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: CNSTLL
widget:
  - text: >-
      in the style of CNSTLL, a white car parked in front of a gas station,
      night time, cinestill 800T
    output:
      url: 1.png
  - text: >-
      in the style of CNSTLL , urban landscape, people fishing on Galata  bridge
      in Istanbul at night
    output:
      url: 2.png
  - text: in the style of CNSTLL , photo of New York City at night, 4k
    output:
      url: 3.png
  - text: >-
      in the style of CNSTLL, an urban landscape with street sellers and  a fish
      market at night, photorealistic
    output:
      url: 4.png

Flux Cinestill

Prompt
in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T

Prompt
in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night

Prompt
in the style of CNSTLL , photo of New York City at night, 4k

Prompt
in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic

FLUX.1-Dev LoRA fine tuned on Cinestill 800T images on Replicate using:

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

Best suited for generating night and dusk time photograph-like images with a distinctive slight halation effect. Keywords that result in better generations "cinestill 800t", "night time", "dusk", "4k", "high resolution", "analog film".

Trigger words

You should use CNSTLL 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('adirik/flux-cinestill', 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