Open alenknight opened 1 year ago
You can verify the weights of the extracted LoRA with networks/check_lora_weights.py
(see --help for usage). Could you please share the result of the script?
Hi, i've the exactly same result. i'll post here my verified weights in a couple of minutes, and i hope you can resolve it for both of us.
You can verify the weights of the extracted LoRA with
networks/check_lora_weights.py
(see --help for usage). Could you please share the result of the script?
Oops. Sorry. Didn't see this till now (apologies was traveling abroad. I'll check and post a bit later today)
here are the resulting weights:
loading: D:/AiTools/stable-diffusion-webui/models/Lora/FabioKOOL_LORAs.safetensors number of LoRA modules: 556 lora_unet_down_blocks_0_attentions_0_proj_in.lora_down.weight,(128-320-1-1),0.04425150901079178,4.3550139139370003e-07 lora_unet_down_blocks_0_attentions_0_proj_in.lora_up.weight,(320-128-1-1),0.004554857034236193,9.920830734699848e-08 lora_unet_down_blocks_0_attentions_0_proj_out.lora_down.weight,(128-320-1-1),0.04454754292964935,6.031627322045097e-07 lora_unet_down_blocks_0_attentions_0_proj_out.lora_up.weight,(320-128-1-1),0.004330792464315891,1.2118771053337696e-07 lora_unet_down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k.lora_down.weight,(128-320),0.044410280883312225,5.4743480859542615e-08 lora_unet_down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k.lora_up.weight,(320-128),0.009329566732048988,8.818092851470283e-07 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sorry, gonna need some assistance... how do I run the networks/check_lora_weights.py file? (I found it... just run in pycharm? ) also.... not sure where to run --help. I thought it'd be a file in the directory...
meanwhile homely DreamFilmVFX's details maybe will help better.
Thank you sharing the details for weights.
It has values that appear to be valid, so it should have some effect. If you increase the weight (multiplier) when applying it, do you see any change in the generated image?
Thank you sharing the details for weights.
It has values that appear to be valid, so it should have some effect. If you increase the weight (multiplier) when applying it, do you see any change in the generated image?
how do we check the weights. I'd like to do this on mine as well to confirm.... but also I wasn't sure what to do when you mentioned --help
. typing it into cmd terminal in windows didn't seem to do anything pertaining to kohya
interesting. now it's working. seems it was an A4 issue. I even ran the LORA I extracted when I opened this ticket... and it's having an effect.
still.... if you could help me understand how to check the weights or how to run the --help
you mentioned so I can check weights for myself in the future; would be helpful.
thanks!
I'm happy to hear it's working.
From the command prompt, please run python networks\check_lora_weights.py path_to_lora.safetensors
to show the weights of LoRA.
running the LORA extractor isn't doing anything. it used to work months ago... but haven't tried it till now.... command terminal isn't showing any errors, the progress bar runs and says successful... but when i apply the LORA in a model... nothing.
loading original SD model : C:/AI/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned.safetensors UNet2DConditionModel: 64, 8, 768, False, False loading u-net:
loading vae:
loading text encoder:
loading tuned SD model : C:/AI/stable-diffusion-webui/models/Stable-diffusion/MLKyunnymgx/MLKyunymgx_filewords_person.ckpt
UNet2DConditionModel: 64, 8, 768, False, False
loading u-net:
loading vae:
loading text encoder:
create LoRA network. base dim (rank): 256, alpha: 256
neuron dropout: p=None, rank dropout: p=None, module dropout: p=None
apply LoRA to Conv2d with kernel size (3,3). dim (rank): 256, alpha: 256.0
create LoRA for Text Encoder:
create LoRA for Text Encoder: 72 modules.
create LoRA for U-Net: 278 modules.
create LoRA network. base dim (rank): 256, alpha: 256
neuron dropout: p=None, rank dropout: p=None, module dropout: p=None
apply LoRA to Conv2d with kernel size (3,3). dim (rank): 256, alpha: 256.0
create LoRA for Text Encoder:
create LoRA for Text Encoder: 72 modules.
create LoRA for U-Net: 278 modules.
Text encoder is same. Extract U-Net only.
calculating by svd
100%|████████████████████████████████████████████████████████████████████████████████| 278/278 [00:26<00:00, 10.45it/s]
create LoRA network from weights
create LoRA for Text Encoder:
create LoRA for Text Encoder: 0 modules.
create LoRA for U-Net: 278 modules.
enable LoRA for text encoder
enable LoRA for U-Net
Loading extracted LoRA weights:
LoRA weights are saved to: C:\AI\stable-diffusion-webui\models\Lora\Custom\MLKyunnymgx.safetensors