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---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: fierce princess, white hair, looking up. in the style of TOK
output:
url: samples/1740141400616__000001000_1.jpg
- text: fierce lion, red eyes. in the style of TOK
output:
url: samples/1740141429380__000001000_2.jpg
- text: fierce panda king, sword. in the style of TOK
output:
url: images/example_rrk1vslci.png
- text: a fierce deer samurai, battlefield. in the style of TOK
output:
url: images/example_xhq6z88qq.png
- text: angry dark chicken, black and white, in the style of TOK
output:
url: images/example_coutth8xk.png
- text: dark fierce llama. in the style of TOK
output:
url: images/example_2uqqt36t1.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: in the style of TOK
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# dark-pointillisme
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `in the style of TOK` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/fffiloni/dark-pointillisme/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('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('fffiloni/dark-pointillisme', weight_name='dark-pointillisme.safetensors')
image = pipeline('close-up of a white tiger, red eyes. in the style of TOK').images[0]
image.save("my_image.png")
```
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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