metadata

tags:
  - text-to-image
  - stable-diffusion
  - lora
  - diffusers
  - image-generation
  - flux
  - safetensors
widget:
  - text: >-
      logo,Minimalist,A man stands in front of a door,his shadow forming the
      word "A",
    output:
      url: images/01.png
  - text: >-
      logo,Minimalist,A pair of chopsticks and a bowl of rice with the word
      "Lee",
    output:
      url: images/02.png
  - text: wablogo,Minimalist,Leaf and cat,logo,
    output:
      url: images/03.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: wablogo, logo, Minimalist
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

FLUX.1-dev-LoRA-Logo-Design

This is a LoRA (Logo-Design) trained on FLUX.1-dev by CJim on Shakker AI.

Showcases

Prompt: logo,Minimalist,A man stands in front of a door,his shadow forming the word "A",

Prompt: logo,Minimalist,A pair of chopsticks and a bowl of rice with the word "Lee",

Prompt: wablogo,Minimalist,Leaf and cat,logo,

Trigger words

You should use wablogo, logo, Minimalist as trigger words. The recommended scale is 0.8 in diffusers.

Usage suggestion

Dual Combination: something and something, e.g., cat and coffee,
Font Combination: a shape plus a letter, e.g., a book with the word "M," or The fingerprint pattern consists of the letters "hp,"
Text Below Graphic: Below the graphic is the word "coffee," directly using with the word "XX" is also feasible

Inference

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design", weight_name="FLUX-dev-lora-Logo-Design.safetensors")
pipe.fuse_lora(lora_scale=0.8)
pipe.to("cuda")

prompt = "logo,Minimalist,A bunch of grapes and a wine glass"
image = pipe(prompt, 
             num_inference_steps=24, 
             guidance_scale=3.5,
            ).images[0]
image.save(f"example.png")

Online Inference

You can also download this model at Shakker AI, where we provide an online interface to generate images.

Acknowledgements

This model is trained by our copyrighted users CJim. We release this model under permissions. The model follows flux-1-dev-non-commercial-license.