manual_seed(42) # SDXL Base Pipeline进行推理 image = pipe(prompt, negative_prompt=negative_prompt,generator=seed).images[0] # Pipeline生成的images包含在一个list中:[<PIL.Image.Image image mode=RGB size=1024x1024>] #所以需要使用
SDXL Lora 参考https://github.com/bmaltais/kohya_ss#tips-for-sdxl-training的训练配置,仅训练 UNET 对应 LORA 的话参考以下 kohya 的训练代码: exportTF_ENABLE_ONEDNN_OPTS=0accelerate launch --num_cpu_threads_per_process=2"/workspace/kohya_ss/sdxl_train_network.py"\--enable_bucket\--pretrai...
LORA Image Captioning How to replace a word in multiple text files on Windows The right SDXL STYLE LORA Training Parameters LORA Type Standard vs LoCON or LoHA LyCORIS Train Batch size Number of Epoch Training BF16 & FP16 & Cache The RIGHT Learning Rate for SDXL STYLE LORA ...
最终训练步数的计算公式也比较简单,Final training step = Min(photo_num * max_steps_per_photos, max_train_steps)。 简单来理解就是: 图片数量少的时候,训练步数为photo_num * max_steps_per_photos。 图片数量多的时候,训练步数为max_train_steps。 b、开启或者关闭验证 EasyPhoto在训练时默认会对训练过程...
不建议变 args.training_prompt = "girl" args.lr_warmup_steps = 0 args.train_batch_size = 1 args.num_train_epochs = 1 # 训练轮数 args.checkpointing_epoches = 1 # 保存轮数 args.learning_rate = 0.00005 # 学习率 args.lr_scheduler = "cosine" # 从这里选择学习率调度策略 ["linear", ...
The other benefit of doing cropping and downscaling on your own is that you can use a high quality downsampling algorithm that produces sharp results for best possible training. Photoshop has good image size reduction when using bicubic (sharper), but in most other programs I often have to app...
What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all # 输入all即可 Do you wish to use FP16 or BF16 (mixed precision)? # 训练精度,可以选择fp16 fp16 # 完成配置后,配置文件default_config.yaml会保存在/root/.cache/huggingface/accelerate下...
说明文字(image_caption):描述图片的文字 按照以上字段和格式准备 Controlnet 微调的训练数据集即可,这里给出一条整理后的训练数据示例: 准备好的训练数据集可以托管在各种存储设施上,比如 Local 本地磁盘 / AWS S3 / AWS EFS…etc,当然也可以 upload 到 Huggingface Datasets。
train_batch_size:训练数据加载器的批处理大小(每个设备)。减少批处理大小,防止训练过程中出现内存不足错误。 num_train_steps:训练步数。建议设置为N × 100,其中N表示训练图像的个数。 checkpointing_steps:每X次更新时保存训练状态的检查点。默认为500。将其设置为更高的值以减少保存的检查点数量,因为模型需要...
export FLAGS_conv_workspace_size_limit=4096 !python -u train_text_to_image_lora_sdxl.py \ --pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \ --pretrained_vae_model_name_or_path="madebyollin/sdxl-vae-fp16-fix" \ --train_data_dir="/home/aistudio/dataset" ...