pixel-art-style
Maintainer: kohbanye
54
🔍
| 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 pixel-art-style model is a fine-tuned version of the popular Stable Diffusion AI. By adding the token "pixelartstyle" to your prompt, you can generate images with a distinctive pixel art aesthetic. This model is maintained by kohbanye, who has also created similar models like pixelart and Nitro-Diffusion.
Model inputs and outputs
Inputs
- Prompt: A text description of the desired image, with the additional token "pixelartstyle" to enable the pixel art style.
Outputs
- Image: A generated image reflecting the provided prompt in a pixel art style.
Capabilities
The pixel-art-style model can transform a wide range of prompts into pixel art-inspired images. For example, you can generate a "an astronaut riding a horse, pixelartstyle" or other whimsical, retro-inspired scenes with this model.
What can I use it for?
The pixel-art-style model can be useful for creating nostalgic, 8-bit inspired artwork for games, websites, or other digital media. Its distinctive visual style can help bring a touch of vintage charm to your creative projects. You could also experiment with combining this model's outputs with other AI-generated assets or post-processing techniques to achieve unique and compelling results.
Things to try
Try playing with different prompts that evoke classic video game aesthetics, such as "fantasy adventure, pixelartstyle" or "futuristic cityscape, pixelartstyle". The model's ability to capture a range of subjects in a pixel art style makes it a versatile tool for exploring retro-futuristic or chiptune-inspired themes.
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
🏷️
All-In-One-Pixel-Model
186
The All-In-One-Pixel-Model is a Stable Diffusion model trained by PublicPrompts to generate pixel art in two distinct styles. With the trigger word "pixelsprite", the model can produce sprite-style pixel art, while the "16bitscene" trigger word enables the generation of 16-bit scene pixel art. This model is designed to provide a versatile pixel art generation capability, complementing similar models like pixel-art-style and pixelart. Model inputs and outputs Inputs Textual prompts to describe the desired pixel art scene or sprite Trigger words "pixelsprite" or "16bitscene" to specify the desired art style Outputs Pixel art images in the specified 8-bit or 16-bit style, ranging from characters and creatures to landscapes and environments Capabilities The All-In-One-Pixel-Model demonstrates the ability to generate a diverse range of pixel art in two distinct styles. The sprite-style art is well-suited for retro game aesthetics, while the 16-bit scene art can create charming, nostalgic environments. The model's performance is further enhanced by the availability of pixelating tools that can refine the output to achieve a more polished, pixel-perfect look. What can I use it for? The All-In-One-Pixel-Model offers creators and enthusiasts a versatile tool for generating pixel art assets. This can be particularly useful for indie game development, retro-inspired digital art projects, or even as a creative starting point for pixel art commissions. The model's ability to produce both sprite-style and 16-bit scene art makes it a valuable resource for a wide range of pixel art-related endeavors. Things to try Experiment with the model's capabilities by exploring different prompt variations, combining the trigger words with specific subject matter, settings, or artistic styles. You can also try using the provided pixelating tools to refine the output and achieve a more polished, pixel-perfect look. Additionally, consider exploring the similar models mentioned, such as pixel-art-style and pixelart, to further expand your pixel art generation toolkit.
Updated Invalid Date
📉
Min-Illust-Background-Diffusion
59
The Min-Illust-Background-Diffusion model is a fine-tuned version of the Stable Diffusion v1.5 model, trained by ProGamerGov on a selection of artistic works by Sin Jong Hun. This model was trained for 2250 iterations with a batch size of 4, using the ShivamShrirao/diffusers library with full precision, prior-preservation loss, the train-text-encoder feature, and the new 1.5 MSE VAE from Stability AI. A total of 4120 regularization / class images were used from this dataset. Similar models like the Vintedois (22h) Diffusion model and the Stable Diffusion v1-4 model also use Stable Diffusion as a base, but are trained on different datasets and have their own unique characteristics. Model inputs and outputs Inputs Prompt: A text description that the model uses to generate the output image. The model responds best to prompts that include the token **sjh style. Outputs Image**: A generated image that matches the prompt. The model outputs images at 512x512, 512x768, and 512x512 resolutions. Capabilities The Min-Illust-Background-Diffusion model is capable of generating artistic, landscape-style images that capture the aesthetic of the training data. The model performs well on prompts that steer the output towards specific artistic styles, even at a weaker strength. However, the model is not as well-suited for generating portraits and related tasks, as the training data was primarily composed of landscapes. What can I use it for? This model could be useful for projects that require the generation of landscape-style artwork, such as concept art, background designs, or illustrations. The ability to fine-tune the artistic style through prompt engineering makes it a flexible tool for creative applications. However, due to the limitations around portrait generation, this model may not be the best choice for projects that require realistic human faces or characters. For those use cases, other Stable Diffusion-based models like Stable Diffusion v1-4 may be a better fit. Things to try One interesting aspect of this model is its ability to capture specific artistic styles through the use of the sjh style token in the prompt. Experimentation with this token and other style-specific keywords could lead to the generation of unique, visually striking artwork. Additionally, exploring the model's ability to generate landscape-focused images with different perspectives, compositions, and lighting conditions could reveal its versatility and lead to the creation of compelling visual assets.
