2023-10-23T12:19:42
Resolution: 1024x1024
Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0
Alpha: 1.0
Module: networks.lora
Learning Rates:
Overall: 0.0005
UNet: 0.0005
Text Encoder: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: cosine_with_restarts
Warmup steps: 0
Training Details:
Epoch: 15
Batches per epoch: 250
Gradient accumulation steps: 1
Images:
Train images: 1000
Regularization images: 0
Noise & Regularization:
Multires noise iterations: 6.0
Multires noise discount: 0.3
Min SNR gamma: 5.0
Zero terminal SNR: True
Max grad norm: 1.0
Clip skip: 1
Dataset: 1 Directory
with 500 images
UNet Weights:
Average magnitude: 2.9637
Average strength: 0.0109
Text Encoder Weights:
(1) Magnitude: 1.7253
| Strength: 0.0086
(2) Magnitude: 1.7251
| Strength: 0.0067
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Starting my GAG Serries π€ π₯
This one is a Potion LORA for 2.1 SD models.
Depending on the model you will get quite different results.
You can use a background remover while generating and get:
again this is a test LORA and is heavily dependent on the input model.
LyCORIS version will be up for testing in a few hours.
And a 2.1 Model is expected in 48-60 hours
For PIXALISATION try:
PIXHELL by R6SPY π€
PS: so far you can use it for demonstration purposes (preview version)
with version 1.0 above (lora and models of GAG series)
two versions will be available:
Licensed - can be licensed for in-game usage upon request (using only my work and licensed images from my end)
Community - using sourced free images and sources but you are responsible is used commercially in your project Β―\_(γ)_/Β― π€ π₯