AIGC——微调技术(Datawhale X 魔搭 Al夏令营)

微调（Fine-tuning）是一种在深度学习和机器学习领域中常用的技术，旨在通过调整预训练模型的参数来使其更好地适应特定任务。其基本原理和参数的理解对于实现更好的效果至关重要。

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前言

了解微调的基本原理，对微调的各种参数有一个更加清楚的了解，来实现一个更好的效果，并且在这个Task中给大家介绍一下文生图的工作流平台工具ComfyUI，来实现一个更加高度定制的文生图。

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一、工具初探一ComfyUI应用场景探索

ComfyUI是一个基于节点流程的AI绘图工具WebUI，它专注于提供更加精准的工作流定制，通过将Stable Diffusion的流程拆分成节点，实现了工作流的定制和可复现性。

1、20分钟速通安装ComfyUI

选择使用魔搭社区提供的Notebook和免费的GPU算力体验来体验ComfyUI。

2、下载脚本代码文件

下载安装ComfyUI的执行文件和task1中微调完成Lora文件

git lfs install
git clone https://www.modelscope.cn/datasets/maochase/kolors_test_comfyui.git
mv kolors_test_comfyui/* ./
rm -rf kolors_test_comfyui/
mkdir -p /mnt/workspace/models/lightning_logs/version_0/checkpoints/
mv epoch=0-step=500.ckpt /mnt/workspace/models/lightning_logs/version_0/checkpoints/   

