Stable Diffusion LoRA微调API

请求方法

POST

请求地址

https://api.lanrui.co/api/sd/train/lora

CURL 示例

curl --location 'https://api.lanrui.co/api/sd/train/lora' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer {填入API Key，删除花括号}' \
--data '{
    "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5",
    "": 
    "LoRA_type": "LyCORIS/iA3",
    "adaptive_noise_scale": 0.005,
    "caption_dropout_rate": 0.5,
    "epoch": 300,
    "gradient_accumulation_steps": 1,
    "keep_tokens": 1,
    "learning_rate": 1.0,
    "lr_scheduler": "constant",
    "lr_warmup": 0,
    "min_snr_gamma": 5,
    "network_alpha": 1024,
    "network_dim": 1024,
    "network_dropout": 0.3,
    "noise_offset": 0.05,
    "noise_offset_type": "Original",
    "optimizer": "Prodigy",
    "optimizer_args": "d_coef=1.0 weight_decay=0.01 safeguard_warmup=False use_bias_correction=False",
    "save_every_n_epochs": 1,
    "scale_weight_norms": 1,
    "seed": "0",
    "shuffle_caption": true,
    "text_encoder_lr": 1.0,
    "train_batch_size": 1,
    "unet_lr": 1.0
}'

请求 Body 参数（application/json）

参数名	参数类型	取值范围	参数释义	是否必填
pretrained_model_name_or_path	string	默认值: `runwayml/stable-diffusion-v1-5` 取值范围: `stabilityai/stable-diffusion-xl-base-1.0` `runwayml/stable-diffusion-v1-5`	Pretrained model name	否
sdxl	boolean	默认值: `False`	sdxl	否
repeats	integer	默认值: `10`	The number of times an image is reused within an epoch	否
train_batch_size	integer	默认值: `1` 取值范围: `[1, inf)`	Batch size for training	否
epoch	integer	默认值: `20` 取值范围: `[1, inf)`	Epoch size for training	否
mixed_precision	string	默认值: `fp16` 取值范围: `no` `fp16` `bf16` `fp8`	Whether or not to use mixed precision training	否
train_data_dir	string	默认值: `空`	Image folder (containing training images subfolders)	否
max_resolution	string	默认值: `512,512`	Max resolution (width, height)	否
color_aug	boolean	默认值: `False`	Enable weak color augmentation	否
flip_aug	boolean	默认值: `False`	Enable horizontal flip augmentation	否
output_dir	string	默认值: `空`	Output directory for trained model	否
save_every_n_epochs	integer	默认值: `10` 取值范围: `[1, inf)`	Save every N epochs	否
save_precision	string	默认值: `fp16` 取值范围: `float` `fp16` `bf16`	Save precision	否
learning_rate	number	默认值: `0.0001`	Learning rate (Set to 0 to not train the Unet)	否
text_encoder_lr	number	默认值: `0.0001` 取值范围: `[0.0, inf)`	Text Encoder learning rate (Optional)	否
unet_lr	number	默认值: `0.0001` 取值范围: `[0.0, inf)`	Unet learning rate (Optional)	否
lr_scheduler	string	默认值: `constant` 取值范围: `constant` `adafactor` `constant_with_warmup` `cosine` `cosine_with_restarts` `linear` `polynomial`	LR Scheduler	否
lr_warmup	number	默认值: `0` 取值范围: `[0.0, inf)`	LR warmup (% of total steps) Can't use LR warmup with LR Scheduler constant... setting to 0 and disabling field...	否
lr_scheduler_num_cycles	integer	默认值: `1` 取值范围: `[1, inf)`	Number of restarts for cosine scheduler with restarts	否
lr_scheduler_power	number	默认值: `1.0` 取值范围: `[0.0, inf)`	Polynomial power for polynomial scheduler	否
optimizer	string	默认值: `PagedAdamW8bit` 取值范围: `AdamW` `AdamW8bit` `Adafactor` `DAdaptation` `DAdaptAdaGrad` `DAdaptAdam` `DAdaptAdan` `DAdaptAdanIP` `DAdaptAdamPreprint` `DAdaptLion` `DAdaptSGD` `Lion` `Lion8bit` `PagedAdamW8bit` `PagedAdamW32bit` `PagedLion8bit` `Prodigy` `SGDNesterov` `SGDNesterov8bit`	Optimizer	否
max_grad_norm	number	默认值: `1.0` 取值范围: `[0.0, 1.0]`	Max grad norm	否
min_snr_gamma	integer	默认值: `0` 取值范围: `[0, 20]`	Recommended value of 5 when used	否
shuffle_caption	boolean	默认值: `False`	Shuffle caption	否
keep_tokens	integer	默认值: `0` 取值范围: `[0, 32]`	Keep n tokens	否
max_token_length	integer	默认值: `75` 取值范围: `75` `150` `225`	max token length of text encoder	否
noise_offset_type	string	默认值: `Original` 取值范围: `Original` `Multires`	Noise offset type	否
noise_offset	number	默认值: `0` 取值范围: `[0.0, 1.0]`	Recommended values are 0.05 - 0.15	否
