Aussielabs
Models by this creator
⛏️
musicgen
525
musicgen is a deployment of Meta's MusicGen model, a state-of-the-art controllable text-to-music generation system. It was developed by the team at aussielabs. musicgen can generate high-quality music from text prompts or continue and mimic existing audio. It is part of the broader AudioCraft library, which contains other impressive audio generation models like AudioGen and EnCodec. Model inputs and outputs Inputs Prompt**: A description of the music you want to generate. Input Audio**: An audio file that will influence the generated music. The generated music can either continue the audio file's melody or mimic its style. Duration**: The desired duration of the generated audio in seconds. Continuation Start/End**: The start and end times of the audio file to use for continuation. Model Version**: The specific MusicGen model to use, such as the "melody" version. Output Format**: The desired format for the generated audio, such as WAV. Normalization Strategy**: The strategy for normalizing the output audio. Temperature**: Controls the "conservativeness" of the sampling process. Top K/P**: Reduces the sampling to the most likely tokens. Classifier Free Guidance**: Increases the influence of the input on the output. Outputs Output**: The generated audio file in the specified format. Capabilities musicgen can generate diverse and high-quality musical compositions from text prompts. It can also continue and mimic existing audio, allowing for creative remixing and mashups. The model is highly controllable, with options to adjust the generated music's style, duration, and other parameters. What can I use it for? musicgen can be used for a variety of applications, such as: Generating custom background music for videos, games, or podcasts Creating unique musical compositions for personal or commercial projects Experimenting with remixing and mashups by continuing or mimicking existing tracks Exploring new musical ideas and styles through text-based prompts Things to try One interesting capability of musicgen is its ability to continue and mimic existing audio. Try providing an audio file as input and experiment with the "continuation" and "melody" options to see how the model can extend or transform the original music. You can also try adjusting the temperature and guidance settings to generate more diverse or controlled outputs.
Updated 6/17/2024
iblend
8
The iblend model, created by the team at Aussielabs, is a powerful tool for blending and compositing images. It shows similarities to other AI models like blip-2 for answering questions about images, gfpgan for face restoration, i2vgen-xl for image-to-video synthesis, and cog-a1111-ui for anime stable diffusion models. However, iblend is uniquely focused on the task of blending and compositing images. Model inputs and outputs The iblend model takes in a variety of inputs to generate blended and composited images. These include a prompt, a control image, guidance scale, negative prompt, and various settings for controlling the output. Inputs Prompt**: The initial text prompt to guide the image generation. Control Image**: A reference image that helps guide the generation process. Guidance Scale**: A scale that controls the strength of the text prompt's influence on the output. Negative Prompt**: Text describing what the model should not include in the output. Scheduling, Conditioning, and Upscaling Settings**: Additional parameters to fine-tune the image generation process. Outputs Array of Image URLs**: The iblend model outputs an array of image URLs representing the blended and composited images. Capabilities The iblend model excels at blending and compositing images in creative and visually striking ways. It can take input images and text prompts and generate new images that seamlessly combine elements from the various inputs. This makes it a powerful tool for artists, designers, and content creators looking to explore new visual styles and compositions. What can I use it for? The iblend model can be used for a variety of applications, such as creating unique album covers, generating concept art for games or films, or producing eye-catching social media content. Its ability to blend and composite images in novel ways opens up a world of creative possibilities for those willing to experiment. By leveraging the iblend model, you can take your visual projects to the next level and stand out from the crowd. Things to try One interesting application of the iblend model is to use it to create surreal, dreamlike compositions by blending disparate elements from different images. Try using a landscape photo as the control image and combining it with abstract shapes, fantastical creatures, or other unexpected visual elements to see what kind of unexpected and evocative results you can generate.
Updated 6/17/2024