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ComfyUI中基于Fluxgym训练Flux的Lora模型

article 2025/2/21 20:56:48

1、介绍

Fluxgym训练非常方便，只需要更改一个配置文件内容即可。训练时也不需要提前进行图片裁剪、打标等前置工作。

本文章是介绍在16G以下显存下训练Flux模型的方法。

2、部署项目

（1）下载Fluxgym 和 kohya-ss/sd-scripts

git clone https://github.com/cocktailpeanut/fluxgym
cd fluxgym
git clone -b sd3 https://github.com/kohya-ss/sd-scripts

完成之后的文件夹结构应该如下所示：

/fluxgym
  app.py
  requirements.txt
  sd-scripts/

（2）创建虚拟环境fluxgym

使用conda创建，

conda create -n fluxgym python=3.10
conda activate fluxgym

（3）安装python依赖项

进入sd-scripts文件夹，安装依赖项

cd sd-scripts
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

完成之后返回根文件夹(fluxgym)，修改requirements.txt文件，将huggingface.co改为hf-mirror.com：

然后安装依赖项：

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

（4）安装pytorch Nightly

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121

3、启动项目

指定server_name 和 server_port，启动服务。

cd fluxgym
export GRADIO_SERVER_NAME=0.0.0.0
export GRADIO_SERVER_PORT=16080
python app.py

启动成功的日志如下：

打开页面后显示如下：

4、训练模型

（1）模型选择

fluxgym默认支持flux-dev / flux-schnell / bdsqlsz/flux1-dev2pro-single 三种flux模型。

该信息在models.yaml文件里。

不过文件中指定的model全都是大于20G的，在16G显存中无法训练。

我们可以使用flux1-dev-fp8.safetensors模型，链接如下：

F.1-dev/fp8/NF4/GGUF-所需模型文件包-Other-墨幽-LiblibAI

（2）下载模型

该文件比较大，假如公司网络限速的话，可以直接获取下载地址，在linux服务器上下载。

Chrome浏览器上获取下载源地址的方法：

首先点击那14G文件进行下载

然后在浏览器上输入chrome://net-export/，单击开始记录日志，隔个5秒钟左右关闭记录，查看日志，找到liblibai-online.liblib.cloud的链接地址。

最后在linux服务器上直接通过wget命令进行下载。

注意这个是zip包，不是模型文件！！！下载之后需要通过unzip命令解压缩后才能使用。

下载中的日志：

然后在linux上通过wget命令下载该文件。

通过tail -f wget-log.4可以下载进度：

下载完毕后，通过mv命令修改为zip后缀的文件。

然后通过unzip命令解压文件。

然后把里面的文件放到fluxgym对应的目录之下：

mv flux1-dev-fp8.safetensors xx/fluxgym/models/unet

mv clip_l.safetensors xx/fluxgym/models/clip

mv t5xxl_fp8_e4m3fn.safetensors xx/fluxgym/models/clip

mv ae.sft xx/fluxgym/models/vae

修改app.py文件：

将文件中所有的t5xxl_fp16.safetensors替换为t5xxl_fp8_e4m3fn.safetensors。

（3）修改models.yaml文件

在末尾添加如下内容：

flux1-dev-fp8:
repo: .
base: .
license: other
license_name: flux1-dev-fp8-license
file: flux1-dev-fp8.safetensors

然后重新启动项目。

（4）启动训练

1）基本设置

The name of your LoRA：设置Lora的名称
Trigger word/sentence：lora的触发词，结尾处需要增加一个英文的逗号
Base model：基模，选择较小的那个模型
VRAM：选择12G
repeat trains per image：修改为1，默认为10，但是10的效果不一定比1好

上传图片，然后再点击"Add AI captions with Florence-2"，生成图片对应的提示词。首次生成时会自动下载模型，模型大概1.5G。

Max Train Expochs：最多的训练轮次，假如提前收敛则会提前结束。
Expected training steps：自动计算出来的训练步数
Sample Image Prompts：提示词样例，不影响训练结果
Sample Image Every N Steps：不要修改该值
Resize dataset images：训练模型结果对应的分辨率。

注意（使用阶段的剧透）：即使Resize dataset images设置为512*512，但是如果空潜空间图像设置为1024*1024，那么最终生成的还是1024*1024的图像。

2）高级选项

点击Advanced options打开高级选项：

save_every_n_epochs：每N次保存一次模型，总轮次不多的话填1
console_log_file：日志文件位置，比如："/data/work/xiehao/fluxgym/log2/2253"，记得加双引号。
console_log_simple：打勾
fp8_base_unet：打勾，因为我们使用的是fp8的模型

