We rebuilt the trainer. Watch your run as it trains, generate with an epoch before it's done, and compare epochs side by side. It's in beta, and you switch it on yourself in your preferences.

Training Studio: Watch It Train, Generate As It Goes

A training and content contest with over 2 Million Buzz in prizes and 50% off H3 generation for the duration of the contest.

The Civitai H3 Contest: 2 Million Buzz Up For Grabs

It would be great if you could share your works using this module with me!

Using colors as trigger words in the 1.0 version doesn't seem like a good idea, so version 2.0 uses other trigger words and increases the training steps.<pre><code>Folder 10_mix_gost: 66 images found
Folder 10_mix_gost: 660 steps
Folder 25_gost: 5 images found
Folder 25_gost: 125 steps
Folder 25_mix_axolotl: 30 images found
Folder 25_mix_axolotl: 750 steps
Folder 25_mix_blueD: 30 images found
Folder 25_mix_blueD: 750 steps
Folder 25_mix_bug: 31 images found
Folder 25_mix_bug: 775 steps
Folder 25_mix_coral: 29 images found
Folder 25_mix_coral: 725 steps
Folder 25_mix_creature: 30 images found
Folder 25_mix_creature: 750 steps
Folder 25_mix_flower: 31 images found
Folder 25_mix_flower: 775 steps
Folder 25_mix_frog: 30 images found
Folder 25_mix_frog: 750 steps
Folder 25_mix_J: 30 images found
Folder 25_mix_J: 750 steps
Folder 25_mix_octopus: 30 images found
Folder 25_mix_octopus: 750 steps
Folder 25_mix_preal: 30 images found
Folder 25_mix_preal: 750 steps
Folder 25_mix_sea slug: 30 images found
Folder 25_mix_sea slug: 750 steps
Folder 25_mix_shark: 29 images found
Folder 25_mix_shark: 725 steps
Folder 25_mix_snow: 30 images found
Folder 25_mix_snow: 750 steps
Folder 25_mix_spring: 31 images found
Folder 25_mix_spring: 775 steps
max_train_steps = 28275
stop_text_encoder_training = 0
lr_warmup_steps = 0

Here is the trainer command as a reference. It will not be executed:

accelerate launch --num_cpu_threads_per_process=2 "train_network.py" 
--pretrained_model_name_or_path="D:/ai/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned.safetensors" 
--train_data_dir="D:/ai/test/image" 
--resolution=768,768 --output_dir="D:/ai/test/model" 
--logging_dir="D:/ai/test/log" 
--network_alpha="128" 
--save_model_as=safetensors 
--network_module=networks.lora 
--text_encoder_lr=5e-5 
--unet_lr=0.0001 
--network_dim=128 
--output_name="25_preal_1v5_768_0001BD5e_NR128NA128off" 
--lr_scheduler_num_cycles="5" 
--learning_rate="0.0001" 
--lr_scheduler="constant" 
--train_batch_size="2" 
--max_train_steps="28275" 
--save_every_n_epochs="1" 
--mixed_precision="bf16" 
--save_precision="bf16" 
--seed="1234" 
--caption_extension=".txt" 
--cache_latents 
--optimizer_type="AdamW8bit" 
--max_data_loader_n_workers="1" 
--clip_skip=2 
--bucket_reso_steps=64 
--xformers 
--bucket_no_upscale</code></pre>

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SD 1.5_1.0

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