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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=False
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- style
- mosaic
- candy
- pill
- pills
- candies
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Pill and Candy Mosaic
widget:
- text: Pill and Candy Mosaic. cute cat
output:
url: 51623373.jpeg
- text: Pill and Candy Mosaic. Planet earth floating in space
output:
url: 51622606.jpeg
- text: Pill and Candy Mosaic. Planet earth floating in space
output:
url: images/example_yk3zu5j5u.png
- text: Pill and Candy Mosaic. A cute closeup on a Llama's head
output:
url: images/example_atajggya0.png
datasets:
- Norod78/PillAndCandyMosaic
---
# Pill and Candy Mosaic Style [FLUX]
<Gallery />
([CivitAI](https://civitai.com/models/1140807))
## Model description
<p>Have <em>Pill and Candy Mosaic </em>in your prompts </p>
## Trigger words
You should use `Pill and Candy Mosaic` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Norod78/pill-and-candy-mosaic-style-flux/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
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Norod78/pill-and-candy-mosaic-style-flux', weight_name='Pill_and_Candy_Mosaic_Style_FLUX.safetensors')
image = pipeline('Pill and Candy Mosaic. Two cyclists in the foreground and one in the background').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)