hyper-flux-16step
Maintainer: lucataco
12
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
hyper-flux-16step is a 16-step version of the Hyper FLUX LoRA model developed by ByteDance. It is an implementation of the ByteDance/Hyper-SD FLUX.1-dev 8-step model as a Cog model by lucataco. Similar models include the Hyper FLUX 8-step LoRA, SDXL-Lightning by ByteDance, and various other FLUX.1-Dev and FLUX.1-Schnell LoRA explorers by lucataco.
Model inputs and outputs
This model takes a text prompt as input and generates an image based on that prompt. The key inputs include the prompt, aspect ratio, number of outputs, guidance scale, and number of inference steps. The output is an array of image URLs.
Inputs
- Prompt: The text prompt describing the image to generate
- Aspect Ratio: The aspect ratio of the generated image, with options for 1:1, 16:9, 4:3, and custom
- Num Outputs: The number of images to generate (up to 4)
- Guidance Scale: The guidance scale for the diffusion process (0-10)
- Num Inference Steps: The number of inference steps (1-30)
Outputs
- Array of Image URLs: The generated images as an array of URLs
Capabilities
The hyper-flux-16step model can generate high-quality images from text prompts, with a focus on photorealistic styles. It is particularly adept at rendering detailed scenes, objects, and characters. The increased number of inference steps compared to the 8-step version allows for more refined and detailed outputs.
What can I use it for?
The hyper-flux-16step model can be useful for a variety of creative and commercial applications, such as:
- Generating concept art or illustrations for games, films, or books
- Creating product visualizations or marketing imagery
- Exploring creative ideas and inspirations through text-to-image generation
Things to try
One interesting thing to try with the hyper-flux-16step model is experimenting with different guidance scale settings. Increasing the guidance scale can result in more detailed and faithful renderings of the prompt, while lower values can produce more abstract or stylized outputs. You can also try combining this model with other text-to-image models, such as SDXL-Lightning, to explore different artistic styles and approaches.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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