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---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- base model
- dalle2
base_model: stabilityai/stable-diffusion-xl-base-1.0
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---
# [LuisaP❤️]NOT DALLE2 [27MB]
<Gallery />
## Model description
<h1 id="sdxl-1.0-base-only-higly-recomended.-d57lx1zpt"><strong><span style="color:#fa5252">SDXL 1.0 BASE ONLY, HIGLY RECOMENDED.</span></strong></h1><p><strong>About: Trained on selected dataset of <s>dalle-2 </s>images, need the default model to work.</strong></p><p>Default model: <a target="_blank" rel="ugc" href="https://civitai.com/models/101055/sd-xl?modelVersionId=128078">https://civitai.com/models/101055/sd-xl?modelVersionId=128078</a></p><p><strong>For Foocus, styles can improve your image look, more description is better.</strong></p><p>❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️<br /><strong>Do and tips: </strong>1 weight for generation / 1.2/1.5 weight for img2img</p><p><strong>Keyword: oil painting and fantasy can bring back some esque for different models.</strong></p>
## Download model
Weights for this model are available in Safetensors format.
[Download](/multimodalart/luisap-not-dalle2-27mb/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('multimodalart/luisap-not-dalle2-27mb', weight_name='DARRE2.safetensors')
image = pipeline('Your custom prompt').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)
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