Updated Invalid Date
🐍
isopixel-diffusion-v1
42
The isopixel-diffusion-v1 is a Stable Diffusion v2-768 model trained by nerijs to generate isometric pixel art. It can be used to create a variety of pixel art scenes, such as isometric bedrooms, sushi stores, gas stations, and magical forests. This model is one of several pixel art-focused models created by nerijs, including PixelCascade128 v0.1 and Pixel Art XL. Model Inputs and Outputs Inputs Textual prompts that include the token "isopixel" to trigger the pixel art style Outputs High-quality isometric pixel art images in 768x768 resolution Capabilities The isopixel-diffusion-v1 model can generate a wide variety of isometric pixel art scenes with impressive detail and cohesive visual styles. The examples provided show the model's ability to create convincing pixel art representations of bedrooms, sushi stores, gas stations, and magical forests. The model performs best with high step counts using the Euler_a sampler and low CFG scales. What Can I Use It For? The isopixel-diffusion-v1 model could be useful for a variety of pixel art-related projects, such as game environments, illustrations, or concept art. The model's ability to create cohesive isometric scenes makes it well-suited for designing pixel art-based user interfaces, icons, or background elements. Additionally, the model's outputs could be used as a starting point for further refinement or post-processing in pixel art tools. Things to Try When using the isopixel-diffusion-v1 model, it's recommended to always use a 768x768 resolution and experiment with high step counts on the Euler_a sampler for the best results. Additionally, using a low CFG scale can help achieve the desired pixel art aesthetic. For even better results, users can employ tools like Pixelator to further refine the model's outputs.
Updated Invalid Date
📊
Illustration-Diffusion
157
The Illustration-Diffusion model, created by the maintainer ogkalu, is a fine-tuned Stable Diffusion model trained on the artwork of illustrator Hollie Mengert. This model allows users to generate images in Hollie's distinct artistic style, which is characterized by a unique 2D illustration look that is scarce in other Stable Diffusion models. While Hollie is not affiliated with this model, the maintainer was inspired by her work and aimed to make it more accessible through this fine-tuned model. The Illustration-Diffusion model can be contrasted with similar models like Comic-Diffusion, which allows users to mix and match various comic art styles, and Van-Gogh-diffusion, which specializes in emulating the iconic visual style of Van Gogh's paintings. Model inputs and outputs Inputs Prompts**: Users can provide text prompts to the Illustration-Diffusion model, with the correct token being "holliemengert artstyle" to invoke the desired artistic style. Outputs Images**: The model generates high-quality 2D illustrations in Hollie Mengert's distinctive style, which can be used for a variety of purposes such as digital art, concept design, and illustration projects. Capabilities The Illustration-Diffusion model excels at generating portraits and landscapes that capture the essence of Hollie Mengert's artwork. The resulting images have a unique, hand-drawn quality with a focus on bold colors, expressive linework, and a flattened perspective. This makes the model particularly well-suited for creating illustrations, concept art, and other visual assets with a distinct 2D aesthetic. What can I use it for? The Illustration-Diffusion model can be a valuable tool for artists, designers, and creators who want to incorporate Hollie Mengert's artistic style into their projects. It can be used to generate illustrations, character designs, background art, and other visual elements for a variety of applications, such as: Concept art and visual development for films, games, and other media Illustration and graphic design for books, magazines, and marketing materials Character design and worldbuilding for tabletop roleplaying games or webcomics Personal art projects and digital sketches By leveraging the model's capabilities, users can create unique and visually striking illustrations without the need for extensive artistic training or experience. Things to try One interesting aspect of the Illustration-Diffusion model is its ability to generate a wide range of imagery, from portraits to landscapes, while maintaining a consistent artistic style. Users can experiment with different prompts and subject matter to see how the model interprets and adapts Hollie Mengert's style to various contexts. Additionally, combining the Illustration-Diffusion model with other fine-tuned models, such as Comic-Diffusion, could lead to intriguing hybrid styles and creative possibilities.
Updated Invalid Date