3）训练过程

29张图片，以上参数，会占用10G的显存。

29张图片，768分辨率，会占用12G的显存。

运行中观察Volatile GPU-Util的值，需要大于0，一般是99%或100%。

如果是0，说明停止训练了。

训练完整日志如下：

[2025-01-27 21:26:51] [INFO] Running bash "/data/work/xiehao/fluxgym/outputs/girl-flux/train.sh"
[2025-01-27 21:27:00] [INFO] 2025-01-27 21:27:00 INFO     highvram is enabled / highvramが有効です                                                                                                          train_util.py:4199
[2025-01-27 21:27:00] [INFO] WARNING  cache_latents_to_disk is enabled, so cache_latents is also enabled / cache_latents_to_diskが有効なため、cache_latentsを有効にします               train_util.py:4216
[2025-01-27 21:27:00] [INFO] /data/work/anaconda3/envs/fluxgym/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
[2025-01-27 21:27:00] [INFO] warnings.warn(
[2025-01-27 21:27:01] [INFO] You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
[2025-01-27 21:27:01] [INFO] 0%|          | 0/29 [00:00<?, ?it/s]
100%|██████████| 29/29 [00:00<00:00, 125655.80it/s]
[2025-01-27 21:27:01] [INFO] read caption:   0%|          | 0/29 [00:00<?, ?it/s]
read caption: 100%|██████████| 29/29 [00:00<00:00, 4335.74it/s]
[2025-01-27 21:27:01] [INFO] 0%|          | 0/29 [00:00<?, ?it/s]
100%|██████████| 29/29 [00:00<00:00, 679524.11it/s]
[2025-01-27 21:27:01] [INFO] accelerator device: cuda
[2025-01-27 21:27:01] [INFO] FLUX: Block swap enabled. Swapping 18 blocks, double blocks: 9, single blocks: 18.
[2025-01-27 21:27:33] [INFO] import network module: networks.lora_flux
[2025-01-27 21:27:33] [INFO] 0%|          | 0/29 [00:00<?, ?it/s]
100%|██████████| 29/29 [00:00<00:00, 6469.94it/s]
[2025-01-27 21:27:35] [INFO] 0%|          | 0/29 [00:00<?, ?it/s]
100%|██████████| 29/29 [00:00<00:00, 3760.78it/s]
[2025-01-27 21:27:41] [INFO] FLUX: Gradient checkpointing enabled. CPU offload: False
[2025-01-27 21:27:41] [INFO] prepare optimizer, data loader etc.
[2025-01-27 21:27:41] [INFO] override steps. steps for 6 epochs is / 指定エポックまでのステップ数: 174
[2025-01-27 21:27:41] [INFO] enable fp8 training for U-Net.
[2025-01-27 21:28:03] [INFO] running training / 学習開始
[2025-01-27 21:28:03] [INFO] num train images * repeats / 学習画像の数×繰り返し回数: 29
[2025-01-27 21:28:03] [INFO] num reg images / 正則化画像の数: 0
[2025-01-27 21:28:03] [INFO] num batches per epoch / 1epochのバッチ数: 29
[2025-01-27 21:28:03] [INFO] num epochs / epoch数: 6
[2025-01-27 21:28:03] [INFO] batch size per device / バッチサイズ: 1
[2025-01-27 21:28:03] [INFO] gradient accumulation steps / 勾配を合計するステップ数 = 1
[2025-01-27 21:28:03] [INFO] total optimization steps / 学習ステップ数: 174
[2025-01-27 21:28:47] [INFO] steps:   0%|          | 0/174 [00:00<?, ?it/s]
[2025-01-27 21:28:47] [INFO] epoch 1/6
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[2025-01-27 21:56:53] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux-000001.safetensors
[2025-01-27 21:56:53] [INFO] /data/work/xiehao/fluxgym/sd-scripts/networks/lora_flux.py:861: FutureWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/main/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
[2025-01-27 21:56:53] [INFO] return super().state_dict(destination, prefix, keep_vars)
[2025-01-27 21:56:53] [INFO] 
[2025-01-27 21:56:53] [INFO] epoch 2/6
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[2025-01-27 22:25:05] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux-000002.safetensors
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[2025-01-27 22:25:06] [INFO] epoch 3/6
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[2025-01-27 22:53:19] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux-000003.safetensors
[2025-01-27 22:53:19] [INFO] 
[2025-01-27 22:53:19] [INFO] epoch 4/6
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[2025-01-27 23:21:31] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux-000004.safetensors
[2025-01-27 23:21:31] [INFO] 
[2025-01-27 23:21:31] [INFO] epoch 5/6
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[2025-01-27 23:49:42] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux-000005.safetensors
[2025-01-27 23:49:42] [INFO] 
[2025-01-27 23:49:42] [INFO] epoch 6/6
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steps: 100%|██████████| 174/174 [2:49:10<00:00, 58.34s/it, avr_loss=0.433]
steps: 100%|██████████| 174/174 [2:49:10<00:00, 58.34s/it, avr_loss=0.434]
[2025-01-28 00:17:58] [INFO] saving checkpoint: /data/work/xiehao/fluxgym/outputs/girl-flux/girl-flux.safetensors
[2025-01-28 00:17:58] [INFO] steps: 100%|██████████| 174/174 [2:49:10<00:00, 58.34s/it, avr_loss=0.434]
[2025-01-28 00:18:02] [INFO] Command exited successfully
[2025-01-28 00:18:02] [INFO] Runner: <LogsViewRunner nb_logs=54 exit_code=0>

训练中，会在 /data/work/xiehao/fluxgym/outputs/tmallyc-flux 生成对应的lora模型。

本文参考：

Flux“炼丹炉”——fluxgym安装教程-CSDN博客

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原文地址:https://blog.csdn.net/benben044/article/details/145322217
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