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| job: extension | |
| config: | |
| # this name will be the folder and filename name | |
| name: "my_first_wan21_1b_lora_v1" | |
| 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 | |
| # trigger_word: "p3r5on" | |
| network: | |
| type: "lora" | |
| linear: 32 | |
| linear_alpha: 32 | |
| save: | |
| dtype: float16 # precision to save | |
| save_every: 250 # save every this many steps | |
| max_step_saves_to_keep: 4 # how many intermittent saves to keep | |
| push_to_hub: false #change this to True to push your trained model to Hugging Face. | |
| # You can either set up a HF_TOKEN env variable or you'll be prompted to log-in | |
| # hf_repo_id: your-username/your-model-slug | |
| # hf_private: true #whether the repo is private or public | |
| datasets: | |
| # datasets are a folder of images. captions need to be txt files with the same name as the image | |
| # for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently | |
| # images will automatically be resized and bucketed into the resolution specified | |
| # on windows, escape back slashes with another backslash so | |
| # "C:\\path\\to\\images\\folder" | |
| # AI-Toolkit does not currently support video datasets, we will train on 1 frame at a time | |
| # it works well for characters, but not as well for "actions" | |
| - folder_path: "/path/to/images/folder" | |
| 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: [ 632 ] # will be around 480p | |
| train: | |
| batch_size: 1 | |
| steps: 2000 # total number of steps to train 500 - 4000 is a good range | |
| gradient_accumulation: 1 | |
| train_unet: true | |
| train_text_encoder: false # probably won't work with wan | |
| gradient_checkpointing: true # need the on unless you have a ton of vram | |
| noise_scheduler: "flowmatch" # for training only | |
| timestep_type: 'sigmoid' | |
| optimizer: "adamw8bit" | |
| lr: 1e-4 | |
| optimizer_params: | |
| weight_decay: 1e-4 | |
| # uncomment this to skip the pre training sample | |
| # skip_first_sample: true | |
| # uncomment to completely disable sampling | |
| # disable_sampling: true | |
| # ema will smooth out learning, but could slow it down. Recommended to leave on. | |
| ema_config: | |
| use_ema: true | |
| ema_decay: 0.99 | |
| dtype: bf16 | |
| model: | |
| # huggingface model name or path | |
| name_or_path: "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" | |
| arch: 'wan21' | |
| quantize_te: true # saves vram | |
| sample: | |
| sampler: "flowmatch" | |
| sample_every: 250 # sample every this many steps | |
| width: 832 | |
| height: 480 | |
| num_frames: 40 | |
| fps: 15 | |
| # samples take a long time. so use them sparingly | |
| # samples will be animated webp files, if you don't see them animated, open in a browser. | |
| prompts: | |
| # you can add [trigger] to the prompts here and it will be replaced with the trigger word | |
| # - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\ | |
| - "woman playing the guitar, on stage, singing a song, laser lights, punk rocker" | |
| neg: "" | |
| seed: 42 | |
| walk_seed: true | |
| guidance_scale: 5 | |
| sample_steps: 30 | |
| # you can add any additional meta info here. [name] is replaced with config name at top | |
| meta: | |
| name: "[name]" | |
| version: '1.0' | |