Upload train_sincity.yaml

train_sincity.yaml

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+---
+job: extension
+config:
+  # this name will be the folder and filename name 训练结果的目录名和文件名
+  name: "sincitymov"
+  process:
+    - type: 'sd_trainer'
+      # root folder to save training sessions/samples/weights
+      training_folder: "output"
+      # uncomment to see performance stats in the terminal every N steps
+      performance_log_every: 1000
+      device: cuda:0
+      # if a trigger word is specified, it will be added to captions of training data if it does not already exist
+      # alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
+      # 触发词，不设置触发词也可以
+      trigger_word: "sincitymov"
+      network:
+        type: "lora"
+        linear: 16
+        linear_alpha: 16
+      save:
+        dtype: float16 # precision to save
+        save_every: 200 # save every this many steps 多少步保存一个模型
+        max_step_saves_to_keep: 40 # how many intermittent saves to keep 最多保留多少个模型，可以设置大一点，反正每个文件也就168M
+      datasets:
+        - folder_path: "C:/sincitymov" # 数据集所在目录
+          caption_ext: "txt"
+          caption_dropout_rate: 0.05  # will drop out the caption 5% of time
+          shuffle_tokens: false  # shuffle caption order, split by commas
+          cache_latents_to_disk: true  # leave this true unless you know what you're doing
+          resolution: [ 512, 768, 1024 ]  # flux enjoys multiple resolutions
+      train:
+        batch_size: 1
+        steps: 4000  # total number of steps to train 500 - 4000 is a good range 成与不成通常在1500步以内
+        gradient_accumulation_steps: 1
+        train_unet: true
+        train_text_encoder: false  # probably won't work with flux
+        content_or_style: balanced  # content, style, balanced
+        gradient_checkpointing: true  # need the on unless you have a ton of vram
+        noise_scheduler: "flowmatch" # for training only
+        optimizer: "adamw8bit"
+        lr: 6e-4 # 学习率，默认是1e-4，就是0.0001如果训练下来发现没学到特征就加大，很快就过拟合了就缩小
+        # uncomment this to skip the pre training sample
+        skip_first_sample: true
+
+        # ema will smooth out learning, but could slow it down. Recommended to leave on.
+        ema_config:
+          use_ema: true
+          ema_decay: 0.99
+
+        # will probably need this if gpu supports it for flux, other dtypes may not work correctly
+        dtype: bf16
+      model:
+        # huggingface model name or path
+        name_or_path: "black-forest-labs/FLUX.1-dev"
+        is_flux: true
+        quantize: true  # run 8bit mixed precision
+        low_vram: true  # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower. 量化处理模型的时候减少显存占用
+      sample:
+        sampler: "flowmatch" # must match train.noise_scheduler
+        sample_every: 200 # sample every this many steps 多少步输出一批测试图片，最好和保存模型的步数一致，否则没办法把测试图片和模型直接对应起来
+        width: 1024
+        height: 1024
+        prompts:
+          # you can add [trigger] to the prompts here and it will be replaced with the trigger word 生成测试图片的提示词
+#          - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
+          - "a woman holding a coffee cup, in a beanie, sitting at a cafe, [trigger]"
+          - "[trigger], A monochromatic portrait of a woman with striking green eyes and bold red lipstick. She is positioned in a dramatic pose, holding a pistol in her right hand."
+          - "a bear building a log cabin in the snow covered mountains, [trigger]"
+          - "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop, [trigger]"
+          - "a man holding a sign that says, 'this is a sign', [trigger]"
+          - "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
+        neg: ""  # not used on flux 不需要设置负面提示词
+        seed: 42
+        walk_seed: true
+        guidance_scale: 4
+        sample_steps: 20
+# you can add any additional meta info here. [name] is replaced with config name at top
+meta:
+  name: "[sincitymov]"
+  version: '1.0'