How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("dandkim/ai-test")

prompt = "DKXJKT"
image = pipe(prompt).images[0]

Ai Test

About this LoRA

This is a LoRA for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.

It was trained on Replicate using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train

Trigger words

You should use DKXJKT to trigger the image generation.

Run this LoRA with an API using Replicate

import replicate

input = {
    "prompt": "DKXJKT",
    "lora_weights": "https://huggingface.co/dandkim/ai-test/resolve/main/lora.safetensors"
}

output = replicate.run(
    "black-forest-labs/flux-dev-lora",
    input=input
)
for index, item in enumerate(output):
    with open(f"output_{index}.webp", "wb") as file:
        file.write(item.read())

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('dandkim/ai-test', weight_name='lora.safetensors')
image = pipeline('DKXJKT').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Training details

  • Steps: 3000
  • Learning rate: 0.0004
  • LoRA rank: 64

Contribute your own examples

You can use the community tab to add images that show off what you’ve made with this LoRA.

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