87 lines
3.4 KiB
TypeScript
87 lines
3.4 KiB
TypeScript
import React from 'react';
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import { ConfigDoc } from '@/types';
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const docs: { [key: string]: ConfigDoc } = {
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'config.name': {
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title: 'Training Name',
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description: (
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<>
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The name of the training job. This name will be used to identify the job in the system and will the the filename
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of the final model. It must be unique and can only contain alphanumeric characters, underscores, and dashes. No
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spaces or special characters are allowed.
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</>
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),
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},
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'gpuids': {
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title: 'GPU ID',
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description: (
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<>
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This is the GPU that will be used for training. Only one GPU can be used per job at a time via the UI currently.
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However, you can start multiple jobs in parallel, each using a different GPU.
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</>
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),
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},
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'config.process[0].trigger_word': {
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title: 'Trigger Word',
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description: (
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<>
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Optional: This will be the word or token used to trigger your concept or character.
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<br />
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<br />
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When using a trigger word,
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If your captions do not contain the trigger word, it will be added automatically the beginning of the caption. If you do not have
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captions, the caption will become just the trigger word. If you want to have variable trigger words in your captions to put it in different spots,
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you can use the <code>{'[trigger]'}</code> placeholder in your captions. This will be automatically replaced with your trigger word.
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<br />
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<br />
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Trigger words will not automatically be added to your test prompts, so you will need to either add your trigger word manually or use the
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<code>{'[trigger]'}</code> placeholder in your test prompts as well.
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</>
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),
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},
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'config.process[0].model.name_or_path': {
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title: 'Name or Path',
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description: (
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<>
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The name of a diffusers repo on Huggingface or the local path to the base model you want to train from. The folder needs to be in
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diffusers format for most models. For some models, such as SDXL and SD1, you can put the path to an all in one safetensors checkpoint here.
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</>
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),
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},
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'datasets.control_path': {
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title: 'Control Dataset',
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description: (
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<>
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The control dataset needs to have files that match the filenames of your training dataset. They should be matching file pairs.
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These images are fed as control/input images during training.
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</>
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),
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},
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'datasets.num_frames': {
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title: 'Number of Frames',
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description: (
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<>
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This sets the number of frames to shrink videos to for a video dataset. If this dataset is images, set this to 1 for one frame.
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If your dataset is only videos, frames will be extracted evenly spaced from the videos in the dataset.
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<br/>
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<br/>
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It is best to trim your videos to the proper length before training. Wan is 16 frames a second. Doing 81 frames will result in a 5 second video.
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So you would want all of your videos trimmed to around 5 seconds for best results.
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<br/>
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<br/>
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Example: Setting this to 81 and having 2 videos in your dataset, one is 2 seconds and one is 90 seconds long, will result in 81
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evenly spaced frames for each video making the 2 second video appear slow and the 90second video appear very fast.
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</>
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),
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},
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};
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export const getDoc = (key: string | null | undefined): ConfigDoc | null => {
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if (key && key in docs) {
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return docs[key];
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}
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return null;
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};
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export default docs;
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