? - SupaSeeder - ?
Using AI to Generate Seed Data for SupabaseEvery developer knows the importance of having good seed data for fast testing and development. Manually crafting SQL queries to populate your database with realistic data can be tedious and error-prone, especially when dealing with complex relationships between tables.SupaSeeder connects to your Supabase instance, extracts the database schema, then you can either generate SQL insert statements or optimized prompts to use with any AI model to generate the seed data you need.


Try it out online at supaseeder.vercel.app ?



⚙️ How It Works

Provide Supabase URL Anon Key
Connects to your database and reads the schema.
Write a prompt
Describe the data you want (e.g. "10 users with 5 posts each").
Pick mode


Prompt Mode: Get optimized prompts to use with any AI (ChatGPT, Claude, etc.)

Direct Mode: Get complete SQL queries generated using OpenAI


Get SQL output
Copy paste to your SQL editor or Supabase SQL Runner.



?️ Setup Locally



Method 2: Run Locally

Clone the repository:

⛶git clone https://github.com/mmvergara/supaseeder.git
cd supaseeder
Install dependencies:

⛶npm install
# or
yarn install
# or
pnpm install
Start the development server:

⛶npm run dev
# or
yarn dev
# or
pnpm dev

This is a simplified guide to an AI model called Controlnet-1.1-X-Realistic-Vision-V2.0 maintained by Usamaehsan. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.


Model overview
The controlnet-1.1-x-realistic-vision-v2.0 model is a powerful AI tool created by Usama Ehsan that combines several advanced techniques to generate high-quality, realistic images. It builds upon the ControlNet and Realistic Vision models, incorporating techniques like multi-ControlNet, single-ControlNet, IP-Adapter, and consistency-decoder to produce remarkably realistic and visually stunning outputs.


Model inputs and outputs
The controlnet-1.1-x-realistic-vision-v2.0 model takes a variety of inputs, including an image, a prompt, and various parameters to fine-tune the generation process. The output is a high-quality, realistic image that aligns with the provided prompt and input image.


Inputs


Image: The input image that serves as a reference or starting point for the generation process.

Prompt: A text description that guides the model in generating the desired image.

Seed: A numerical value that can be used to randomize the generation process.

Steps: The number of inference steps to be taken during the generation process.

Strength: The strength or weight of the control signal, which determines how much the model should focus on the input image.

Max Width/Height: The maximum dimensions of the generated image.

Guidance Scale: A parameter that controls the balance between the input prompt and the control signal.

Negative Prompt: A text description that specifies elements to be avoided in the generated image.



Outputs


Output Image: The generated, high-quality, realistic image that aligns with the provided prompt and input image.



Capabilities
The `controlnet-1.1-x-realistic-vision...Click here to read the full guide to Controlnet-1.1-X-Realistic-Vision-V2.0