Instructions to use MouseTrap/StyleGen-Loopster-DL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MouseTrap/StyleGen-Loopster-DL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("riffusion/riffusion-model-v1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("MouseTrap/StyleGen-Loopster-DL") prompt = "Loopster style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: riffusion/riffusion-model-v1 | |
| instance_prompt: Loopster style | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| # LoRA DreamBooth - MouseTrap/StyleGen-Looper | |
| These are LoRA adaption weights for riffusion/riffusion-model-v1. The weights were trained on Loopster style using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. | |
| LoRA for the text encoder was enabled: False. | |