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。
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。
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：
{
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"last_link_id": 18,
"nodes": [
{
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"type": "VAELoader",
"pos": [
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"size": {
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"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
12
],
"shape": 3
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],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"sdxl.vae.safetensors"
]
},
{
"id": 10,
"type": "VAEDecode",
"pos": [
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"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": [
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"size": {
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"1": 222
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"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,
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"fixed",
25,
5,
"EulerDiscreteScheduler"
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{
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"type": "DownloadAndLoadKolorsModel",
"pos": [
201,
368
],
"size": {
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"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"
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{
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"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 13
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],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 12,
"type": "KolorsTextEncode",
"pos": [
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],
"size": [
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"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
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},
{
"id": 15,
"type": "Note",
"pos": [
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"size": [
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149.55464588512064
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"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": [
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],
"size": [
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"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"
]
}
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"links": [
[
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"VAE"
],
[
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"IMAGE"
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[
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"CHATGLM3MODEL"
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[
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[
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"KOLORS_EMBEDS"
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[
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"LATENT"
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],
"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,
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],
"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
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"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,
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],
"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"
],
[
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"CHATGLM3MODEL"
],
[
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],
[
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"LATENT"
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[
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"KOLORSMODEL"
],
[
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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
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。 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。
总结