputs Text Prompts**: The model takes in textual prompts that describe the desired image. These can be simple or more complex, and the model will attempt to generate an image that matches the prompt. Outputs Images**: The model outputs generated images that correspond to the provided text prompt. The images are high-quality and can be used for a variety of purposes. Capabilities The vintedois-diffusion-v0-2 model is capable of generating detailed and visually striking images from text prompts. It performs well on a wide range of subjects, from landscapes and portraits to more fantastical and imaginative scenes. The model can also handle different aspect ratios, making it useful for a variety of applications. What Can I Use It For? The vintedois-diffusion-v0-2 model can be used for a variety of creative and commercial applications. Artists and designers can use it to quickly generate visual concepts and ideas, while content creators can leverage it to produce unique and engaging imagery for their projects. The model's ability to handle different aspect ratios also makes it suitable for use in web and mobile design. Things to Try One interesting aspect of the vintedois-diffusion-v0-2 model is its ability to generate high-fidelity faces with relatively few steps. This makes it well-suited for "dreamboothing" applications, where the model can be fine-tuned on a small set of images to produce highly realistic portraits of specific individuals. Additionally, you can experiment with prepending your prompts with "estilovintedois" to enforce a particular style.
Updated 5/28/2024
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cabrita-lora-v0-1
70
Cabrita is a Portuguese language model that was fine-tuned on a Portuguese translation of the Alpaca dataset. This model is based on the LLaMA-7B architecture and was developed by 22h. Similar models include Sabi-7B, another Portuguese language model, and various Alpaca-based models in different languages and model sizes. Model inputs and outputs Cabrita is a text-to-text model, accepting text input and generating text output. The model was fine-tuned on a Portuguese translation of the Alpaca dataset, which consists of a variety of instructions and responses. As a result, the model is well-suited for tasks like question answering, task completion, and open-ended conversation in Portuguese. Inputs Text**: The model accepts natural language text in Portuguese as input. Outputs Text**: The model generates natural language text in Portuguese as output. Capabilities Cabrita is capable of understanding and generating Portuguese text across a variety of domains, including question answering, task completion, and open-ended conversation. The model has been shown to perform well on Portuguese language benchmarks and can be used as a starting point for building Portuguese language applications. What can I use it for? Cabrita can be used for a variety of Portuguese language applications, such as: Language assistants**: Cabrita can be used to build Portuguese-language virtual assistants that can answer questions, complete tasks, and engage in open-ended conversation. Content generation**: The model can be used to generate Portuguese text for a variety of use cases, such as creative writing, article summarization, or product descriptions. Fine-tuning**: Cabrita can be fine-tuned on domain-specific data to create specialized Portuguese language models for applications like customer service, medical diagnosis, or legal analysis. Things to try One interesting aspect of Cabrita is its ability to generate coherent and contextually relevant responses. For example, you could try prompting the model with a question about a specific topic and see how it responds. You could also try providing the model with a series of instructions and see how it handles task completion. Additionally, you could explore the model's capabilities in open-ended conversation by engaging it in a back-and-forth dialogue.
Updated 5/28/2024