description: <p><strong><em><u><span style="color:#ff0000">Trigger</span></u> not needed but <br /><br /><span style="color:#69ffbe">illustration_by_omone_hokoma_agm</span><br /><br /><span style="color:#ffffff">was added to every caption and does seem to make the effect stronger but tends to add agm signature a lot more so id recommend if you use the trigger word adding signature,watermark,artist_name in the negative seems to get rid of it, ill update this though after im done editing all the dataset's images by manually removing each signature using photoshop and then train on that dataset so no extra negative words will be needed</span></em></strong><br /><br /><br /><strong><u><span style="color:#bb33ff">Here's The Parameters</span></u><span style="color:#bb33ff"> </span></strong><br /><br /><br /><strong><span style="color:#ffd500">Training info</span></strong><span>▼</span></p><p><strong><u>Most frequent tags in captions</u></strong></p><p>illustration_by_omone_hokoma_agm  <br />100%</p><p><strong><u>Dataset folder structure</u></strong></p><p><strong><span>Name      </span></strong><br /><span style="color:rgb(243, 244, 246)">40_agm      </span><span>                        </span></p><p><strong>Image Count</strong><br />595 </p><p><strong>Repeats</strong><br />40<br /><strong>Total Images</strong><br />23800<br /><strong><span style="color:rgb(243, 244, 246)">(Total)</span></strong><br />595 <br /><strong>Image Count</strong><br />595 <br /><strong>Repeats</strong><br />40<br /><strong>Total Images</strong><br />23800<br /><br /><br /><strong><u><span style="color:#2e43ff">Training Parameters</span></u></strong><br />{</p><p><span style="color:#ffffff">"ss_adaptive_noise_scale":</span><span style="color:#00ab0e"> "None",</span></p><p><span style="color:#fcfcfc">"ss_caption_dropout_rate":</span><span style="color:#00ab0e"> "0.0",</span></p><p><span style="color:#ffffff">"ss_steps":</span><span style="color:#00ab0e"> "8500",</span></p><p><span style="color:#fcfcfc">"ss_noise_offset":</span><span style="color:#00ab0e"> "None",</span></p><p><span style="color:#ffffff">"ss_sd_scripts_commit_hash": </span><span style="color:#00ab0e">"15dd0a638af86f89dd0c457428e165598d4884a2",</span></p><p><span style="color:#ffffff">"ss_num_batches_per_epoch":</span><span style="color:#00ab0e"> "23800",</span></p><p><span style="color:#ffffff">"ss_color_aug":</span><span style="color:#00ab0e"> "False",</span></p><p><span style="color:#ffffff">"ss_epoch":</span><span style="color:#00ab0e"> "0",</span></p><p><span style="color:#ffffff"> "ss_total_batch_size":</span><span style="color:rgb(0, 171, 14)"> </span><span style="color:#00ab0e">"1",</span></p><p><span style="color:#ffffff">"ss_network_alpha": </span><span style="color:#00ab0e">"64.0",</span></p><p><span style="color:#ffffff">"ss_ip_noise_gamma":</span><span style="color:#00ab0e"> "None",</span></p><p><span style="color:#f7f7f7">"ss_num_epochs"</span><span style="color:#ffffff">:</span><span style="color:#00ab0e"> "1",</span></p><p><span style="color:#ffffff">"ss_session_id":</span><span style="color:#00ab0e"> "3173715531",</span></p><p><span style="color:#ffffff">"ss_network_dim":</span><span style="color:#00ab0e"> "64",</span></p><p><span style="color:#ffffff">"ss_keep_tokens":</span><span style="color:#00ab0e"> "0",</span></p><p><span style="color:#ffffff">"ss_learning_rate":</span><span style="color:#00ab0e"> "1.0",</span></p><p><span style="color:#ffffff">"ss_new_sd_model_hash":</span><span style="color:#00ab0e"> "e6bb9ea85bbf7bf6478a7c6d18b71246f22e95d41bcdd80ed40aa212c33cfeff",</span></p><p><span style="color:#ffffff">"ss_lr_warmup_steps":</span><span style="color:#00ab0e"> "0",</span></p><p><span style="color:#ffffff">"ss_optimizer":</span><span style="color:#00ab0e"> "prodigyopt.prodigy.Prodigy",</span></p><p><span style="color:#ffffff">"ss_caption_dropout_every_n_epochs":</span><span style="color:#00ab0e"> "0",</span></p><p><span style="color:#ffffff">"ss_network_module": </span><span style="color:#00ab0e">"lycoris.kohya",</span></p><p><span style="color:#ffffff">"ss_reg_dataset_dirs": </span><span style="color:#00ab0e">"{}",</span></p><p><span style="color:#ffffff">"ss_sd_model_hash": </span><span style="color:#00ab0e">"be9edd61",</span></p><p><span style="color:#ffffff">"ss_gradient_accumulation_steps":</span><span style="color:#00ab0e"> "1",</span></p><p><span style="color:#ffffff">"ss_bucket_no_upscale": </span><span style="color:#00ab0e">"False",</span></p><p><span style="color:#ffffff">"ss_bucket_info":</span><span style="color:#00ab0e"> "null",</span></p><p><span style="color:#ffffff">"ss_full_fp16":</span><span style="color:#00ab0e"> "False",</span></p><p><span style="color:#ffffff">"ss_mixed_precision": </span><span style="color:#00ab0e">"fp16",</span></p><p><span style="color:#ffffff">"ss_network_dropout":</span><span style="color:#00ab0e"> "0.0",</span></p><p><span style="color:#ffffff">"ss_gradient_checkpointing":</span><span style="color:#00ab0e"> "True",</span></p><p><span style="color:#ffffff">"ss_random_crop":</span><span