Instructions to use Dino-LeeTaeHun/lumina2-ghibli-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Dino-LeeTaeHun/lumina2-ghibli-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Dino-LeeTaeHun/lumina2-ghibli-lora") prompt = "howl phogh1b11 style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Lumina2 DreamBooth LoRA - Dino-LeeTaeHun/lumina2-ghibli-lora

- Prompt
- howl phogh1b11 style

- Prompt
- howl phogh1b11 style

- Prompt
- howl phogh1b11 style

- Prompt
- howl phogh1b11 style
Model description
These are Dino-LeeTaeHun/lumina2-ghibli-lora DreamBooth LoRA weights for Alpha-VLLM/Lumina-Image-2.0.
The weights were trained using DreamBooth with the Lumina2 diffusers trainer.
Trigger words
You should use gh1b11 style to trigger the image generation.
The following system_prompt was also used used during training (ignore if None): None.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
TODO
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for Dino-LeeTaeHun/lumina2-ghibli-lora
Base model
Alpha-VLLM/Lumina-Image-2.0