Added train example to ti
This commit is contained in:
Jaret Burkett
@@ -1,70 +1,72 @@
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---
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---
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job: extension
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job: extension
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config:
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config:
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name: example_name
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name: test_v1
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process:
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process:
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- type: 'image_reference_slider_trainer'
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- type: 'textual_inversion_trainer'
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training_folder: "/mnt/Train/out/LoRA"
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training_folder: "out/TI"
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device: cuda:0
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device: cuda:0
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# for tensorboard logging
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# for tensorboard logging
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log_dir: "/home/jaret/Dev/.tensorboard"
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log_dir: "out/.tensorboard"
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network:
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embedding:
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type: "lora"
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trigger: "your_trigger_here"
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linear: 8
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tokens: 12
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linear_alpha: 8
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init_words: "man with short brown hair"
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save_format: "safetensors" # 'safetensors' or 'pt'
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save:
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dtype: float16 # precision to save
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save_every: 100 # save every this many steps
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max_step_saves_to_keep: 5 # only affects step counts
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datasets:
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- folder_path: "/path/to/dataset"
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caption_type: "txt"
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default_caption: "[trigger]"
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buckets: true
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resolution: 512
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train:
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train:
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noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
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noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
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steps: 5000
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noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
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lr: 1e-4
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steps: 3000
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train_unet: true
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weight_jitter: 0.0
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lr: 5e-5
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train_unet: false
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gradient_checkpointing: true
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gradient_checkpointing: true
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train_text_encoder: true
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train_text_encoder: false
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optimizer: "adamw"
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optimizer: "adamw"
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# optimizer: "prodigy"
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optimizer_params:
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optimizer_params:
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weight_decay: 1e-2
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weight_decay: 1e-2
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lr_scheduler: "constant"
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lr_scheduler: "constant"
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max_denoising_steps: 1000
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max_denoising_steps: 1000
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batch_size: 1
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batch_size: 4
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dtype: bf16
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dtype: bf16
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xformers: true
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xformers: true
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skip_first_sample: true
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min_snr_gamma: 5.0
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noise_offset: 0.0
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# skip_first_sample: true
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noise_offset: 0.0 # not needed for this
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model:
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model:
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name_or_path: "/path/to/model.safetensors"
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# objective reality v2
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name_or_path: "https://civitai.com/models/128453?modelVersionId=142465"
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is_v2: false # for v2 models
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is_v2: false # for v2 models
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is_xl: false # for SDXL models
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is_xl: false # for SDXL models
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is_v_pred: false # for v-prediction models (most v2 models)
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is_v_pred: false # for v-prediction models (most v2 models)
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save:
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dtype: float16 # precision to save
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save_every: 1000 # save every this many steps
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max_step_saves_to_keep: 2 # only affects step counts
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sample:
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sample:
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sampler: "ddpm" # must match train.noise_scheduler
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sampler: "ddpm" # must match train.noise_scheduler
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sample_every: 100 # sample every this many steps
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sample_every: 100 # sample every this many steps
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width: 512
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width: 512
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height: 512
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height: 512
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prompts:
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prompts:
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- "photo of a woman with red hair taking a selfie --m -3"
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- "photo of [trigger] laughing"
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- "photo of a woman with red hair taking a selfie --m -1"
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- "photo of [trigger] smiling"
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- "photo of a woman with red hair taking a selfie --m 1"
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- "[trigger] close up"
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- "photo of a woman with red hair taking a selfie --m 3"
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- "dark scene [trigger] frozen"
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- "close up photo of a man smiling at the camera, in a tank top --m -3"
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- "[trigger] nighttime"
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- "close up photo of a man smiling at the camera, in a tank top--m -1"
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- "a painting of [trigger]"
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- "close up photo of a man smiling at the camera, in a tank top --m 1"
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- "a drawing of [trigger]"
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- "close up photo of a man smiling at the camera, in a tank top --m 3"
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- "a cartoon of [trigger]"
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- "photo of a blonde woman smiling, barista --m -3"
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- "[trigger] pixar style"
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- "photo of a blonde woman smiling, barista --m -1"
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- "[trigger] costume"
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- "photo of a blonde woman smiling, barista --m 1"
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neg: ""
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- "photo of a blonde woman smiling, barista --m 3"
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- "photo of a Christina Hendricks --m -1"
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- "photo of a Christina Hendricks --m -1"
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- "photo of a Christina Hendricks --m 1"
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- "photo of a Christina Hendricks --m 3"
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- "photo of a Christina Ricci --m -3"
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- "photo of a Christina Ricci --m -1"
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- "photo of a Christina Ricci --m 1"
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- "photo of a Christina Ricci --m 3"
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neg: "cartoon, fake, drawing, illustration, cgi, animated, anime"
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seed: 42
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seed: 42
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walk_seed: false
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walk_seed: false
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guidance_scale: 7
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guidance_scale: 7
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@@ -76,32 +78,15 @@ config:
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use_wandb: false # not supported yet
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use_wandb: false # not supported yet
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verbose: false
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verbose: false
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slider:
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# You can put any information you want here, and it will be saved in the model.
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datasets:
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# The below is an example, but you can put your grocery list in it if you want.
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- pair_folder: "/path/to/folder/side/by/side/images"
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# It is saved in the model so be aware of that. The software will include this
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network_weight: 2.0
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# plus some other information for you automatically
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target_class: "" # only used as default if caption txt are not present
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size: 512
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- pair_folder: "/path/to/folder/side/by/side/images"
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network_weight: 4.0
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target_class: "" # only used as default if caption txt are not present
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size: 512
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# you can put any information you want here, and it will be saved in the model
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# the below is an example. I recommend doing trigger words at a minimum
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# in the metadata. The software will include this plus some other information
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meta:
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meta:
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name: "[name]" # [name] gets replaced with the name above
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# [name] gets replaced with the name above
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description: A short description of your model
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name: "[name]"
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trigger_words:
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# version: '1.0'
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- put
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# creator:
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- trigger
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# name: Your Name
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- words
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# email: your@gmail.com
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- here
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# website: https://your.website
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version: '0.1'
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creator:
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name: Your Name
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email: your@email.com
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website: https://yourwebsite.com
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any: All meta data above is arbitrary, it can be whatever you want.
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