CURSOR

AI-Driven App Architect

As AI transforms the coding process, the role of traditional programmers is becoming increasingly commoditized. However, this shift elevates visionaries and architects who can orchestrate systems and integrate diverse components.

As an architect, I leverage AI tools to accelerate development, focusing not just on how to code but on what to build and why. By modularizing projects and utilizing the best APIs, SDKs, and open-source solutions, I often deliver MVPs at 10x speed.

AI-Powered Development with Cursor & Wispr

I interact with Cursor for intelligent code suggestions, make adjustments, save, and instantly see real-time preview updates. This rinse-and-repeat process creates a fast, iterative development cycle, enabling me to focus on high-level architecture while AI speeds up execution. This empowers me to iterate quickly.

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Demo on X. To fine-tune the FLUX Pro model on Replicate:

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Prerequisites:

  1. A Replicate account.
  2. Training images (around 12-20 for best results).
  3. A few dollars for running the model (approximately $2 for a full run).

Fine-Tuning Steps:

  1. Gather Training Data:
    • You’ll need a collection of high-quality images (JPG or PNG) representing the concept you want to teach the model. Ensure variety in settings, poses, and lighting to cover different aspects of the subject.
    • Optionally, you can create captions in a .txt file for each image, but it’s not mandatory.
  2. Prepare a Zip File:
    • Once you have the images, zip them up. This zip file will be uploaded to Replicate for fine-tuning.
  3. Use a Unique Trigger Word:
    • Choose a unique trigger word to activate your specific fine-tuning. This is the term you’ll use in prompts to activate the fine-tuned model (e.g., “MY_TRIGGER”).
    • Avoid common words to prevent clashes with other models.
  4. Start the Fine-Tuning Process:
    • On Replicate, navigate to the FLUX.1 fine-tuning page.
    • Upload your zip file and fill in the required parameters (e.g., the number of training steps, trigger word, etc.).
    • For most use cases, leave the defaults for learning rate and other advanced settings.
  5. Monitor Training:
    • The process usually takes about 20 minutes for 1000 steps and 10 images. You can use Replicate’s web interface or programmatically through the API to track the training progress.
  6. Generate Images:
    • Once training is complete, you can generate images via the web or through an API. In your prompt, be sure to include the trigger word to use your fine-tuned concept.
  7. Optional: API Integration:
    • You can integrate the trained model into your app using the Replicate API. The API provides flexibility, allowing you to run and tweak your model programmatically in languages like Python and JavaScript.

With these steps, you’ll be able to fine-tune and customize the FLUX Pro model to generate specific images based on the training data you’ve provided.

GitHub - IkigaiLabsETH/train
Contribute to IkigaiLabsETH/train development by creating an account on GitHub.

If you'd like to see more details on this process, you can check out the Replicate blog and AI Devin’s guide.