3、进入ComfyUI的安装文件

4、一键执行安装程序（大约10min）

5、进入预览界面

当执行到最后一个节点的内容输出了一个访问的链接的时候，复制链接到浏览器中访问

PS：如果链接访问白屏，或者报错，就等一会再访问重试，程序可能没有正常启动完毕

6、浅尝ComfyUI工作流

1.不带Lora的工作流样例

创建.json格式文件：

{
  "last_node_id": 15,
  "last_link_id": 18,
  "nodes": [
    {
      "id": 11,
      "type": "VAELoader",
      "pos": [
        1323,
        240
      ],
      "size": {
        "0": 315,
        "1": 58
      },
      "flags": {},
      "order": 0,
      "mode": 0,
      "outputs": [
        {
          "name": "VAE",
          "type": "VAE",
          "links": [
            12
          ],
          "shape": 3
        }
      ],
      "properties": {
        "Node name for S&R": "VAELoader"
      },
      "widgets_values": [
        "sdxl.vae.safetensors"
      ]
    },
    {
      "id": 10,
      "type": "VAEDecode",
      "pos": [
        1368,
        369
      ],
      "size": {
        "0": 210,
        "1": 46
      },
      "flags": {},
      "order": 6,
      "mode": 0,
      "inputs": [
        {
          "name": "samples",
          "type": "LATENT",
          "link": 18
        },
        {
          "name": "vae",
          "type": "VAE",
          "link": 12,
          "slot_index": 1
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            13
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "VAEDecode"
      }
    },
    {
      "id": 14,
      "type": "KolorsSampler",
      "pos": [
        1011,
        371
      ],
      "size": {
        "0": 315,
        "1": 222
      },
      "flags": {},
      "order": 5,
      "mode": 0,
      "inputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "link": 16
        },
        {
          "name": "kolors_embeds",
          "type": "KOLORS_EMBEDS",
          "link": 17
        }
      ],
      "outputs": [
        {
          "name": "latent",
          "type": "LATENT",
          "links": [
            18
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "KolorsSampler"
      },
      "widgets_values": [
        1024,
        1024,
        1000102404233412,
        "fixed",
        25,
        5,
        "EulerDiscreteScheduler"
      ]
    },
    {
      "id": 6,
      "type": "DownloadAndLoadKolorsModel",
      "pos": [
        201,
        368
      ],
      "size": {
        "0": 315,
        "1": 82
      },
      "flags": {},
      "order": 1,
      "mode": 0,
      "outputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "links": [
            16
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "DownloadAndLoadKolorsModel"
      },
      "widgets_values": [
        "Kwai-Kolors/Kolors",
        "fp16"
      ]
    },
    {
      "id": 3,
      "type": "PreviewImage",
      "pos": [
        1366,
        468
      ],
      "size": [
        535.4001724243165,
        562.2001106262207
      ],
      "flags": {},
      "order": 7,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 13
        }
      ],
      "properties": {
        "Node name for S&R": "PreviewImage"
      }
    },
    {
      "id": 12,
      "type": "KolorsTextEncode",
      "pos": [
        519,
        529
      ],
      "size": [
        457.2893696934723,
        225.28656056301645
      ],
      "flags": {},
      "order": 4,
      "mode": 0,
      "inputs": [
        {
          "name": "chatglm3_model",
          "type": "CHATGLM3MODEL",
          "link": 14,
          "slot_index": 0
        }
      ],
      "outputs": [
        {
          "name": "kolors_embeds",
          "type": "KOLORS_EMBEDS",
          "links": [
            17
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "KolorsTextEncode"
      },
      "widgets_values": [
        "cinematic photograph of an astronaut riding a horse in space |\nillustration of a cat wearing a top hat and a scarf  |\nphotograph of a goldfish in a bowl |\nanime screencap of a red haired girl",
        "",
        1
      ]
    },
    {
      "id": 15,
      "type": "Note",
      "pos": [
        200,
        636
      ],
      "size": [
        273.5273818969726,
        149.55464588512064
      ],
      "flags": {},
      "order": 2,
      "mode": 0,
      "properties": {
        "text": ""
      },
      "widgets_values": [
        "Text encoding takes the most VRAM, quantization can reduce that a lot.\n\nApproximate values I have observed:\nfp16 - 12 GB\nquant8 - 8-9 GB\nquant4 - 4-5 GB\n\nquant4 reduces the quality quite a bit, 8 seems fine"
      ],
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 13,
      "type": "DownloadAndLoadChatGLM3",
      "pos": [
        206,
        522
      ],
      "size": [
        274.5334274291992,
        58
      ],
      "flags": {},
      "order": 3,
      "mode": 0,
      "outputs": [
        {
          "name": "chatglm3_model",
          "type": "CHATGLM3MODEL",
          "links": [
            14
          ],
          "shape": 3
        }
      ],
      "properties": {
        "Node name for S&R": "DownloadAndLoadChatGLM3"
      },
      "widgets_values": [
        "fp16"
      ]
    }
  ],
  "links": [
    [
      12,
      11,
      0,
      10,
      1,
      "VAE"
    ],
    [
      13,
      10,
      0,
      3,
      0,
      "IMAGE"
    ],
    [
      14,
      13,
      0,
      12,
      0,
      "CHATGLM3MODEL"
    ],
    [
      16,
      6,
      0,
      14,
      0,
      "KOLORSMODEL"
    ],
    [
      17,
      12,
      0,
      14,
      1,
      "KOLORS_EMBEDS"
    ],
    [
      18,
      14,
      0,
      10,
      0,
      "LATENT"
    ]
  ],
  "groups": [],
  "config": {},
  "extra": {
    "ds": {
      "scale": 1.1,
      "offset": {
        "0": -114.73954010009766,
        "1": -139.79705810546875
      }
    }
  },
  "version": 0.4
}

