Type | |
Stats | 230 555 |
Reviews | (30) |
Published | Apr 1, 2024 |
Base Model | |
Training | Steps: 1,500 Epochs: 10 |
Usage Tips | Clip Skip: 2 Strength: 0.8 |
Trigger Words | CCCPposter |
Hash | AutoV2 EDCE1D93B5 |
关于PonyXL-experimental版本
尝试以PonyXL作为底模进行训练，得到的结果勉强靠近了3.0，但是在影像细节上缺少很多质感，目前仍在探索更好的版本。训练参数见版本相关。
About the PonyXL experimental version
Attempting to train with PonyXL as the base model resulted in a result that was barely close to 3.0, but lacked a lot of texture in image details. We are still exploring a better version. See about the version for training parameters.
关于3.0版本
在3.0版本中，使用了多分辨率噪声技术（金字塔噪声），没有使用正则化，所有图像都被应用到训练中。
光影效果似乎变强了，另外对于规则物体的绘画也更灵敏。缺点是似乎更喜欢画建筑物，在画人物时需要增加1girl或1male的权重。
训练参数如下：
multires_noise_iterations="6"
multires_noise_discount=0.3
In 3.0 version, mutlires noise was used. No Regularization. All images were used in training.
The light and shadow seem to have become stronger, and the painting of regular objects is also more sensitive. The disadvantage is that it seems to prefer to paint buildings, and when drawing characters, you need to increase the weight of 1 girl or 1 male.
Here's the parameter of this version:
multires_noise_iterations="6"
multires_noise_discount=0.3
导言
这是一个用于绘制苏联海报风格的LyCORIS。当权重较低时，它偏向模型本身的风格，而权重较高时则会更偏向海报风。
目前仅在二次元模型上测试过，主要测试模型为AOM3A，viewerMix，使用效果比较好，如果在偏真人的模型上可能可以得到更偏向于原版海报的效果。
训练参数
以NAI Final作为底模训练，使用了36张图片作为训练集，35张图片作为回归训练集，在训练前进行了镜像增强，每张图片重复训练10次，训练10个epoch，最终步数为7000步左右，以下为主要设置参数：
network_dim=32
network_alpha=32
keep_tokens=4
conv_dim=4
conv_alpha=4
lr="1e-4"
unet_lr="1e-4"
text_encoder_lr="1e-5"
batch_size = 2
Introduction
This is a LyCORIS of CCCP poster styles. When the weight is low, it tends to favor the style of the model itself, while when the weight is high, it's more like the poster style.
At present, it has only been tested on the anime model. The main test models are AOM3A and viewerMix, and the effect is relatively good. The effect of this LyCORIS may be more favorable to the original poster on the realistic model.
Training parameters
It is trained based on NAI final. I used 36 images as a training set and 35 images as a regression training set, image enhancement was performed before training. Each image was repeatedly trained 10 times for 10 epochs, with a final number of steps of about 7000. The following are the main setting parameters:
network_ dim=32
network_ alpha=32
keep_ tokens=4
conv_ dim=4
conv_ alpha=4
lr="1e-4"
unet_ lr="1e-4"
text_ encoder_ lr="1e-5"
batch_ size = 2