Enhance AI with Your Content: A Step-by-Step Guide
Capture Wallet powered by Numbers Protocol plays a crucial role in building ethical AI with user-owned and user-controlled dataset. Combining AssetTree (metadata) and Nid (decentralized content storage) together, digital content (image, video and music files) in the Capture wallet forms a robust training dataset for AI. This setup allows AI models to access user content securely, with explicit wallet permission and AI training consent.
By adhering to ERC-7517, it filters content that allows AI training using miningPreference
field in the AssetTree , further protecting the creators' rights and ensuring ethical AI practices.
The page demonstrates the use case that developers can utilize Capture, HuggingFace, and Colab to train your own models with your registered assets and generate new AI inference images. By following these steps, you can leverage our service to enhance your creative capabilities and produce high-quality images. By filtering content following ERC-7517,
Access Colab with Hugging Face Diffusers
Click on the provided Colab link to open it in your web browser.
Navigate to the urls in the
Settings for teaching your new concept
section.
Access Data From Capture Wallet
Replace the entire code block labeled 'urls' with the provided code. The code will list links to the creator's images that match the configured mining preference and AI training tag.
Make sure to complete the fields: fill in
capture_token
with your Capture token,creator_email
with your Capture email, andmatched_tag_value
with the tag assigned to assets that will be used in this training.
Configure Training Parameters
Configure the
instance_prompt
in theSettings for your newly created concept
subsection.
Navigate to the
Run the code with your newly trained model
section. Ensure to tick thesave_concept
option, specifyname_of_your_concept
, choose where_to_save_concept, and inputhf_token_write
with your Hugging Face token in theSave your newly created concept?
subsection.
Click on
Show code
and replace the codeapi = HfApi()
withapi = HfApi(token=hf_token_write)
in the same section.
Train Your Model
Run each subsection in the
Initial setup
,Settings for teaching your new concept
, andTeach the model the new concept (fine-tuning with Dreambooth)
sections to train your model.
Improve training data (Optional)
Dreambooth automatically crops center square images. If your target isn't centered but you want to refine the training data, here's what to do after you've completed the
Setup and check the images you have just added
subsection but before moving on toSettings for your newly created concept
:Download the images from Colab to your local machine.
Remove the original images from Colab.
Manually crop the images as necessary and save them locally.
Once cropped, upload the newly saved images back to Colab.
Save Your Model
Run the
Save your newly created concept?
subsection in theRun the code with your newly trained model
section to save your trained model.
Generating AI Inference Images
Click the link
Click here to access it
to open the Hugging Face model.
Run the
Text-to-Image
task to generate a new image.
Examples
Usopp toy
Elephant toy
Lion toy
Usopp toy (Improve training data)
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