加载模型，并完成第一次生图

PS：首次点击生成图片会加载资源，时间较长，大家耐心等待

2.带Lora的工作流样例

创建.json格式文件：

      
{
  "last_node_id": 16,
  "last_link_id": 20,
  "nodes": [
    {
      "id": 11,
      "type": "VAELoader",
      "pos": [
        1323,
        240
      ],
      "size": {
        "0": 315,
        "1": 58
      },
      "flags": {},
      "order": 0,
      "mode": 0,
      "outputs": [
        {
          "name": "VAE",
          "type": "VAE",
          "links": [
            12
          ],
          "shape": 3
        }
      ],
      "properties": {
        "Node name for S&R": "VAELoader"
      },
      "widgets_values": [
        "sdxl.vae.safetensors"
      ]
    },
    {
      "id": 10,
      "type": "VAEDecode",
      "pos": [
        1368,
        369
      ],
      "size": {
        "0": 210,
        "1": 46
      },
      "flags": {},
      "order": 7,
      "mode": 0,
      "inputs": [
        {
          "name": "samples",
          "type": "LATENT",
          "link": 18
        },
        {
          "name": "vae",
          "type": "VAE",
          "link": 12,
          "slot_index": 1
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            13
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "VAEDecode"
      }
    },
    {
      "id": 15,
      "type": "Note",
      "pos": [
        200,
        636
      ],
      "size": {
        "0": 273.5273742675781,
        "1": 149.5546417236328
      },
      "flags": {},
      "order": 1,
      "mode": 0,
      "properties": {
        "text": ""
      },
      "widgets_values": [
        "Text encoding takes the most VRAM, quantization can reduce that a lot.\n\nApproximate values I have observed:\nfp16 - 12 GB\nquant8 - 8-9 GB\nquant4 - 4-5 GB\n\nquant4 reduces the quality quite a bit, 8 seems fine"
      ],
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 13,
      "type": "DownloadAndLoadChatGLM3",
      "pos": [
        206,
        522
      ],
      "size": {
        "0": 274.5334167480469,
        "1": 58
      },
      "flags": {},
      "order": 2,
      "mode": 0,
      "outputs": [
        {
          "name": "chatglm3_model",
          "type": "CHATGLM3MODEL",
          "links": [
            14
          ],
          "shape": 3
        }
      ],
      "properties": {
        "Node name for S&R": "DownloadAndLoadChatGLM3"
      },
      "widgets_values": [
        "fp16"
      ]
    },
    {
      "id": 6,
      "type": "DownloadAndLoadKolorsModel",
      "pos": [
        201,
        368
      ],
      "size": {
        "0": 315,
        "1": 82
      },
      "flags": {},
      "order": 3,
      "mode": 0,
      "outputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "links": [
            19
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "DownloadAndLoadKolorsModel"
      },
      "widgets_values": [
        "Kwai-Kolors/Kolors",
        "fp16"
      ]
    },
    {
      "id": 12,
      "type": "KolorsTextEncode",
      "pos": [
        519,
        529
      ],
      "size": {
        "0": 457.28936767578125,
        "1": 225.28656005859375
      },
      "flags": {},
      "order": 4,
      "mode": 0,
      "inputs": [
        {
          "name": "chatglm3_model",
          "type": "CHATGLM3MODEL",
          "link": 14,
          "slot_index": 0
        }
      ],
      "outputs": [
        {
          "name": "kolors_embeds",
          "type": "KOLORS_EMBEDS",
          "links": [
            17
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "KolorsTextEncode"
      },
      "widgets_values": [
        "二次元，长发，少女，白色背景",
        "",
        1
      ]
    },
    {
      "id": 3,
      "type": "PreviewImage",
      "pos": [
        1366,
        469
      ],
      "size": {
        "0": 535.400146484375,
        "1": 562.2001342773438
      },
      "flags": {},
      "order": 8,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 13
        }
      ],
      "properties": {
        "Node name for S&R": "PreviewImage"
      }
    },
    {
      "id": 16,
      "type": "LoadKolorsLoRA",
      "pos": [
        606,
        368
      ],
      "size": {
        "0": 317.4000244140625,
        "1": 82
      },
      "flags": {},
      "order": 5,
      "mode": 0,
      "inputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "link": 19
        }
      ],
      "outputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "links": [
            20
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "LoadKolorsLoRA"
      },
      "widgets_values": [
        "/mnt/workspace/models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt",
        2
      ]
    },
    {
      "id": 14,
      "type": "KolorsSampler",
      "pos": [
        1011,
        371
      ],
      "size": {
        "0": 315,
        "1": 266
      },
      "flags": {},
      "order": 6,
      "mode": 0,
      "inputs": [
        {
          "name": "kolors_model",
          "type": "KOLORSMODEL",
          "link": 20
        },
        {
          "name": "kolors_embeds",
          "type": "KOLORS_EMBEDS",
          "link": 17
        },
        {
          "name": "latent",
          "type": "LATENT",
          "link": null
        }
      ],
      "outputs": [
        {
          "name": "latent",
          "type": "LATENT",
          "links": [
            18
          ],
          "shape": 3,
          "slot_index": 0
        }
      ],
      "properties": {
        "Node name for S&R": "KolorsSampler"
      },
      "widgets_values": [
        1024,
        1024,
        0,
        "fixed",
        25,
        5,
        "EulerDiscreteScheduler",
        1
      ]
    }
  ],
  "links": [
    [
      12,
      11,
      0,
      10,
      1,
      "VAE"
    ],
    [
      13,
      10,
      0,
      3,
      0,
      "IMAGE"
    ],
    [
      14,
      13,
      0,
      12,
      0,
      "CHATGLM3MODEL"
    ],
    [
      17,
      12,
      0,
      14,
      1,
      "KOLORS_EMBEDS"
    ],
    [
      18,
      14,
      0,
      10,
      0,
      "LATENT"
    ],
    [
      19,
      6,
      0,
      16,
      0,
      "KOLORSMODEL"
    ],
    [
      20,
      16,
      0,
      14,
      0,
      "KOLORSMODEL"
    ]
  ],
  "groups": [],
  "config": {},
  "extra": {
    "ds": {
      "scale": 1.2100000000000002,
      "offset": {
        "0": -183.91309381910426,
        "1": -202.11110769225016
      }
    }
  },
  "version": 0.4
}

