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:
- apple
- flux
- diffusers
- lora
- replicate
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: APPLE
Apple
About this LoRA
this was trained on 64 of the same image of an apple here is the link
Trigger words
You should use APPLE to trigger the image generation.
Run this LoRA with an API using Replicate
import replicate
input = {
"prompt": "APPLE",
"lora_weights": "https://huggingface.co/boisterous/apple/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('boisterous/apple', weight_name='lora.safetensors')
image = pipeline('APPLE').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
Contribute your own examples
You can use the community tab to add images that show off what you’ve made with this LoRA.