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| job: extension | |
| config: | |
| name: example_name | |
| process: | |
| - type: 'image_reference_slider_trainer' | |
| training_folder: "/mnt/Train/out/LoRA" | |
| device: cuda:0 | |
| # for tensorboard logging | |
| log_dir: "/home/jaret/Dev/.tensorboard" | |
| network: | |
| type: "lora" | |
| linear: 8 | |
| linear_alpha: 8 | |
| train: | |
| noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a" | |
| steps: 5000 | |
| lr: 1e-4 | |
| train_unet: true | |
| gradient_checkpointing: true | |
| train_text_encoder: true | |
| optimizer: "adamw" | |
| optimizer_params: | |
| weight_decay: 1e-2 | |
| lr_scheduler: "constant" | |
| max_denoising_steps: 1000 | |
| batch_size: 1 | |
| dtype: bf16 | |
| xformers: true | |
| skip_first_sample: true | |
| noise_offset: 0.0 | |
| model: | |
| name_or_path: "/path/to/model.safetensors" | |
| is_v2: false # for v2 models | |
| is_xl: false # for SDXL models | |
| is_v_pred: false # for v-prediction models (most v2 models) | |
| save: | |
| dtype: float16 # precision to save | |
| save_every: 1000 # save every this many steps | |
| max_step_saves_to_keep: 2 # only affects step counts | |
| sample: | |
| sampler: "ddpm" # must match train.noise_scheduler | |
| sample_every: 100 # sample every this many steps | |
| width: 512 | |
| height: 512 | |
| prompts: | |
| - "photo of a woman with red hair taking a selfie --m -3" | |
| - "photo of a woman with red hair taking a selfie --m -1" | |
| - "photo of a woman with red hair taking a selfie --m 1" | |
| - "photo of a woman with red hair taking a selfie --m 3" | |
| - "close up photo of a man smiling at the camera, in a tank top --m -3" | |
| - "close up photo of a man smiling at the camera, in a tank top--m -1" | |
| - "close up photo of a man smiling at the camera, in a tank top --m 1" | |
| - "close up photo of a man smiling at the camera, in a tank top --m 3" | |
| - "photo of a blonde woman smiling, barista --m -3" | |
| - "photo of a blonde woman smiling, barista --m -1" | |
| - "photo of a blonde woman smiling, barista --m 1" | |
| - "photo of a blonde woman smiling, barista --m 3" | |
| - "photo of a Christina Hendricks --m -1" | |
| - "photo of a Christina Hendricks --m -1" | |
| - "photo of a Christina Hendricks --m 1" | |
| - "photo of a Christina Hendricks --m 3" | |
| - "photo of a Christina Ricci --m -3" | |
| - "photo of a Christina Ricci --m -1" | |
| - "photo of a Christina Ricci --m 1" | |
| - "photo of a Christina Ricci --m 3" | |
| neg: "cartoon, fake, drawing, illustration, cgi, animated, anime" | |
| seed: 42 | |
| walk_seed: false | |
| guidance_scale: 7 | |
| sample_steps: 20 | |
| network_multiplier: 1.0 | |
| logging: | |
| log_every: 10 # log every this many steps | |
| use_wandb: false # not supported yet | |
| verbose: false | |
| slider: | |
| datasets: | |
| - pair_folder: "/path/to/folder/side/by/side/images" | |
| network_weight: 2.0 | |
| target_class: "" # only used as default if caption txt are not present | |
| size: 512 | |
| - pair_folder: "/path/to/folder/side/by/side/images" | |
| network_weight: 4.0 | |
| target_class: "" # only used as default if caption txt are not present | |
| size: 512 | |
| # you can put any information you want here, and it will be saved in the model | |
| # the below is an example. I recommend doing trigger words at a minimum | |
| # in the metadata. The software will include this plus some other information | |
| meta: | |
| name: "[name]" # [name] gets replaced with the name above | |
| description: A short description of your model | |
| trigger_words: | |
| - put | |
| - trigger | |
| - words | |
| - here | |
| version: '0.1' | |
| creator: | |
| name: Your Name | |
| email: [email protected] | |
| website: https://yourwebsite.com | |
| any: All meta data above is arbitrary, it can be whatever you want. |