Bytedance

Models by this creator

594.8K

sdxl-lightning-4step

bytedance

sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.

Updated 12/8/2024

Text-to-Image

3.2K

hyper-flux-8step

bytedance

hyper-flux-8step is a text-to-image AI model developed by ByteDance. It is a variant of the ByteDance/Hyper-SD FLUX.1-dev model, which is a diffusion-based model trained to generate high-quality images from textual descriptions. The hyper-flux-8step version uses an 8-step inference process, compared to the 16-step process of the original Hyper FLUX model. This makes it faster to run while still producing compelling images. The model is similar to other AI text-to-image models like sdxl-lightning-4step and hyper-flux-16step, all of which are developed by ByteDance. These models offer varying trade-offs between speed, quality, and resource requirements. Model inputs and outputs The hyper-flux-8step model takes a text prompt as input and generates one or more corresponding images as output. The input prompt can describe a wide variety of subjects, scenes, and styles, and the model will attempt to create visuals that match the description. Inputs Prompt**: A text description of the image you want the model to generate. Seed**: A random seed value to ensure reproducible generation. Width/Height**: The desired width and height of the generated image, if using a custom aspect ratio. Num Outputs**: The number of images to generate (up to 4). Aspect Ratio**: The aspect ratio of the generated image, such as 1:1 or custom. Output Format**: The file format for the generated images, such as WEBP or PNG. Guidance Scale**: A parameter that controls the strength of the text-to-image guidance. Num Inference Steps**: The number of steps to use in the diffusion process (8 in this case). Disable Safety Checker**: An option to disable the model's safety checks for inappropriate content. Outputs One or more image files in the requested format, corresponding to the provided prompt. Capabilities The hyper-flux-8step model is capable of generating a wide variety of high-quality images from textual descriptions. It can create realistic scenes, fantastical creatures, abstract art, and more. The 8-step inference process makes it faster to use compared to the 16-step version, while still producing compelling results. What can I use it for? You can use hyper-flux-8step to generate custom images for a variety of applications, such as: Illustrations for articles, blog posts, or social media Concept art for games, films, or other creative projects Product visualizations or mockups Unique artwork and designs for personal or commercial use The speed and quality of the generated images make it a useful tool for rapid prototyping, ideation, and content creation. Things to try Some interesting things you could try with the hyper-flux-8step model include: Generating images with specific art styles or aesthetics by including relevant keywords in the prompt. Experimenting with different aspect ratios and image sizes to see how the model handles different output formats. Trying out the [disable_safety_checker] option to see how it affects the generated images (while being mindful of potential issues). Combining the hyper-flux-8step model with other AI tools or workflows to create more complex visual content. The key is to explore the model's capabilities and see how it can fit into your creative or business needs.

Updated 12/8/2024

Text-to-Image

842

hyper-flux-16step

bytedance

hyper-flux-16step is a text-to-image generation model developed by ByteDance, the parent company of TikTok. Similar to other ByteDance AI models like SDXL-Lightning 4-step and Hyper FLUX 8-step, hyper-flux-16step is capable of generating high-quality images from text prompts. It is a 16-step variant of the Hyper FLUX model, which may offer improved performance or capabilities compared to the 8-step version. Model inputs and outputs hyper-flux-16step takes a variety of inputs to control the image generation process, including the text prompt, image size and aspect ratio, seed for reproducibility, and settings like guidance scale and inference steps. The model outputs one or more image files in the WebP format, which can then be used or further processed as needed. Inputs Prompt**: The text prompt that describes the desired image Seed**: A random seed value for reproducible generation Width/Height**: Dimensions of the generated image (when using custom aspect ratio) Aspect Ratio**: Aspect ratio of the generated image (e.g. 1:1, 16:9) Num Outputs**: Number of images to generate per prompt Guidance Scale**: Strength of the text guidance during the diffusion process Num Inference Steps**: Number of steps in the diffusion process Outputs Image(s)**: One or more image files in the WebP format Capabilities hyper-flux-16step can generate a wide variety of photorealistic images from text prompts, with the 16-step process potentially offering improved quality or fidelity compared to the 8-step variant. The model appears capable of rendering detailed scenes, objects, and characters with strong adherence to the provided prompt. What can I use it for? With its text-to-image capabilities, hyper-flux-16step could be useful for a range of applications, such as creating custom images for marketing, illustration, concept art, or product visualization. The model's speed and quality may also make it suitable for rapid prototyping or ideation. As with other AI-generated content, it's important to consider the ethical implications and potential for misuse when using this technology. Things to try Experiment with the hyper-flux-16step model by providing detailed, imaginative prompts that challenge the model's abilities. Try incorporating specific styles, themes, or artistic references to see how the model responds. You can also explore using different settings, like higher guidance scales or more inference steps, to observe the impact on the generated images.

