je-suis-tm/jordana_brewster_lora_flux
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Jordana Brewster Lora Flux
<Gallery />
All files are also archived in https://github.com/je-suis-tm/huggingface-archive in case this gets censored.
This a non-quantized version of https://huggingface.co/je-suis-tm/jordana_brewster_lora_flux_nf4. Both are trained on the same dataset. The training is based on https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/test_dreambooth_lora_flux.py. The training took 3 hours on A100 80GB with max VRAM consumption at 35GB. The inference consumes 36GB of VRAM.
Train
export MODEL_NAME="black-forest-labs/FLUX.1-dev"
export INSTANCE_DIR="/pvol/Jordana Brewster"
export OUTPUT_DIR="/pvol/jordana_brewster_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="Jordana Brewster" \
--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
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/jordana_brewster_lora_flux",
weight_name='pytorch_lora_weights.safetensors')
prompt = "Glacier beauty. Beautiful colors. Jordana Brewster stands on a frozen lake, dressed in a dvr dulcesa onepiece made of ral kntarmr fabric, radiating a mysterious allure. The open knit design over her toned stomach reveals fragments of skin, allowing icy light to shine through. She has long straight hair as she stares intently into the camera, the reflection of the glaciers creating a surreal mirror effect."
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("Jordana Brewster.png")Trigger words
You should use Jordana Brewster to trigger the image generation.
Download model
Download them in the Files & versions tab.
