flux-80s-cyberpunk
Maintainer: fofr
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The flux-80s-cyberpunk model is a Flux LoRA (LatentOverriding Attention) model trained on a 1980s cyberpunk aesthetic, as described by the maintainer fofr. This model can be used to generate images with a distinct 80s cyberpunk style, and can be combined with other LoRA models like [object Object], [object Object], [object Object], [object Object], and [object Object] to achieve unique and interesting results.
Model inputs and outputs
The flux-80s-cyberpunk model takes in a variety of inputs, including an input image, a prompt, and various parameters that control the generation process. The outputs are one or more images that match the provided prompt and input.
Inputs
- Prompt: The text prompt that describes the desired image. Using the "trigger word" from the training process can help activate the trained style.
- Image: An input image for inpainting or img2img mode.
- Mask: A mask for the input image, where black areas will be preserved and white areas will be inpainted.
- Seed: A random seed value for reproducible generation.
- Model: The specific model to use for the inference, with options for "dev" and "schnell" which have different performance characteristics.
- Width/Height: The desired dimensions of the generated image, if using a custom aspect ratio.
- Aspect Ratio: The aspect ratio of the generated image, with options like "1:1", "4:3", and "custom".
- Num Outputs: The number of images to generate (up to 4).
- Guidance Scale: The guidance scale for the diffusion process, which affects the realism of the generated images.
- Prompt Strength: The strength for inpainting, where 1.0 corresponds to full destruction of information in the input image.
- Num Inference Steps: The number of steps for the inference process, where more steps can lead to more detailed images.
- Extra LoRA: Additional LoRA models to combine with the primary model.
- LoRA Scale: The scale factor for applying the primary LoRA model.
- Extra LoRA Scale: The scale factor for applying the additional LoRA model.
- Output Format: The format of the output images, such as WEBP or PNG.
- Output Quality: The quality setting for the output images.
- Replicate Weights: Optional custom weights to use for the Replicate LoRA.
- Disable Safety Checker: A flag to disable the safety checker for the generated images.
Outputs
- Output Images: One or more images generated by the model, in the specified format and quality.
Capabilities
The flux-80s-cyberpunk model can generate images with a distinct 1980s cyberpunk aesthetic, including elements like neon lights, futuristic cityscapes, and retro-futuristic technology. By combining this model with other Flux LoRA models, you can create unique and interesting image compositions that blend different styles and concepts.
What can I use it for?
The flux-80s-cyberpunk model can be useful for a variety of projects and applications, such as:
- Generating concept art or illustrations for 80s-inspired sci-fi or cyberpunk stories, games, or movies.
- Creating social media content, graphics, or artwork with a retro-futuristic aesthetic.
- Exploring and experimenting with different styles and combinations of AI-generated art.
Things to try
To get the most out of the flux-80s-cyberpunk model, you can try:
- Experimenting with different prompts and trigger words to see how they influence the generated images.
- Combining the model with other Flux LoRA models, such as [object Object] or [object Object], to create unique blends of styles.
- Adjusting the model parameters, like the guidance scale and number of inference steps, to find the right balance between realism and stylization.
- Using the inpainting and img2img capabilities to transform existing images or fill in missing areas with the 80s cyberpunk aesthetic.
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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