![[LuisaP] Tutorial Hypernetwork - Monkeypatch method版本none (ID: 4565)](https://image.1111down.com/xG1nkqKTMzGDvpLrqFT7WA/9c00f573-ce9d-48fd-dd82-6ab01aab0600/width=450/31392.jpeg)
clip 2, vae on, hypernetwork strenght 1.
1-Install Monkeypatch Extension and reload the ui
https://github.com/aria1th/Hypernetwork-MonkeyPatch-Extension
2-Go to create Beta hypernetwork in your train section.
3-Place this layer structure 1,0.1,0.1,1 //thanks queria!, i personally like this so much.
4-Select activation function of hypernetwork:tanh
5-Select Layer weights initialization:xavier normal
6-and finally, create the hypernetwork.
7-now in Train_Gamma, select your new hypernetwork.
8-Hypernetwork Learning rate: 6.5e-3 "this is for the math" so is perfectly normal ,also, 6.5e-4 will cause less damage to original image.
9-enable Show advanced learn rate scheduler options(for Hypernetworks) and Uses CosineAnnealingWarmupRestarts Scheduler.
10-Steps for cycle = number of images in your dataset.
11-Step multiplier per cycle: 1.1 or 1.2
12-Warmup step per cycle = the half of number of images.
13-Minimum learning rate for beta scheduler = 1e-5 [ or 6.5e-7 , will get less style from dataset, but more control ]
14-Decays learning rate every cycle = 0.9 or 1
15a-batchsize 2, grad 1, steps 1000.
15b-you can also do this [ batchsize 2, grad(number of image in dataset divided by two) but for that you only will need something like 250 steps, but personally i don't like it.
16- your prompt file need to be style.txt.
17- you can also try to "Read parameters (prompt, etc...) from txt2img tab when making previews" to see results with the style in your prompt, for example, mine is "girl in a red kimono".
Note: i train with 2 clip skip, none hypernetwork, and 1 hypernetwork strength.
18- and i'ts that! 5 MB of hypernetwork trained in under 10/20 minutes.
描述:
this is one of my first trains using this method
训练词语:
名称: LuisapTutorial_none.pt
大小 (KB): 4530
类型: Model
Pickle 扫描结果: Success
Pickle 扫描信息: **Detected Pickle imports (3)**
```
torch.FloatStorage
collections.OrderedDict
torch._utils._rebuild_tensor_v2
```
病毒扫描结果: Success