noise_offset_random_strength	boolean	默认值: `False`	Use random strength between 0~noise_offset for noise offset	否
adaptive_noise_scale	number	默认值: `0` 取值范围: `[-1.0, 1.0]`	Add `latent mean absolute value * this value` to noise_offset	否
multires_noise_iterations	integer	默认值: `0` 取值范围: `[0, 64]`	Enable multires noise (recommended values are 6-10)	否
multires_noise_discount	number	默认值: `0.3` 取值范围: `[0.0, 1.0]`	Recommended values are 0.8. For LoRAs with small datasets, 0.1-0.3	否
sample_every_n_epochs	integer	默认值: `0`	Sample every n epochs	否
sample_sampler	string	默认值: `euler_a` 取值范围: `ddim` `pndm` `lms` `euler` `euler_a` `heun` `dpm_2` `dpm_2_a` `dpmsolver` `dpmsolver++` `dpmsingle` `k_lms` `k_euler` `k_euler_a` `k_dpm_2` `k_dpm_2_a`	Sample sampler	否
sample_prompts	string	默认值: `空`	Enter one sample prompt per line to generate multiple samples per cycle. Optional specifiers include: --w (width), --h (height), --d (seed), --l (cfg scale), --s (sampler steps) and --n (negative prompt). eg: masterpiece, best quality, 1girl, in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28	否
seed	integer	默认值: `0` 取值范围: `[0, inf)`	Seed (Set to 0 to make random)	否
clip_skip	integer	默认值: `1` 取值范围: `[0, 12]`	Clip skip	否
stop_text_encoder_training_pct	integer	默认值: `0` 取值范围: `[0, 100]`	Stop TE (% of total steps)	否
gradient_accumulation_steps	integer	默认值: `1` 取值范围: `[1, 120]`	Number of updates steps to accumulate before performing a backward/update pass	否
optimizer_args	string	默认值: `空`	(Optional) eg: relative_step=True scale_parameter=True warmup_init=True	否
lr_scheduler_args	string	默认值: `空`	(Optional) eg: milestones=[1,10,30,50] gamma=0.1	否
enable_bucket	boolean	默认值: `True`	Enable buckets	否
random_crop	boolean	默认值: `False`	Random crop instead of center crop	否
min_bucket_reso	integer	默认值: `64` 取值范围: `[64, 4096]`	Minimum size in pixel a bucket can be (>= 64)	否
max_bucket_reso	integer	默认值: `2048` 取值范围: `[64, 4096]`	Maximum size in pixel a bucket can be (>= 64)	否
bucket_no_upscale	boolean	默认值: `True`	Don't upscale bucket resolution	否
bucket_reso_steps	integer	默认值: `64` 取值范围: `[1, 128]`	Bucket resolution steps	否
caption_dropout_every_n_epochs	integer	默认值: `0`	Dropout caption every n epochs	否
caption_dropout_rate	number	默认值: `0` 取值范围: `[0.0, 1.0]`	Rate of caption dropout	否
weighted_captions	boolean	默认值: `False`	Enable weighted captions in the standard style (token:1.3). No commas inside parens, or shuffle/dropout may break the decoder.	否
ip_noise_gamma	number	默认值: `0` 取值范围: `[0.0, 1.0]`	Enable input perturbation noise. used for regularization. recommended value: around 0.1	否
ip_noise_gamma_random_strength	boolean	默认值: `False`	Use random strength between 0~ip_noise_gamma for input perturbation noise	否
min_timestep	integer	默认值: `0` 取值范围: `[0, 1000]`	Values greater than 0 will make the model more img2img focussed. 0 = image only	否
max_timestep	integer	默认值: `1000` 取值范围: `[0, 1000]`	Values lower than 1000 will make the model more img2img focussed. 1000 = noise only	否
LoRA_type	string	默认值: `Standard` 取值范围: `Kohya DyLoRA` `Kohya LoCon` `LoRA-FA` `LyCORIS/iA3` `LyCORIS/BOFT` `LyCORIS/Diag-OFT` `LyCORIS/DyLoRA` `LyCORIS/GLoRA` `LyCORIS/LoCon` `LyCORIS/LoHa` `LyCORIS/LoKr` `LyCORIS/Native Fine-Tuning` `Standard`	LoRA type	否
LyCORIS_preset	string	默认值: `full` 取值范围: `attn-mlp` `attn-only` `full` `full-lin` `unet-transformer-only` `unet-convblock-only`	LyCORIS Preset	否
network_dim	integer	默认值: `8` 取值范围: `[1, 512]`	Network Rank (Dimension)	否
network_alpha	number	默认值: `1.0` 取值范围: `[1e-05, 1024.0]`	Alpha for LoRA weight scaling	否
conv_dim	integer	默认值: `1` 取值范围: `[0, 512]`	Convolution Rank (Dimension)	否
conv_alpha	integer	默认值: `1` 取值范围: `[0, 512]`	Convolution Alpha	否
factor	integer	默认值: `-1` 取值范围: `[-1, 64]`	LoKr factor	否
