This is an experimental collection made of LoRAs trained on 1 image each. They are obviously overfitted, but this is the intended result.Be aware that results are frequently unstable, so use or don't use trigger words accordingly. Don't hesitate to change the rate of LoRA or any trigger word if you are using one.The base model is <a target="_blank" rel="ugc" href="https://civarchive.com/models/5043/suzumehachi">Suzumehachi</a>.<h3>UPDATE:</h3>The workflow.The way I create it is as follows. I find an interesting picture and either make it square or manually crop it into one or two interesting pieces. Then I use BLIP (sometimes with DeepDanbooru) to caption them and check if everything is ok with that. After that, I usually add a special trigger word just in case (to give some punch to LoRA if it will struggle in an unusual prompt environment)I use kohya with the parameters (only the most important ones are shown):number of steps for dataset image - 100-200--network_alpha="128" --text_encoder_lr=5e-5--unet_lr=0.0001--network_dim="128"--lr_scheduler_num_cycles="1"--learning_rate="0.0001"--lr_scheduler="constant"--train_batch_size="2"--mixed_precision="bf16"--clip_skip=2--noise_offset=0.1--min_snr_gamma=5Because of my other workflow, I make 4 variants of a LoRA with different seeds and combine them pair by pair with around 0.7 mult. factor:python.exe "networks\merge_<a target="_blank" rel="ugc" href="http://lora.py">lora.py</a>" --save_precision fp16 --precision fp16 --save_to combined_lora.safetensors --models lora_1.safetensors lora_2.safetensors --ratios 0.7 0.7If something goes wrong I recombine them with different mult. factor or exclude some LoRA-outlier.

One-image LoRAs vol.1

This is a placeholder image. Don't download the "LoRA" file. See other "versions" for actual LoRAs. You can download the dataset zip file, tho.

Files with SHA256: 6b451084760cb8915454c18e4da1eee0efc302876ecd3c07fd40701c64fb8185