- 🖼️ Images stored in 🌐 IPFS
- 📋 Metadata stored in 🌐 IPFS
Watch the video tutorial!
- Install all dependencies
- Prepare your trait layers and put them in
image-sources
directory - Modify
generator.ipynb
to add your traits and new layers. Also set number of images to generate. - Run
generator.ipynb
to generate traits and NFT images. Images will be stored inimages-output
directory and traits inmetadata
We are using pinata.cloud for storing images
- Create account on pinata.cloud (Free plan for 1 GB storage)
- Get keys https://app.pinata.cloud/keys (click on admin key toggle), save them to
./uploaders/ipfs-image-uploader.ipynb
- Run IPFS uploader to upload all of images
./uploaders/ipfs-image-uploader.ipynb
You can also upload metadata to IPFS. In this case you will not be able to edit it and add new traits to collection. But it is most simple way to store traits, cause you don't need API server for this.
As with images upload to IPFS we will use pinata.cloud.
Set keys and run ./uploaders/ipfs-metadata-uploader.ipynb
You can test your collection on local machine. First make sure you have installed local blockchain in docker.
- Run
everdev se start
- Navigate to "true-nft" directory:
cd true-nft
- Run
yarn install
- Run
lerna bootstrap
- Test with
yarn run test-minter-ipfs
. This command will deploy sample collection to local network. - Deploy your generated collection with
yarn run deploy-minter
. - If you have not created
.env
file in./true-nft/components/true-nft-core
, then program will generate new MultiSig wallet. Its address and keys will be on screen. Better to create .env file and put there this address and keys to prevent generation of new wallet every time you run this command. - Keys for this collection will be stored in
test-deploy-result.json
file. - Copy this file into tnft-explorer for future step:
cp test-deploy-result.json ../tnft-explorer/src
- Navigate to "tnft-explorer" directory:
cd ../tnft-explorer
- Run
yarn install
- Make sure you have copied test-deploy-result.json file after deploying collection
- Run
yarn start
- Open http://localhost:3000/ in your browser