bypass_mode	boolean	默认值: `False`	Designed for bnb 8bit/4bit linear layer. (QLyCORIS)	否
dora_wd	boolean	默认值: `False`	Enable the DoRA method for these algorithms	否
use_cp	boolean	默认值: `False`	A two-step approach utilizing tensor decomposition and fine-tuning to accelerate convolution layers in large neural networks, resulting in significant CPU speedups with minor accuracy drops.	否
use_tucker	boolean	默认值: `False`	Efficiently decompose tensor shapes, resulting in a sequence of convolution layers with varying dimensions and Hadamard product implementation through multiplication of two distinct tensors.	否
use_scalar	boolean	默认值: `False`	Train an additional scalar in front of the weight difference, use a different weight initialization strategy.	否
rank_dropout_scale	boolean	默认值: `False`	Adjusts the scale of the rank dropout to maintain the average dropout rate, ensuring more consistent regularization across different layers.	否
constrain	number	默认值: `0.0`	Limits the norm of the oft_blocks, ensuring that their magnitude does not exceed a specified threshold, thus controlling the extent of the transformation applied.	否
rescaled	boolean	默认值: `False`	Applies an additional scaling factor to the oft_blocks, allowing for further adjustment of their impact on the model's transformations.	否
train_norm	boolean	默认值: `False`	Selects trainable layers in a network, but trains normalization layers identically across methods as they lack matrix decomposition.	否
decompose_both	boolean	默认值: `False`	Controls whether both input and output dimensions of the layer's weights are decomposed into smaller matrices for reparameterization.	否
train_on_input	boolean	默认值: `True`	Set if we change the information going into the system (True) or the information coming out of it (False).	否
down_lr_weight	string	默认值: `空`	Specify the learning rate weight of the down blocks of U-Net. (Optional) eg: 0,0,0,0,0,0,1,1,1,1,1,1	否
mid_lr_weight	string	默认值: `空`	Specify the learning rate weight of the mid block of U-Net. (Optional) eg: 0.5	否
up_lr_weight	string	默认值: `空`	Specify the learning rate weight of the up blocks of U-Net. The same as down_lr_weight. (Optional) eg: 0,0,0,0,0,0,1,1,1,1,1,1	否
block_lr_zero_threshold	string	默认值: `空`	If the weight is not more than this value, the LoRA module is not created. The default is 0. (Optional) eg: 0.1	否
block_dims	string	默认值: `空`	Specify the dim (rank) of each block. Specify 25 numbers. (Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2	否
block_alphas	string	默认值: `空`	Specify the alpha of each block. Specify 25 numbers as with block_dims. If omitted, the value of network_alpha is used. (Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2	否
conv_block_dims	string	默认值: `空`	Extend LoRA to Conv2d 3x3 and specify the dim (rank) of each block. Specify 25 numbers. (Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2	否
conv_block_alphas	string	默认值: `空`	Specify the alpha of each block when expanding LoRA to Conv2d 3x3. Specify 25 numbers. If omitted, the value of conv_alpha is used. (Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2	否
scale_weight_norms	number	默认值: `0` 取值范围: `[0.0, 10.0]`	Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR #545 on kohya_ss/sd_scripts repo for details. Recommended setting: 1. Higher is weaker, lower is stronger.	否
network_dropout	number	默认值: `0.0` 取值范围: `[0.0, 1.0]`	Is a normal probability dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Recommended range 0.1 to 0.5	否
rank_dropout	number	默认值: `0.0` 取值范围: `[0.0, 1.0]`	Can specify `rank_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3	否
module_dropout	number	默认值: `0.0` 取值范围: `[0.0, 1.0]`	Can specify `module_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3	否
unit	integer	默认值: `1` 取值范围: `[1, 64]`	DyLoRA Unit / Block size	否

