emo_lora_sd

微调脚本

PYTHONPATH=. torchrun examples/pytorch/stable_diffusion/finetune_stable_diffusion.py \
    --model '/root/autodl-tmp/models/AI-ModelScope/stable-diffusion-v2-1/' \
    --prompt "生成一张可爱的表情包图片" \
    --work_dir '/root/autodl-tmp/models/lora_diffusion/' \
    --train_dataset_name '/root/autodl-tmp/emo-visual-data/' \
    --max_epochs 50 \
    --lora_rank 16 \
    --lora_alpha 32 \
    --save_ckpt_strategy 'by_epoch' \
    --logging_interval 1 \
    --train.dataloader.workers_per_gpu 0 \
    --evaluation.dataloader.workers_per_gpu 0 \
    --train.optimizer.lr 1e-4 \
    --sample_nums 10 \
    --num_inference_steps 30 \
    --use_model_config true

推理脚本

from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
import cv2

pipe = pipeline(
    task=Tasks.text_to_image_synthesis,
    model=model_path, # e.g. /root/autodl-tmp/models/AI-ModelScope/stable-diffusion-v2-1/
    lora_dir=lora_path, # e.g. /root/autodl-tmp/models/lora_diffusion/unet/default/
    use_swift=True)
prompt = '一张愤怒的猫眯表情包'
image = pipe({'text': prompt, 'num_inference_steps': 100})
    cv2.imwrite(f'./lora_result.png', image['output_imgs'][0])

环境：使用了3090的显卡，24g显存 SDK下载

#安装ModelScope
pip install modelscope

#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('changmu/emo_sd_lora')