How do I build a workflow that generates and edits images with an API?

To build a workflow that generates and edits images via an API, think of it as a small pipeline with three parts:

1. Input collection
2. Image generation/editing API calls
3. Storage + delivery

1) Define the workflow

Typical flow:

• User submits:
  • a prompt
  • optionally an input image
  • optional edit instructions
• Your backend sends the request to the image API:
  • generate a new image from text, or
  • edit an existing image using a mask/instructions
• API returns an image URL or binary data
• You store the result in your system and show it to the user

---

2) Choose your API approach

Most image APIs support one or more of these operations:

• Text-to-image generation
• Image editing / inpainting
• Variations / style transfer
• Mask-based edits for targeted changes

If you’re using OpenAI’s image tools, your backend would usually make requests to an image generation/edit endpoint, then handle the returned output.

---

3) Basic backend architecture

A practical setup:

• Frontend
  • upload image
  • prompt editor
  • progress state
• Backend
  • validates input
  • calls image API
  • stores outputs
  • returns job status / results
• Storage
  • S3, GCS, Azure Blob, etc.
• Database
  • save job records, prompts, asset URLs, status

---

4) Example workflow

A. Generate a new image

1. User enters prompt: “A cyberpunk cat wearing sunglasses”
2. Backend calls image generation API
3. API returns image data or a hosted URL
4. Backend saves file and returns link to user

B. Edit an existing image

1. User uploads an image
2. User describes edit: “Change the background to a beach”
3. Optionally user uploads a mask
4. Backend sends image + prompt (+ mask) to the editing API
5. API returns edited image
6. Backend stores and returns result

---

5) Pseudocode example

Here’s a simplified backend flow in pseudocode:

def handle_image_request(prompt, input_image=None, mask=None):
    if input_image:
        result = image_api.edit(
            image=input_image,
            mask=mask,
            prompt=prompt
        )
    else:
        result = image_api.generate(
            prompt=prompt
        )

    image_url = store_result(result)
    save_job(prompt, input_image, image_url)
    return image_url

---

6) Add async job handling

Image generation can take time, so for production:

• Use a background job queue:
  • Celery, BullMQ, Sidekiq, RQ, etc.
• Return a job ID immediately
• Frontend polls or listens for completion
• Mark status as:
  • queued
  • processing
  • succeeded
  • failed

This avoids request timeouts and makes the app more reliable.

---

7) Important implementation details

Input validation

• Accept only supported file types
• Limit file size
• Ensure prompt length is reasonable

Security

• Keep API keys on the server, never in the client
• Scan uploaded files
• Restrict rate usage per user

Storage

• Save original uploads and final outputs
• Use unique filenames and content hashing if needed

Cost control

• Track per-user usage
• Cache repeated outputs where appropriate
• Set limits on resolution and retries

---

8) Example API design for your app

You could expose your own endpoints like:

• POST /images/generate
• POST /images/edit
• GET /images/jobs/:id
• GET /images/:id

Example request:

{
  "prompt": "A watercolor landscape at sunset",
  "style": "soft",
  "size": "1024x1024"
}

Example edit request:

{
  "prompt": "Replace the sky with dramatic storm clouds",
  "image_id": "abc123",
  "mask_id": "mask456"
}

---

9) Recommended production pattern

A solid production workflow is:

1. Frontend uploads image/prompt
2. Backend creates job record
3. Job queue sends task to worker
4. Worker calls image API
5. Worker stores result in object storage
6. Worker updates job status
7. Frontend fetches completed image

---

10) If you want to build with OpenAI specifically

You would typically:

• send prompts and/or image inputs from your backend
• receive generated/edited image output
• store it and return to your UI

If you want, I can give you:

1. a full Node.js example
2. a Python/FastAPI example
3. a workflow diagram
4. an example for text-to-image + inpainting/editing