下表展示了与不同的LoRA_type相关的具体参数：

参数名/ LoRA_type	Kohya DyLoRA	Kohya LoCon	LoRA-FA	LyCORIS/iA3	LyCORIS/BOFT	LyCORIS/Diag-OFT	LyCORIS/DyLoRA	LyCORIS/GLoRA	LyCORIS/LoCon	LyCORIS/LoHa	LyCORIS/LoKr	LyCORIS/Native Fine-Tuning	Standard
LyCORIS_preset	-	-	-	✓	✓	✓	✓	✓	✓	✓	✓	✓	-
network_alpha	✓	✓	✓	-	✓	✓	✓	✓	✓	✓	✓	-	✓
conv_dim	-	-	-	-	-	-	-	-	-	-	-	-	-
conv_alpha	-	-	-	-	-	-	-	-	-	-	-	-	-
factor	-	-	-	-	-	-	-	-	-	-	✓	-	-
bypass_mode	-	-	-	-	-	-	-	-	✓	✓	✓	-	-
dora_wd	-	-	-	-	-	-	-	-	✓	✓	✓	-	-
use_cp	-	-	-	-	-	-	-	-	-	-	-	-	-
use_tucker	-	-	-	-	✓	✓	✓	-	✓	✓	-	✓	-
use_scalar	-	-	-	-	✓	✓	-	-	✓	✓	✓	✓	-
rank_dropout_scale	-	-	-	-	✓	✓	-	✓	✓	✓	✓	✓	-
constrain	-	-	-	-	✓	✓	-	-	-	-	-	-	-
rescaled	-	-	-	-	✓	✓	-	-	-	-	-	-	-
train_norm	-	-	-	-	✓	✓	✓	✓	✓	✓	✓	✓	-
decompose_both	-	-	-	-	-	-	-	-	-	-	✓	-	-
train_on_input	-	-	-	✓	-	-	-	-	-	-	-	-	-
down_lr_weight	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
mid_lr_weight	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
up_lr_weight	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
block_lr_zero_threshold	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
block_dims	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
block_alphas	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
conv_block_dims	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
conv_block_alphas	✓	✓	✓	-	-	-	-	-	-	-	-	-	✓
scale_weight_norms	✓	✓	✓	-	-	-	✓	✓	✓	✓	✓	-	✓
network_dropout	✓	✓	✓	-	✓	✓	✓	✓	✓	✓	✓	✓	✓
rank_dropout	✓	✓	✓	-	✓	✓	-	✓	✓	✓	-	✓	✓
module_dropout	✓	✓	✓	-	✓	✓	-	✓	✓	✓	-	✓	✓
unit	✓	-	-	-	-	-	✓	-	-	-	-	-	-

请求方法​

请求地址​

请求 Body 参数（application/json）​

返回响应（json）​

请求方法

请求地址

请求 Body 参数（application/json）

返回响应（json）