README.md

metadata

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: CARAVAGGIO
widget:
  - text: >-
      a coca cola can "sacred elixir" arcana in the style of TOK a trtcrd, tarot
      style
    output:
      url: examples/1.webp
  - text: >-
      a person giving a ted talk on a TED stage with the TED logo, "the speaker"
      in the style of TOK a trtcrd, tarot style
    output:
      url: examples/2.webp
  - text: >-
      a trtcrd of a person on a computer, on the computer you see a meme being
      made with an ancient looking trollface, "the shitposter" arcana, in the
      style of TOK a trtcrd, tarot style
    output:
      url: examples/3.jpeg
  - text: a work cubicle in the style of TOK a trtcrd, tarot style
    output:
      url: examples/4.jpeg
  - text: a dog eating pasta, in the style of TOK a trtcrd, tarot style
    output:
      url: >-
        examples/a1a8466576514faa9119fab78d7aa4a2_fba6e09c329a49a2b5fcf57ec29a8d56.png
  - text: >-
      a man cutting meat from a kebab vertical rotisserie, it reads "Döner" in
      the style of TOK a trtcrd, tarot style
    output:
      url: images/5.webp
  - text: >-
      a man cutting meat from a kebab vertical rotisserie, it reads "Döner" in
      the style of TOK a trtcrd, tarot style
    output:
      url: images/6.jpeg

Flux Lora Caravaggio

Trained on Replicate using:

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

Trigger words

You should use CARAVAGGIO to trigger the image generation.

Use it with the 🧨 diffusers library

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('ludocomito/flux-lora-caravaggio', 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