---
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
widget:
  - text: 'screenprint tshirt design, a happy cat holding a sign that says "I LOVE  VE REPLICATE", LNTP illustration style'
    output:
      url: "images/1.webp"

  - text: "a t-shirt, LNTP illustration style"
    output:
      url: "images/2.webp"

  - text: "a young girl playing piano, yellow background, LNTP illustration style"
    output:
      url: "images/3.webp"

  - text: "a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style"
    output:
      url: "images/4.webp"

  - text: "a robot, blue background, LNTP illustration style"
    output:
      url: "images/5.webp"

  - text: "girl, orange background, LNTP illustration style"
    output:
      url: "images/6.webp"

instance_prompt: LNTP
---

# Flux latentpop

flux-latentpop features vibrant backgrounds with grungy limited screenprinting color goodness.

It does great with t-shirt designs, general illustrations, and character portraits.

<Gallery />

It was trained on Replicate, here: https://replicate.com/ostris/flux-dev-lora-trainer/train

The training set is comprised of 23 images generated on MidJourney using the `--sref 3102110963` and `--personalize 3xdy3qw` flags. You can find the entire training set here in this repo: `./2024-08-24-latentpop.zip`

Below are the training parameters I used, which seem to work fairly well for illustration/cartoony Flux LoRAs:

```
{
  "steps": 1300,
  "lora_rank": 24,
  "optimizer": "adamw8bit",
  "batch_size": 4,
  "resolution": "512,768,1024",
  "autocaption": true,
  "input_images": "https://replicate.delivery/pbxt/Lg3C1KUPfrRZZvJFaaSTmQ9qtAyXSonLvLSuTuj4Nop9vcSu/2024-08-24-latentpop.zip",
  "trigger_word": "LNTP",
  "learning_rate": 0.0002,
  "autocaption_suffix": "LNTP style",
  "caption_dropout_rate": 0.05,
}
```

Shoutout to @ciguleva on x who originally shared this sref on x: https://x.com/ciguleva/status/1827398343779098720

## Usage

You should use `LNTP` to trigger the image generation. The output images look more stylistically interesting with a `guidance_scale` of ~`2.5`.

## 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('jakedahn/flux-latentpop', 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)