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
File size: 531 Bytes
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---
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.
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