M1dataset/sacristy

--- pretty_name: sacristy --- INSTALL REQUIREMENTS !wget -q https://github.com/ShivamShrirao/diffusers/raw/main/examples/dreambooth/train_dreambooth.py !wget -q https://github.com/ShivamShrirao/diffusers/raw/main/scripts/convert_diffusers_to_original_stable_diffusion.py %pip install -qq git+https://github.com/ShivamShrirao/diffusers %pip install -q -U --pre triton %pip install -q accelerate transformers ftfy bitsandbytes==0.35.0 gradio natsort safetensors WEIGHTS save_to_gdrive = False if save_to_gdrive: from google.colab import drive drive.mount('/content/drive') MODEL_NAME = "runwayml/stable-diffusion-v1-5" OUTPUT_DIR = "stable_diffusion_weights/zwx" if save_to_gdrive: OUTPUT_DIR = "/content/drive/MyDrive/" + OUTPUT_DIR else: OUTPUT_DIR = "/content/" + OUTPUT_DIR print(f"[*] Weights will be saved at {OUTPUT_DIR}") !mkdir -p $OUTPUT_DIR CONCEPT LIST concepts_list = [ { "instance_prompt": "photo of sacristy", "class_prompt": "photo of a room", "instance_data_dir": "/content/data/sacristy", "class_data_dir": "/content/data/room" }, { "instance_prompt": "photo of screens furniture", "class_prompt": "photo of a furniture", "instance_data_dir": "/content/data/screens", "class_data_dir": "/content/data/furniture" } ] import json import os for c in concepts_list: os.makedirs(c["instance_data_dir"], exist_ok=True) with open("concepts_list.json", "w") as f: json.dump(concepts_list, f, indent=4) UPLOADS import os from google.colab import files import shutil for c in concepts_list: print(f"Uploading instance images for `{c['instance_prompt']}`") uploaded = files.upload() for filename in uploaded.keys(): dst_path = os.path.join(c['instance_data_dir'], filename) shutil.move(filename, dst_path) TRAINING !accelerate launch train_dreambooth.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_name_or_path="stabilityai/sd-vae-ft-mse" \ --output_dir=$OUTPUT_DIR \ --revision="fp16" \ --with_prior_preservation --prior_loss_weight=1.0 \ --seed=1337 \ --resolution=512 \ --train_batch_size=1 \ --train_text_encoder \ --mixed_precision="fp16" \ --use_8bit_adam \ --gradient_accumulation_steps=1 \ --learning_rate=1e-6 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=50 \ --sample_batch_size=4 \ --max_train_steps=800 \ --save_interval=10000 \ --concepts_list="concepts_list.json"

原始信息汇总

数据集概述

数据集名称

• pretty_name: sacristy

数据集结构

• 概念列表: 包含两个概念，每个概念包含以下字段：
  • instance_prompt: 实例提示，如"photo of sacristy"和"photo of screens furniture"
  • class_prompt: 类别提示，如"photo of a room"和"photo of a furniture"
  • instance_data_dir: 实例数据目录，如"/content/data/sacristy"和"/content/data/screens"
  • class_data_dir: 类别数据目录，如"/content/data/room"和"/content/data/furniture"

数据集操作

• 数据目录创建: 使用os.makedirs确保每个实例数据目录存在。
• 数据上传: 通过Google Colab的files.upload功能上传实例图像，并移动到相应的实例数据目录。

训练配置

• 模型和VAE路径: 使用"runwayml/stable-diffusion-v1-5"作为模型，"stabilityai/sd-vae-ft-mse"作为VAE。
• 输出目录: 根据是否保存到Google Drive设置不同的输出目录。
• 训练参数: 包括但不限于：
  • 分辨率: 512
  • 训练批次大小: 1
  • 学习率: 1e-6
  • 最大训练步骤: 800
  • 保存间隔: 10000
  • 概念列表文件: "concepts_list.json"