Updated 12/8/2024

Image-to-Image

piano-transcription

bytedance

The piano-transcription model is a high-resolution piano transcription system developed by ByteDance that can detect piano notes from audio. It is a powerful tool for converting piano recordings into MIDI files, enabling efficient storage and manipulation of musical performances. This model can be compared to similar music AI models like cantable-diffuguesion for generating and harmonizing Bach chorales, stable-diffusion for generating photorealistic images from text, musicgen-fine-tuner for fine-tuning music generation models, and whisperx for accelerated audio transcription. Model inputs and outputs The piano-transcription model takes an audio file as input and outputs a MIDI file representing the transcribed piano performance. The model can detect piano notes, their onsets, offsets, and velocities with high accuracy, enabling detailed, high-resolution transcription. Inputs audio_input**: The input audio file to be transcribed Outputs Output**: The transcribed MIDI file representing the piano performance Capabilities The piano-transcription model is capable of accurately detecting and transcribing piano performances, even for complex, virtuosic pieces. It can capture nuanced details like pedal use, note velocity, and precise onset and offset times. This makes it a valuable tool for musicians, composers, and music enthusiasts who want to digitize and analyze piano recordings. What can I use it for? The piano-transcription model can be used for a variety of applications, such as converting legacy analog recordings into digital MIDI files, creating sheet music from live performances, and building large-scale classical piano MIDI datasets like the GiantMIDI-Piano dataset developed by the model's creators. This can enable further research and development in areas like music information retrieval, score-informed source separation, and music generation. Things to try Experiment with the piano-transcription model by transcribing a variety of piano performances, from classical masterpieces to modern pop songs. Observe how the model handles different styles, dynamics, and pedal use. You can also try combining the transcribed MIDI files with other music AI tools, such as musicgen, to create new and innovative music compositions.

Updated 12/8/2024

Audio-to-Text

res-adapter

bytedance

res-adapter is a plug-and-play resolution adapter developed by ByteDance's AutoML team. It enables any diffusion model to generate resolution-free images without additional training, inference, or style transfer. This is in contrast to similar models like real-esrgan which require separate upscaling, or kandinsky-2 and kandinsky-2.2 which are trained on specific datasets. ResAdapter is designed to be compatible with a wide range of diffusion models. Model inputs and outputs ResAdapter takes a text prompt as input and generates an image as output. The model is able to generate images at resolutions outside of its original training domain, allowing for more flexible and diverse outputs. Inputs Prompt**: The text prompt describing the desired image Width/Height**: The target output image dimensions Resadapter Alpha**: The weight to apply the ResAdapter, ranging from 0 to 1 Outputs Image**: The generated image at the specified resolution Capabilities ResAdapter can generate high-quality, consistent images at resolutions beyond a model's original training domain. This allows for more flexibility in the types of images that can be produced, such as generating large, detailed images from small models or vice versa. The model can also be used in combination with other techniques like ControlNet and IP-Adapter to further enhance the outputs. What can I use it for? ResAdapter can be used for a variety of text-to-image generation tasks, from creating detailed fantasy scenes to generating stylized portraits. Its ability to produce resolution-free images makes it suitable for use cases where flexibility in image size is important, such as in graphic design, video production, or virtual environments. Additionally, the model's compatibility with other techniques allows for even more creative and customized output. Things to try Experiment with different values for the Resadapter Alpha parameter to see how it affects the quality and consistency of the generated images. Try using ResAdapter in combination with other models and techniques to see how they complement each other. Explore a wide range of prompts to discover the model's versatility and capability in generating diverse, high-quality images.

Updated 12/8/2024

Image-to-Image