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spockren/
naruto_lora_xl

Name: naruto_lora_xl
Availability: InStock

diffusers

tensorboard

stable-diffusion-xl

stable-diffusion-xl-diffusers

text-to-image

diffusers-training

lora

stabilityai/stable-diffusion-xl-base-1.0

adapter

creativeml-openrail-m

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LoRA text2image fine-tuning - spockren/naruto-lora-xl

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following.

LoRA for the text encoder was enabled: False.

Special VAE used for training: None.

Intended uses & limitations

How to use

python

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_lora_weights("spockren/naruto_lora_xl", weight_name="pytorch_lora_weights.safetensors")
image = pipeline("A naruto with blue eyes").images[0]
image

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

shell

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export OUTPUT_DIR="./xl_lora/naruto"
export HUB_MODEL_ID="naruto-lora-xl"
export DATASET_NAME="lambdalabs/naruto-blip-captions"

accelerate launch --mixed_precision="fp16"  train_text_to_image_lora_sdxl.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME \
  --dataloader_num_workers=8 \
  --resolution=512 \
  --center_crop \
  --random_flip \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=15000 \
  --learning_rate=1e-04 \
  --max_grad_norm=1 \
  --lr_scheduler="cosine" \
  --lr_warmup_steps=0 \
  --output_dir=${OUTPUT_DIR} \
  --push_to_hub \
  --hub_model_id=${HUB_MODEL_ID} \
  --checkpointing_steps=500 \
  --validation_prompt="A naruto with blue eyes." \
  --checkpoints_total_limit=6 \
  --seed=1337