---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: SU1JS0FT
widget:
- text: >-
    SU1JS0FT, Elegant young woman, light-skinned,  with long blonde hair, seated
    in a winter forest.  A delicate flower crown adorns her flowing blonde
    locks.  She wears a form-fitting, off-the-shoulder, corset-style white gown
    embellished with delicate lace and glittering details.  The gown’s
    voluminous skirt cascades around her, showcasing its soft, flowing texture. 
    She wears sheer, light-colored stockings. Her expression is serene and
    wistful.  The snow-covered forest floor is the backdrop for the scene.  
    Tall, snow-laden trees surround her, creating a serene winter wonderland.
    Luminous soft light bathes the scene, enhancing realistic depiction  of
    delicate snowflakes and frost-covered branches.  The setting evokes a
    dreamlike,  fairytale atmosphere. Detail-oriented, photorealistic style;
    detailed illumination; soft lighting. Digital painting. by style SU1JS0FT
  output:
    url: images/example_a5dik3pgo.png

---

# Su1Js0Ft

<Gallery />

Trained on Replicate using:

https://replicate.com/ostris/flux-dev-lora-trainer/train


## Trigger words
You should use `SU1JS0FT` to trigger the image generation.


## 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.float16).to('cuda')
pipeline.load_lora_weights('emrodriguezx/SU1JS0FT', weight_name='lora.safetensors')
image = pipeline('your 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)