Use flux.1-dev with LoRa in diffusers

flux_lora_diffusers.py

import torch
from diffusers import FluxPipeline

base_model = "D:/flux1-dev"

pipe = FluxPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16, local_files_only=True, use_safetensors=True)
pipe.enable_model_cpu_offload()

lora_path = "D:/training_output/fern.safetensors"
prefix = "fern"
names = [prefix]
weights = [1.0]

pipe.load_lora_weights(lora_path, adapter_name=prefix, local_files_only=True, weight_name=prefix)
pipe.set_adapters(names, adapter_weights=weights)

prompt = "a girl named fernsoso with purple long hair and a black coat and white dress  sits in a forest"

output = "D:/output.png"

image = pipe(prompt=prompt, num_inference_steps=28, guidance_scale=6.5).images[0]
image.save(output)