je-suis-tm/florence_pugh_lora_flux_nf4
Viewer • Updated • 20 • 5
How to use je-suis-tm/florence_pugh_lora_flux with Diffusers:
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("je-suis-tm/florence_pugh_lora_flux")
prompt = "Training With LoRA: Florence Pugh exudes glamour in this image. Her platinum blonde hair is styled in a sleek, slicked-back fashion, complementing a striking black dress. The dress, seemingly crafted from sheer fabric, features delicate straps that wrap and tie at the back, creating a low-cut neckline. A gold chain necklace with a prominent pendant adds a touch of luxury, while small stud earrings complete the ensemble. The backdrop is a clean white wall accented with gray tones. Her posture and attire suggest a formal occasion, perhaps a red-carpet event. The overall impression is one of sophisticated elegance and confident style, drawing the viewer's attention to her poised presence."
image = pipe(prompt).images[0]



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/florence_pugh_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.
export MODEL_NAME="black-forest-labs/FLUX.1-dev"
export INSTANCE_DIR="/pvol/florence_pugh"
export OUTPUT_DIR="/pvol/florence_pugh_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="Florence Pugh" \
--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"
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/florence_pugh_lora_flux",
weight_name='pytorch_lora_weights.safetensors')
prompt = "Portrait, hyper-realistic photograph, HD, 8K, high resolution, extremely detailed. Florence Pugh with dark hair pulled back, and a gold necklace. Her outfit consists of a mint green fuzzy jacket, a pistachio V-neck blouse, and short, tight, thong-style blue capri-style shorts with glitter. On her feet, she wears high-top boots decorated with light blue sequins or beads. In one hand, she holds a shiny box, and the other is brought to her mouth, which is slightly open. The background is an underground parking lot with several cars. Photo for a professional photoshoot for a fashion magazine. Canon EOS-1D X MARK II, realistic style, HDR, HD, 4K, 8K, 32K and 64K resolution, extremely high level of detail."
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("florence_pugh.png")
You should use Florence Pugh to trigger the image generation.
Download them in the Files & versions tab.
Base model
black-forest-labs/FLUX.1-dev