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findix /sd-scripts:fbd7a9ec
Input schema
The fields you can use to run this model with an API. If you don’t give a value for a field its default value will be used.
| Field | Type | Default value | Description |
|---|---|---|---|
| pretrained_model_name_or_path |
string
|
CompVis/stable-diffusion-v1-4
|
base model name or path | 底模名称或路径
|
| train_data_zip |
string
|
train dataset zip file | 训练数据集zip压缩包
|
|
| network_weights |
string
|
pretrained weights for LoRA network | 若需要从已有的 LoRA 模型上继续训练，请上传文件
|
|
| training_comment |
string
|
this LoRA model credit from replicate-sd-scripts
|
training_comment | 训练介绍，可以写作者名或者使用触发关键词
|
| output_name |
string
|
CompVis/stable-diffusion-v1-4
|
output model name | 模型保存名称
|
| save_model_as |
string
(enum)
|
safetensors
Options: ckpt, pt, safetensors |
model save ext | 模型保存格式 ckpt, pt, safetensors
|
| resolution |
string
|
512
|
image resolution must be 'size' or 'width,height'. 图片分辨率，正方形边长 或 宽,高。支持非正方形，但必须是 64 倍数
|
| batch_size |
integer
|
1
Min: 1 |
batch size 一次性训练图片批处理数量，根据显卡质量对应调高
|
| max_train_epoches |
integer
|
10
Min: 1 |
max train epoches | 最大训练 epoch
|
| save_every_n_epochs |
integer
|
2
Min: 1 |
save every n epochs | 每 N 个 epoch 保存一次
|
| network_dim |
integer
|
32
Min: 1 |
network dim | 常用 4~128，不是越大越好
|
| network_alpha |
integer
|
32
Min: 1 |
network alpha | 常用与 network_dim 相同的值或者采用较小的值，如 network_dim的一半 防止下溢。默认值为 1，使用较小的 alpha 需要提升学习率
|
| clip_skip |
integer
|
2
|
clip skip | 玄学 一般用 2
|
| seed |
integer
|
1337
Min: 1 |
reproducable seed | 设置跑测试用的种子，输入一个prompt和这个种子大概率得到训练图。可以用来试触发关键词
|
| noise_offset |
number
|
0
|
noise offset | 在训练中添加噪声偏移来改良生成非常暗或者非常亮的图像，如果启用，推荐参数为 0.1
|
| keep_tokens |
integer
|
0
|
keep heading N tokens when shuffling caption tokens | 在随机打乱 tokens 时，保留前 N 个不变
|
| learning_rate |
number
|
0.00006
|
Learning rate | 学习率
|
| unet_lr |
number
|
0.00006
|
UNet learning rate | UNet 学习率
|
| text_encoder_lr |
number
|
0.000007
|
Text Encoder learning rate | Text Encoder 学习率
|
| lr_scheduler |
string
(enum)
|
cosine_with_restarts
Options: linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup |
"linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup" | PyTorch自带6种动态学习率函数
constant，常量不变, constant_with_warmup 线性增加后保持常量不变, linear 线性增加线性减少, polynomial 线性增加后平滑衰减, cosine 余弦波曲线, cosine_with_restarts 余弦波硬重启，瞬间最大值。
推荐默认cosine_with_restarts或者polynomial，配合输出多个epoch结果更玄学
|
| lr_warmup_steps |
integer
|
0
|
warmup steps | 仅在 lr_scheduler 为 constant_with_warmup 时需要填写这个值
|
| lr_scheduler_num_cycles |
integer
|
1
Min: 1 |
cosine_with_restarts restart cycles | 余弦退火重启次数，仅在 lr_scheduler 为 cosine_with_restarts 时起效
|
| min_bucket_reso |
integer
|
256
Min: 1 |
arb min resolution | arb 最小分辨率
|
| max_bucket_reso |
integer
|
1024
Min: 1 |
arb max resolution | arb 最大分辨率
|
| persistent_data_loader_workers |
boolean
|
True
|
makes workers persistent, further reduces/eliminates the lag in between epochs. however it may increase memory usage | 跑的更快，吃内存。大概能提速2.5倍，容易爆内存，保留加载训练集的worker，减少每个 epoch 之间的停顿
|
| optimizer_type |
string
(enum)
|
Lion
Options: adaFactor, AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation |
优化器，"adaFactor","AdamW","AdamW8bit","Lion","SGDNesterov","SGDNesterov8bit","DAdaptation", 推荐 新优化器Lion。推荐学习率unetlr=lr=6e-5,tenclr=7e-6
|
| network_module |
string
(enum)
|
networks.lora
Options: networks.lora |
Network module
|
Output schema
The shape of the response you’ll get when you run this model with an API.
{'format': 'uri', 'title': 'Output', 'type': 'string'}