LoRA学习中,会在训练图像中添加各种强度的噪声进行学习(详细内容略),但根据添加噪声的强度,学习会变得更接近或远离学习目标,使学习变得稳定. 引入最小 SNR gamma来补偿这一点。尤其是在学习没有大量噪声的图像时,系统可能会明显偏离目标,因此要尽量抑制这种跳跃。 我将省略细节,因为它们很复杂,但这个值可以设置在 0...
AdaLN的核心思想是根据输入的不同条件信息,自适应地调整LN的 \gamma 缩放参数和 \beta 偏移参数。AdaLN的核心步骤包括: 1. 提取条件信息:从输入的条件(如Text Embeddings、类别标签等)中提取信息,一般来说会专门使用一个神经网络模块(比如全连接层等)来处理输入条件,并生成与输入数据相对应的缩放和偏移参数。 在...
min_snr_gamma = 0 network_module = "networks.lora" network_dim = 32 network_alpha = 32 sample_prompts = "(masterpiece, best quality:1.2), 1girl, solo, --n lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality...
这里又可以选择是否使用一种加速训练的技术。如果使用,则 args.snr_gamma 推荐设置为 5.0。原 DDPM 的做法是直接算预测噪声和真实噪声的均方误差。 if args.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3....
[--dataset_config DATASET_CONFIG] [--min_snr_gamma MIN_SNR_GAMMA] [--weighted_captions] [--no_metadata] [--save_model_as {None,ckpt,pt,safetensors}][--unet_lr UNET_LR] [--text_encoder_lr TEXT_ENCODER_LR] [--network_weights NETWORK_WEIGHTS] [--network_module NETWORK_MODULE][--...
ss_min_snr_gamma:'None' ss_mixed_precision:'fp16' ss_network_alpha:'64.0' ss_network_dim:'128' ss_network_module:'networks.lora' ss_new_sd_model_hash:'e4a30e4607faeb06b5d590b2ed8e092690c631da0b2becb6224d4bb5327104b7' ss_noise_offset:'None' ...
"256"ss_min_snr_gamma:"None"ss_mixed_precision:"fp16"ss_network_alpha:"64.0"ss_network_dim:"128"ss_network_module:"networks.lora"ss_new_sd_model_hash:"e4a30e4607faeb06b5d590b2ed8e092690c631da0b2becb6224d4bb5327104b7"ss_noise_offset:"None"ss_num_batches_per_epoch:"675"ss_num_...
‘–cache_latents_to_disk’, ‘–optimizer_type=AdamW’, ‘–max_train_epochs=50’, ‘–max_data_loader_n_workers=0’, ‘–caption_dropout_rate=0.05’, ‘–bucket_reso_steps=64’, ‘–min_snr_gamma=5’, ‘–gradient_checkpointing’, ‘–xformers’, ‘–noise_offset=0.0′]’ died...
ss_min_snr_gamma:"None" ss_mixed_precision:"fp16" ss_network_alpha:"64.0" ss_network_dim:"128" ss_network_module:"networks.lora" ss_new_sd_model_hash:"e4a30e4607faeb06b5d590b2ed8e092690c631da0b2becb6224d4bb5327104b7" ss_noise_offset:"None" ...
Xu, X., Wang, R., Fu, C.W., Jia, J.: SNR-aware low-light image enhancement. In: CVPR, pp. 17714–17724 (2022) Google Scholar Xu, X., Wang, R., Lu, J.: Low-light image enhancement via structure modeling and guidance. In: CVPR, pp. 9893–9903 (2023) ...