CivArchive
    Headshot - SDXL lora trained on SD1.5 output - v2.0

    SDXL lora trained on SD1.5. Trained on 'headshot of person' where person is a wildcard.

    update much smaller lora + cleaner dataset (2671 filtered images) + trained longer (8025 steps at batch size 16)


    Larger images at https://ntcai.xyz/sdxl/headshot

    Usage:

    https://ntcai.xyz/comfyui/lorasimplesdxl.json comfyui

    Quick note: Support us and receive an exclusive NFT airdrop as a token of our appreciation. patreon.com/NTCAI

    This process is:

    1. Use img2img on SDXL output using SD1.5. I wrote a tutorial on this here: https://civarchive.com/articles/1430/applying-sd-15-models-and-loras-to-sdxl-1024x1024-comfyui ( mirror )
      Create a large dataset with this technique. I ran this overnight on two A6000s.

      Hint: choose a subject matter that sd1.5 knows well and perhaps reject any distorted images.

    2. Get your files in the correct form. This tutorial helped me:

    3. Train SDXL using https://github.com/kohya-ss/sd-scripts on the generated images.

      # Full command
      CUDA_VISIBLE_DEVICES=0 accelerate launch --num_cpu_threads_per_process=2 "sdxl_train_network.py" --enable_bucket --pretrained_model_name_or_path="/ml2/trained/ComfyUI/models/checkpoints/stable-diffusion-xl-base-1.0/sd_xl_base_1.0.safetensors" --train_data_dir="/ml2/trained/sd-scripts/data/headshot" --resolution="1024,1024" --output_dir="/ml2/trained/ComfyUI/models/loras/Lora/sdxl" --logging_dir="./logs" --save_model_as=safetensors --output_name="headshot2" --network_alpha="1" --network_dim=32 --network_module=networks.lora --text_encoder_lr=0.0004 --unet_lr=0.0004 --lr_scheduler="constant" --train_batch_size="16" --max_train_steps="10000" --mixed_precision="bf16" --save_every_n_epochs="1" --save_precision="bf16" --caption_extension=".txt" --optimizer_type="Adafactor" --optimizer_args scale_parameter=False relative_step=False warmup_init=False --max_data_loader_n_workers="0" --bucket_reso_steps=64 --gradient_checkpointing --xformers --bucket_no_upscale


    My hope is that this can help creators migrate their work. Happy training!



    Resources used:

    Description

    Trained for 8025 steps at batch size 16 on the phrase ' headshot of person ' where person is a wildcard. The phrase was run through the SDXL to SD1.5 pipeline outlined here https://civitai.com/articles/1430/applying-sd-15-models-and-loras-to-sdxl-1024x1024-comfyui

    # Full command
    CUDA_VISIBLE_DEVICES=0 accelerate launch --num_cpu_threads_per_process=2 "sdxl_train_network.py" --enable_bucket --pretrained_model_name_or_path="/ml2/trained/ComfyUI/models/checkpoints/stable-diffusion-xl-base-1.0/sd_xl_base_1.0.safetensors" --train_data_dir="/ml2/trained/sd-scripts/data/headshot" --resolution="1024,1024" --output_dir="/ml2/trained/ComfyUI/models/loras/Lora/sdxl" --logging_dir="./logs" --save_model_as=safetensors --output_name="headshot2" --network_alpha="1" --network_dim=32 --network_module=networks.lora --text_encoder_lr=0.0004 --unet_lr=0.0004 --lr_scheduler="constant" --train_batch_size="16" --max_train_steps="10000" --mixed_precision="bf16" --save_every_n_epochs="1" --save_precision="bf16" --caption_extension=".txt" --optimizer_type="Adafactor" --optimizer_args scale_parameter=False relative_step=False warmup_init=False --max_data_loader_n_workers="0" --bucket_reso_steps=64 --gradient_checkpointing --xformers --bucket_no_upscale

    Reducing the network_dim reduced the filesize

    LORA
    SDXL 1.0
    by ntc

    Details

    Downloads
    269
    Platform
    CivitAI
    Platform Status
    Deleted
    Created
    7/30/2023
    Updated
    8/7/2025
    Deleted
    8/7/2025

    Files

    headshot2.safetensors

    Mirrors

    CivitAI (1 mirrors)