Option A — Midjourney (simple, human-operated)

1. Create a Midjourney account and select a plan.
2. Open your prompt list and Midjourney side-by-side.




3. Copy → paste → wait for the grid → upscale winners → download.
4. Optional: purchase extra “Fast” time to accelerate large batches.
5. Efficiency boost: Instead of upscaling and downloading in real time as the images get created, instead, do a single bulk curation pass ONLY at the very end. Upscale and download only the very best images that you’ll actually turn into products.

This approach is repetitive, but mechanically simple. Put on music, relax, copy/paste your prompts in batches, and collect the best results—no technical setup is required, and no sweaty computer-programming workflows are required.

Option B — DALL·E 3 (fully automated, from a CSV)

Prefer true hands-off generation? Use OpenAI’s Images API. Place your prompts in a simple CSV (one per line), run the Python script below, and images will be saved to a folder automatically.

# bulk_dalle3_from_csv.py
# Generate images in bulk via OpenAI's Images API using DALL·E 3.
# Reads prompts from prompts.csv (first column), saves PNGs to AI_generated_images/.

import os
import csv
import base64
from pathlib import Path
from typing import List
from openai import OpenAI

# --- Configuration: You edit this as needed before running ---
openai_api_key = "YOUR_OPENAI_API_KEY" # replace with your actual OpenAI API key
prompts_csv_path = "prompts.csv" # first column only, one prompt per row
output_folder_path = "AI_generated_images"
target_model_name = "dall-e-3" # swap to "gpt-image-1" if you prefer that model
image_size_setting = "1024x1024" # DALL·E 3: 1024x1024, 1792x1024 (wide), 1024x1792 (tall)
image_quality_setting = "standard" # DALL·E 3: "standard" or "hd"

# --- Setup ---
client = OpenAI(api_key=openai_api_key)
output_dir = Path(output_folder_path)
output_dir.mkdir(parents=True, exist_ok=True)

def load_prompts_from_csv(csv_path: str) -> List[str]:
    """Read the first column of a CSV; return a list of non-empty prompt strings."""
    prompts: List[str] = []
    with open(csv_path, "r", newline="", encoding="utf-8") as f:
        reader = csv.reader(f)
        for row in reader:
            if not row:
                continue
            prompt_text = (row[0] or "").strip()
            if prompt_text:
                prompts.append(prompt_text)
    return prompts

def generate_and_save_image(prompt_text: str, image_index: int) -> Path:
    """Generate one image from prompt_text and save it as PNG."""
    print(f"[{image_index}] Generating: {prompt_text[:80]}{'...' if len(prompt_text) > 80 else ''}")

    result = client.images.generate(
        model=target_model_name,
        prompt=prompt_text,
        size=image_size_setting,
        quality=image_quality_setting,  # optional; valid values depend on model
        n=1
    )

    b64_data = result.data[0].b64_json  # API returns base64-encoded image
    image_bytes = base64.b64decode(b64_data)
    output_file = output_dir / f"image_{image_index:04d}.png"
    with open(output_file, "wb") as f:
        f.write(image_bytes)

    print(f"[{image_index}] Saved: {output_file}")
    return output_file

def main():
    all_prompts = load_prompts_from_csv(prompts_csv_path)
    if not all_prompts:
        print("No prompts found in CSV. Ensure the first column contains one prompt per row.")
        return

    for idx, current_prompt in enumerate(all_prompts, start=1):
        try:
            generate_and_save_image(current_prompt, idx)
        except Exception as e:
            print(f"[{idx}] ERROR: {e}")

if __name__ == "__main__":
    main()

Prefer the newest image model? Change target_model_name to "gpt-image-1" and use a supported size (for example, 1024x1024, 1536x1024, or 1024x1536).

Meet the Bulk POD Product Creator

The Bulk POD Product Creator takes a folder of images and your perfected Example Product (one you configured by hand) and produces a batch of new products automatically. It:

• Applies your placement logic across sizes (fill the area quickly, preserve aspect ratio & center, or exactly match your Example Product).
• Replicates variants and pricing from your Example Product for consistent listings.
• Generates SEO-aware titles/descriptions/tags automatically—or uses your templates and suffixes.
• Saves your configuration as a Saved Operation for one-click reuse.

Why pairing AI image generation + Bulk POD Product Creator works

• Speed: go from a folder of fresh images to dozens/hundreds of listings in a single run.
• Quality: uniform placements and consistent variant/pricing rules.
• SEO coverage: strong titles/descriptions/tags on every product, without burnout.
• Repeatability: once you like the outputs for a theme, you can scale it reliably.

Your control panel at a glance

1. Image intake: drop in your artwork images.

2. Placement logic: Stretch to fill; Preserve aspect ratio & center; Exactly match Example.



3. Product blueprint: Example Product defines provider/blueprint, print areas, variants, and baseline pricing.




4. SEO engine: choose title format (simple or compound) + standardized suffix; generate AI descriptions and append reusable HTML specs; combine artwork-specific tags with broad, product-level tags.

A simple summary of how it works

1. Choose/select your images (your new batch).
2. Select placement mode (fill, preserve/center, or match Example).
3. Connect your print-on-demand store (API token).
4. Pick your Example Product (the one to mirror).
5. Set your SEO rules (title format + suffix, description strategy + optional HTML, tags).
6. Run the automated operation. Review outputs; publish.