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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: newtext
widget:
- text: >-
The man is wearing a black leather jacket with a modern design, paired with
a dark shirt underneath, and his neck is adorned with a thick, shiny gold
chain that reflects the light clearly, adding a touch of bold elegance to
his look. The lighting in the image is striking and bold, casting direct
light on the lower part of his face while drenching the upper half in deep
shadow, creating a dramatic contrast between light and dark. The bright red
background adds a sharp and daring tone to the scene, with color effects
that heighten the sense of drama and intensity. His facial expression
appears calm and contemplative, as he gazes off to the right, seemingly in
thought or reflection. The lighting highlights the texture of his skin
clearly, with subtle reflections on the surface, adding a realistic
dimension to the image. The shadows are short and sharp due to the direct
and focused lighting.
output:
url: images/example_w5hq1bhk4.png
---
# Texttophoto
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `newtext` 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('renoomon/TextTophoto', 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)
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