    

7、关闭魔塔GPU环境

二、Lora微调

Lora微调，全称为Low-Rank Adaptation（低秩适应），是一种高效的模型微调技术，特别适用于大型预训练模型。该技术通过引入低秩矩阵来保持预训练模型的大部分参数不变，仅调整少量参数以适应特定任务。

1.Lora微调的基本原理

1. 参数矩阵的低秩近似：
   • 大模型通常具有过参数化的特点，即参数矩阵的维度很高，但在特定任务中，只有一小部分参数起主要作用。
   • Lora利用低秩矩阵分解的思想，通过引入两个维度较小的矩阵A和B（A的维度为dxr，B的维度为rxd，其中r远小于d）来近似原始权重矩阵。
   • 这两个矩阵相乘后，得到的矩阵AB的秩远小于原始权重矩阵的秩，但能够在一定程度上保持模型在特定任务上的性能。
2. 旁路结构：
   • 在网络中增加一个旁路结构，该旁路是A和B两个矩阵相乘的结果。
   • 在训练过程中，冻结原始网络的参数，只训练旁路参数A和B。
   • 由于A和B的参数量远远小于原始网络的参数，因此训练时所需的显存开销大大减小。

2.Task2中的的微调代码

代码如下：

import os
cmd = """
python DiffSynth-Studio/examples/train/kolors/train_kolors_lora.py \ # 选择使用可图的Lora训练脚本DiffSynth-Studio/examples/train/kolors/train_kolors_lora.py
  --pretrained_unet_path models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors \ # 选择unet模型
  --pretrained_text_encoder_path models/kolors/Kolors/text_encoder \ # 选择text_encoder
  --pretrained_fp16_vae_path models/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors \ # 选择vae模型
  --lora_rank 16 \ # lora_rank 16 表示在权衡模型表达能力和训练效率时，选择了使用 16 作为秩，适合在不显著降低模型性能的前提下，通过 LoRA 减少计算和内存的需求
  --lora_alpha 4.0 \ # 设置 LoRA 的 alpha 值，影响调整的强度
  --dataset_path data/lora_dataset_processed \ # 指定数据集路径，用于训练模型
  --output_path ./models \ # 指定输出路径，用于保存模型
  --max_epochs 1 \ # 设置最大训练轮数为 1
  --center_crop \ # 启用中心裁剪，这通常用于图像预处理
  --use_gradient_checkpointing \ # 启用梯度检查点技术，以节省内存
  --precision "16-mixed" # 指定训练时的精度为混合 16 位精度（half precision），这可以加速训练并减少显存使用
""".strip()
os.system(cmd) # 执行可图Lora训练    

---

总结

AIGC作为人工智能领域的一个重要分支，正在逐步改变我们的生活方式和工作方式。随着技术的不断发展和完善，我们有理由相信AIGC将在更多领域展现出其独特的魅力和价值。