style="color:#00ab0e"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_prior_loss_weight":</span><span style="color:rgb(0, 171, 14)"> "1.0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_max_grad_norm":</span><span style="color:rgb(0, 171, 14)"> "1.0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_max_bucket_reso":</span><span style="color:rgb(0, 171, 14)"> "None",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_training_comment": </span><span style="color:rgb(0, 171, 14)">"None",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_num_reg_images":</span><span style="color:rgb(0, 171, 14)"> "0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_max_train_steps":</span><span style="color:rgb(0, 171, 14)"> "10000",</span></p><p><span style="color:rgb(245, 245, 245)">"ss_min_snr_gamma": </span><span style="color:rgb(0, 171, 14)">"10.0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_num_train_images":</span><span style="color:rgb(0, 171, 14)"> "23800",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_network_args": </span><span style="color:rgb(0, 171, 14)">"{\"conv_dim\": \"64\", \"conv_alpha\": \"64\", \"factor\": \"-1\", \"use_cp\": \"True\", \"algo\": \"lokr\", \"dropout\": 0.0}",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_shuffle_caption":</span><span style="color:rgb(0, 171, 14)"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_unet_lr": </span><span style="color:rgb(0, 171, 14)">"1.0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_resolution": </span><span style="color:rgb(0, 171, 14)">"(1024, 1024)",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_batch_size_per_device":</span><span style="color:rgb(0, 171, 14)"> "1",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_multires_noise_discount": </span><span style="color:rgb(0, 171, 14)">"0.2",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_flip_aug": </span><span style="color:rgb(0, 171, 14)">"False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_text_encoder_lr": </span><span style="color:rgb(0, 171, 14)">"1.0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_lr_scheduler":</span><span style="color:rgb(0, 171, 14)"> "constant",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_min_bucket_reso": </span><span style="color:rgb(0, 171, 14)">"None",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_zero_terminal_snr":</span><span style="color:rgb(0, 171, 14)"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_lowram":</span><span style="color:rgb(0, 171, 14)"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_seed": </span><span style="color:rgb(0, 171, 14)">"12345",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_multires_noise_iterations": </span><span style="color:rgb(0, 171, 14)">"6",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_base_model_version":</span><span style="color:rgb(0, 171, 14)"> "sdxl_base_v1-0",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_enable_bucket":</span><span style="color:rgb(0, 171, 14)"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_training_started_at":</span><span style="color:rgb(0, 171, 14)"> "1708377854.7039077",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_clip_skip": </span><span style="color:rgb(0, 171, 14)">"None",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_v2":</span><span style="color:rgb(0, 171, 14)"> "False",</span></p><p><span style="color:rgb(255, 255, 255)">"ss_caption_tag_dropout_rate":</span><span style="color:rgb(0, 171, 14)"> "0.0",</span><br /><span style="color:#ffffff">"ss_max_token_length":</span><span style="color:#00ab0e"> "225",</span></p><p><span style="color:#ffffff">"ss_output_name": </span><span style="color:#00ab0e">"AGMXL",</span></p><p><span style="color:#ffffff">"ss_scale_weight_norms":</span><span style="color:#00ab0e"> "None",</span></p><p><span style="color:#ffffff">"ss_training_finished_at": </span><span style="color:#00ab0e">"1708441002.8566256",</span></p><p><span style="color:#ffffff">"ss_sd_model_name": </span><span style="color:#00ab0e">"sdXL_v10VAEFix.safetensors",</span></p><p><span style="color:#ffffff">"ss_cache_latents": </span><span style="color:#00ab0e">"True",</span></p><p><span style="color:#ffffff">"ss_face_crop_aug_range": </span><span style="color:#00ab0e">"None"</span></p><p>}<br /><br /><br /><br /><strong><em><u>This is my 2nd Lora, first was a character Lora so I figured I'd try a style Lora and haven't seen any SDXL Lora's of AGM's styles like there are for SD1.5..Like my first Lora im uploading the exact training Data I used so if those with more experience than me want to make it better having the dataset should make it easier. I used a caption for each image but in my testing it doesn't seem to make much of a difference so no trigger word is needed. Please leave a rating and comment if this works for you, feedback is very important to me, thanks!</u></em></strong></p>
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name: AGM Style AKA 阿戈魔agm (Omone Hokoma AGM)
tags: drawing style illustration 2d agm painting style