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---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: A person in a bustling cafe retrofuturereality
output:
url: samples/1730449588335__000001000_0.jpg
- text: a white spaceship in the middle of a space station, with a watermark in
the top right corner. The spaceship appears to be in the process of being
built, as evidenced by the various tools and materials scattered around it.
retrofuturereality
output:
url: samples/1730449604541__000001000_1.jpg
- text: a man and woman standing next to each other in a room, smiling. The woman
is wearing a necklace and the man is wearing formal dress. In the background,
there are a number of people and lights retrofuturereality
output:
url: samples/1730449620769__000001000_2.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: retrofuturereality
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
---
# retrofuturereality
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `retrofuturereality` 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](/life/retrofuturereality/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('life/retrofuturereality', weight_name='retrofuturereality.safetensors')
image = pipeline('A person in a bustling cafe retrofuturereality').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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