---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=False&allowCommercialUse=RentCivit&allowDerivatives=False&allowDifferentLicense=False
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- concept
- split
- split screen
- protoart

base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: 2splitstyle
widget:
- text: ' '
  
  output:
    url: >-
      24440943.jpeg
- text: ' '
  
  output:
    url: >-
      24429856.jpeg
- text: ' '
  
  output:
    url: >-
      24429858.jpeg
- text: ' '
  
  output:
    url: >-
      24429857.jpeg

---

# Splitstyle 2.0 

<Gallery />





## Model description

<p><span style="color:rgb(193, 194, 197)">Second attempt at creating a LoRa that recruits for different traditional art styles.</span></p><p></p><h3 id="i-would-recommend-having-the-cfg-scale-set-pretty-precise-and-the-step-counts-relatively-high-fwpakxmb0">I would recommend having the CFG scale set pretty precise, and the step counts relatively high</h3><p></p><p>I realized after the training was already completed. I trained us as a style and not a concept so this is the second attempt added a few more images to the data set up and that helps.</p><p>It should now allow you the option to create either two panels or four panel images. with the triggers "<strong>2splitstyle" or "4splitstyle"</strong></p><p><span style="color:rgb(193, 194, 197)">Trained on close-up portraits. Might take a bit more tinkering as it tends to just have two panels instead of four. anyway while i work out the bugs enjoy</span></p>

## Trigger words
You should use `2splitstyle`, `4splitstyle`, `splitstyle`, `split_style`, `evang` to trigger the image generation.
    

## Download model

Weights for this model are available in Safetensors format.

[Download](/brushpenbob/splitstyle-2-0/tree/main) them in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('brushpenbob/splitstyle-2-0', weight_name='Splitstyle_2.0.safetensors')
image = pipeline('`2splitstyle`, `4splitstyle`, `splitstyle`, `split_style`, `evang`').images[0]
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)