---
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: HST
widget:
- text: >-
    HST style autochrome photo, analog camera, blemished extremely elaborate
    mind-bogglingly fine objects within objects and textures within objects
    within textures within objects within subjects within textures within
    objects within textures within subjects within life within objects within
    textures, film photo,  androgynous communist diverse deities amid peaceful
    revolution within revolt within reform within revolution,  consolidate an
    inset progression of co-extending inspiring psychedelicate psychonautical
    images similar to poetic news cycle coverage,  fearless spirits smitten with
    despairlessness, crisp, detailed timelessness, of future solarpunk utopian
    transurbanities, sublime global disassembly of capitalism, postcapitalist
    society, inspired by Walter Benjamin's Theses on the Philosophy of History,
    photorealistic reportage
  output:
    url: images/example_4k24hxgg7.png

---

# Historic_Color_Dev

<!-- <Gallery /> -->

Trained on Replicate using:

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


## Trigger words
You should use `HST` 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('alekseycalvin/historic_color_dev', 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)