    export MODEL_NAME="black-forest-labs/FLUX.1-dev"
    export INSTANCE_DIR="/pvol/monica_bellucci"
    export OUTPUT_DIR="/pvol/monica_bellucci_lora_flux"
    accelerate config default
    accelerate launch train_dreambooth_lora_flux1.py \
      --pretrained_model_name_or_path=$MODEL_NAME  \
      --mixed_precision="bf16" \
      --dataset_name=$INSTANCE_DIR \
      --output_dir=$OUTPUT_DIR \
      --gradient_checkpointing \
      --instance_prompt="Monica Bellucci" \
      --caption_column="text" \
      --resolution=1024 \
      --train_batch_size=1 \
      --guidance_scale=1 \
      --use_8bit_adam \
      --checkpointing_steps=100 \
      --gradient_accumulation_steps=4 \
      --optimizer="adamW" \
      --learning_rate=1e-4 \
      --lr_scheduler="constant" \
      --lr_warmup_steps=100 \
      --max_train_steps=1500 \
      --rank=4 \
      --seed="0"

Usage

python

import torch
from diffusers import FluxPipeline

device = "cuda:0"

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)

pipe.load_lora_weights("je-suis-tm/monica_bellucci_lora_flux",
                       weight_name='pytorch_lora_weights.safetensors')


prompt = "portrait of a beautiful mermaid called Monica Bellucci. there are a lot of small goldfish around and against the background of marine life. the light barely breaks through the water column. Cinematic, 35mm, F/2. 8, Vignette, 4k, Spotlight, Cinematic Lighting, insanely detailed and intricate, elegant, ornate, super detailed, --ar 1:2 --q 2"

image = pipe(
  prompt=prompt,
  generator=torch.Generator(device=device).manual_seed(42),
  num_inference_steps=50, # 28 is a good trade-off
  guidance_scale=4,
  height=1024, 
  width=1024,
).images[0]

image.save("monica_bellucci.png")

Trigger words

You should use Monica Bellucci to trigger the image generation.

Download model

Download them in the Files & versions tab.