aleksa-codes / flux-ghibsky-illustration

Flux LoRA, use 'GHIBSKY style' to trigger generation, creates serene and enchanting landscapes with vibrant, surreal skies and intricate, Ghibli-inspired elements reminiscent of the atmospheric beauty found in Makoto Shinkai's works

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Examples

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Run time and cost

This model costs approximately $0.018 to run on Replicate, or 55 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 12 seconds.

Readme

Description

The Flux Ghibsky Illustration model generates landscapes that blend serene, surreal skies with intricate, Ghibli-inspired details. This fusion of styles creates enchanting scenes that capture the essence of both Ghibli’s whimsical charm and Makoto Shinkai’s atmospheric beauty. Perfect for creating dreamy visuals. Feedback is welcome!

Trigger Words

Use GHIBSKY style to trigger the model’s unique aesthetic. Start your prompt with the trigger word, followed by a description of your scene’s elements, such as nature, skies, houses, roads, villages, etc.

If you are getting too realistic images try adding painting to your prompt, for example: GHIBSKY style painting

Training Details (a9f94946)

  • Trained Using: ostris/flux-dev-lora-trainer
  • Number of Images: 35
  • Trigger Word: GHIBSKY
  • Auto-captioning: Enabled
  • Auto-captioning Prefix: “”
  • Auto-captioning Suffix: “, GHIBSKY style”
  • Training Steps: 1000
  • Learning Rate: 0.0004
  • Batch Size: 1
  • LoRA Rank: 16

Hugging Face

Related Tools

If you’re training your own LoRA model and need a replacement for LLaVA auto captioning that some LoRA training apps use, try GPT Image Captioner, an open-source tool I created that generates AI-powered descriptions for images. This tool streamlines the auto-captioning process by providing a downloadable zip file with caption .txt files that match your image filenames. It integrates seamlessly with platforms like fal LoRA Trainer and Replicate LoRA Trainer.

The tool now supports Ollama for local inference in addition to OpenAI models, which require your own API key. You can use it as a web app or clone/fork the repository to run it locally. For Ollama integration with the web version, you may need to set up a tunnel like ngrok or allow additional web origins. More information can be found in the project’s README.