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 <a target="_blank" rel="ugc" href="https://ntcai.xyz/sdxl/headshot">https://ntcai.xyz/sdxl/headshot</a>Usage:<a target="_blank" rel="ugc" href="https://ntcai.xyz/comfyui/lorasimplesdxl.json">https://ntcai.xyz/comfyui/lorasimplesdxl.json</a> comfyuiQuick note: Support us and receive an exclusive NFT airdrop as a token of our appreciation. <a target="_blank" rel="ugc" href="http://patreon.com/NTCAI">patreon.com/NTCAI</a> This process is:<ol><li>Use img2img on SDXL output using SD1.5. I wrote a tutorial on this here: <a target="_blank" rel="ugc" href="https://civarchive.com/articles/1430/applying-sd-15-models-and-loras-to-sdxl-1024x1024-comfyui">https://civarchive.com/articles/1430/applying-sd-15-models-and-loras-to-sdxl-1024x1024-comfyui</a> ( <a target="_blank" rel="ugc" href="https://ntcai.xyz/articles/applying_stable_diffusion_15_models_loras_controlnet_to_sdxl_with_comfyui_and_img2img/">mirror</a> ) 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.</li><li>Get your files in the correct form. This tutorial helped me:<div data-youtube-video><iframe allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin playlist src="https://www.youtube.com/embed/AY6DMBCIZ3A" start="0" width="640" height="480"></iframe></div></li><li>Train SDXL using <a target="_blank" rel="ugc" href="https://github.com/kohya-ss/sd-scripts">https://github.com/kohya-ss/sd-scripts</a> on the generated images.<pre><code># 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</code></pre></li></ol> My hope is that this can help creators migrate their work. Happy training! Resources used:<ul><li>eyes lora <a target="_blank" rel="ugc" href="https://civarchive.com/models/72447/eyes-trained-with-no-data-trick">https://civarchive.com/models/72447/eyes-trained-with-no-data-trick</a></li><li>SD1.5 aniverse <a target="_blank" rel="ugc" href="https://civarchive.com/models/107842">https://civarchive.com/models/107842</a></li><li>person wildcards <a target="_blank" rel="ugc" href="https://civarchive.com/models/98622/clonecleaner-wildcards-increase-character-consistency">https://civarchive.com/models/98622/clonecleaner-wildcards-increase-character-consistency</a></li></ul>

Headshot - SDXL lora trained on SD1.5 output

headshot.safetensors

v1.0

v2.0

This is a proof of concept for training an SDXL lora on SD1.5 knowledge. Trained on 'headshot of person' where person is a wildcard.More Info Go to https://civitai.com/models/117518/headshot-sdxl-lora-from-sd15-checkpoint-lora

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