You provide in this best settings based on different GB sizes. Did you use runpod to do these tests?
You provide in this best settings based on different GB sizes. Did you use runpod to do these tests?
GAlso is this for Loras or Dreambooth? I noticed the # of epochs is 4, that seems small.
Fyes i use them on runpod too
GGot it. Thank you for sharing
F4 epoch is for 40 repeat
Fso 160 epochs
F
PI'm training LoRa SDXL on my 3060 with 12GB VRAM, 768*768 resolution and it's showing 2 hours and 30 minutes of training time. I've used 15 images with 20 repeats. Is this configuration fine?
--num_cpu_threads_per_process=2 "./sdxl_train_network.py"
--network_alpha="1"
--save_model_as=safetensors
--network_module=networks.lora
--text_encoder_lr=0.0004
--unet_lr=0.0004
--network_dim=32
--output_name="local_test_1"
--lr_scheduler_num_cycles="8"
--no_half_vae
--learning_rate="0.0004"
--lr_scheduler="constant"
--train_batch_size="1"
--max_train_steps="4800"
--save_every_n_epochs="1"
--mixed_precision="fp16"
--save_precision="fp16"
--caption_extension=".txt"
--cache_latents
--cache_latents_to_disk
--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
--full_fp16
--xformers
--bucket_no_upscale
--noise_offset=0.0
--lowram
--num_cpu_threads_per_process=2 "./sdxl_train_network.py"
--network_alpha="1"
--save_model_as=safetensors
--network_module=networks.lora
--text_encoder_lr=0.0004
--unet_lr=0.0004
--network_dim=32
--output_name="local_test_1"
--lr_scheduler_num_cycles="8"
--no_half_vae
--learning_rate="0.0004"
--lr_scheduler="constant"
--train_batch_size="1"
--max_train_steps="4800"
--save_every_n_epochs="1"
--mixed_precision="fp16"
--save_precision="fp16"
--caption_extension=".txt"
--cache_latents
--cache_latents_to_disk
--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
--full_fp16
--xformers
--bucket_no_upscale
--noise_offset=0.0
--lowram
MIt's interesting, because I also have an RTX 3060 12 GB card and I can train a person Lora in 20 minutes on SD 1.5 with 512x512
MI would use 7 repeat and classification images with 10 epochs, no captions, 128/64 weights. For Adafactor use classic parameters for LR and Unet LR: 0.0001, TE: 5e-05, token length: 225, model: RealisticVision 5.1, optimizer args: scale_parameter=False relative_step=False warmup_init=False weight_decay=0.01
PHow were the results obtained?
MI have also looked at the parameters from Furkan and other loras. I experimented with weights and trained on RealisticVision with Adafactor.
MHere is a few sample from yesterday model, and no used hires fix for generation now.
MLook her hands, it's not fixed, just pure prompt
JIs 6700 steps enough for sdxl?
Mand here is a few portrait
Mgenerates stable images even under aux10 model
MWith artUniverse model:
MAgree I made some test for speed/memory. No gradient checkpointing:
xformers + Mem Efficient Attention = xformers without Mem. E. attention
Same speed, same memory usage.
xformers + Mem Efficient Attention = xformers without Mem. E. attention
Same speed, same memory usage.
MI noticed a very interesting thing. I'm training for style and I haven't changed anything else, just the number of epochs from 10 to 30, and I get completely different sample images and the training values are absolutely different. I'm using Prodigy now.
Fyep looking decent
F512x512 much lower resolution
Fit is decent steps yes
Fyep i noticed same
Pbut result was not good 
Fi never got good results with Prodigy
Ftry bf16 too
Fbut reason could be 768x768
Falso add this parameter to optimizer : --max_grad_norm=0.0
Fi will do another research for LoRA
Pok i will follow these tips for next training
J: A tensor with all NaNs was produced in VAE. This could
be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this. Use --disable-nan-check commandline argument to disable this check. How to fix?
be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this. Use --disable-nan-check commandline argument to disable this check. How to fix?
SI wonder what is quality difference if I don't use classification images? training would be much faster, but would I lose a lot of quality?
MI used this parameters for Prodigy, and trained SD 1.5 base model: "use_bias_correction=True" "weight_decay=0.5" "decouple=True" "betas=(0.9, 0.99)" "d_coef=2" "safeguard_warmup=False"
Fthis happens when model totally overtrained
JI don't use with model lora I use model checkpoint Every model doesn't work.Basicly sd 1.5 I found this problem.
Sso is SDXL training essentially the same as 1.5 training just using an SDXL model? @Dr. Furkan Gözükara
Fthe parameters changes too
Flearning rate
F
Sso not 0.000001
Fyes
FI am searching best parameters for SDXL DreamBooth for weeks now
FHuge Stable Diffusion XL (SDXL) Text Encoder DreamBooth training comparison
U-NET is always trained
PNG info (prompts) are in alts
All images are 1024x1024 so download full sizes
https://twitter.com/GozukaraFurkan/status/1711345323900100643
U-NET is always trained
PNG info (prompts) are in alts
All images are 1024x1024 so download full sizes
https://twitter.com/GozukaraFurkan/status/1711345323900100643
Bv23.10.0 adetailer got released. Just noticed because now in the preview of the image a red box appears around the detected face
Bgood job, from a first glance it does not look like it is changing that much? what is your impression
F2023-10-07
- v23.10.0
- 허깅페이스 모델을 다운로드 실패했을 때, 계속 다운로드를 시도하지 않음
- img2img에서 img2img단계를 건너뛰는 기능 추가
- live preview에서 감지 단계를 보여줌 (PR #352)
Fi think it still adds some more extra details
Fso 2e-6 looks like nice. 3e-6 also nice but a little bit starting to cook
Bhow did it influence the training time?