Furry AI Art Generator

Enter a description of what you want to see, and AI will create a unique image based on your input. The more detailed text you make, the easier it will be for AI to understand what result you want to achieve. You may add the art styles for a more impressive artwork, such as Impressionism or Cubism. If you need to improve the result, just try once again or make changes to your prompt. Each time the image generator will create a new image for you.

Use English, Spanish, French, Russian, Chinese, or German. Use English for all other languages.

The AI model is trained on a huge array of image-text pairs collected on the internet. That makes it possible to create stunning works based on the user's prompt.

Here are some of the styles you can use from our templates: Watercolor; Oil painting; Pencil sketch; Street art; Impressionism; Surrealism; Cubism; Pop art; Fauvism, Cartoon; Sci-Fi, Fantasy; Anime. We will gradually increase the number of styles, but if the style in which you want to create an image is not on the list, try adding its name and description to your text query.

The service is available for users of all tariffs.

Yes, after the image has been generated, you can download it to your device.

When you submit your query, you get into a queue to create an image (your number in the queue and approximate waiting time appears in the message). The image will be created as soon as your turn is up.

You can find good examples of using AI-generated images in absolutely different areas, like graphic design, tattoos, or unique screensavers. You are free to implement your most interesting ideas, but remember about ethics and responsibility - it is unacceptable to create content that offends and humiliates other people, nor content that violates copyright and license rights.

A negative prompt allows the user to specify what he doesn’t want to see in a generated picture. It may prevent generating specific things, styles, fix some image abnormalities, and greatly improve the quality. The most commonly used negative prompts are: worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, horror, etc. Check some examples:

Every single generated image as a unique attribute called “Seed”. You don't need to come up with the number yourself because it is randomly generated when not specified. Controlling the seed can help you generate reproducible images, experiment with other parameters, or prompt variations. For example, you can type in a prompt “Boy in a T-shirt” and the seed of 538637855, and try just to change the color of a T-shirt in a prompt. The pictures will be almost identical despite the color of a T-shirt. Note: In case you want to control seed, you should type in about 9-digit number, so that it would be sufficient (like in example above).

Generally speaking, the more steps you use, the better quality you'll achieve. But you shouldn't set steps as high as possible. First, we will introduce you the steps parameter in general. AI models are iterative processes – a repeated cycle that starts with a random noise generated from text input. Some noise is removed with each step, resulting in a higher-quality image over time. The repetition stops when the desired number of steps completes. Around 25 sampling steps are usually enough to achieve high-quality images. Using more may produce a slightly different picture, but not necessarily better quality. For example, compare 10 steps and 50 steps image generations:

The sampler is responsible for carrying out the denoising steps. What is this? To produce an image, AI first generates a completely random image. The noise predictor then estimates the noise of the image. The predicted noise is subtracted from the image. This process is repeated a dozen times. In the end, you get a clean image. This denoising process is called sampling because AI generates a new sample image in each step. The method used in sampling is called the sampler. You can generate pictures of different quality and various degree of realism by choosing different samplers. Here you can see the examples for samplers introduced in BgRem, so that you can choose your favorite for your future generations: