Introduction

AI-generated images are everywhere. From indie game developers prototyping character art to creative teams building ad visuals, image generation has become a core part of the modern content pipeline. But while generating a few images with tools like Midjourney or DALL·E might feel like magic, scaling those same workflows across products, users, or teams introduces a whole different set of challenges.

Let’s break down what image generation is useful for, who’s using it, and how serverless infrastructure, especially platforms like ByteNite, is redefining how developers scale it.

Popular models you can use today

Several models have emerged as the go-to tools for generating images:

• DALL·E 3 (OpenAI) – Known for its strong prompt understanding with detailed outputs, especially for text-in-image rendering.
• Midjourney v6 (Midjourney) – Offers an artistic edge, excelling in photorealism and prompt coherence. Popular in design circles and runs via Discord.
• Stable Diffusion 3.5 (Stability AI) – Fully open-source, with dozens of fine-tuned variants for everything from photorealism to anime.

And now, newer entrants like Kandinsky 3.0 and Playground v2 are pushing the boundaries of quality and speed.

Getting started: the simple path

If you’re just starting out with image generation, these serverless image-generation APIs offer the easiest way to explore without heavy setup:

• OpenAI API – A powerful and easy-to-use REST API for generating images (like DALL·E 3). Authentication is straightforward, and you can be up and running with just a few lines of code.
• Stable Diffusion API – A fully managed, cost-effective REST API for generating images with the latest Stable Diffusion models (including SDXL and 3.5), no specialized hardware or local setup required.
• Hugging Face Inference API – A unified, serverless REST API that lets you generate images (and run other AI tasks) with thousands of open-source and proprietary models directly from the Hugging Face Model Hub.

At its core, generating an image from a model using these APIs requires just a few things:

• A prompt: Plain text, like “A golden retriever riding a skateboard at the skate park.”
• A model: Like Stable Diffusion or DALL·E.
• A platform: An active account on one of the platforms listed above to run the model, process inputs, and return the output.

Once you have these, your platform will provide the necessary computing resources, like VMs with GPUs, CPUs, and RAM, to process inputs, run the model, and return the output.

Here's how simple a basic implementation looks using the OpenAI Python SDK:

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# Basic OpenAI image generation

import openai

openai.api_key = "your-api-key-here"

response = openai.images.generate(
  model="dall-e-3",  # optional; defaults depending on account    
  prompt="A lively coffee shop with laptops and people working",    
  n=1,    
  size="1024x1024"
)

image_url = response.data[0].url

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Simple enough...until you scale.

The hidden complexity of scaling gen AI jobs

When moving from generating a handful of images to hundreds or thousands or using non-standard configurations, simple SaaS platforms like OpenAI APIs won't do anymore. You’ll start to encounter bottlenecks around performance, resource management, throughput, and customization. And that leads most teams to a familiar crossroads.

Choosing between SaaS APIs and building it yourself

The limits of SaaS APIs

Services like OpenAI's and StabilityAI's API offer the simplest path to image generation, but come with significant limitations:

• Limited customization – You're locked into supported models and can't bring your own fine-tuned or custom model variants
• Pricing constraints – Per-image pricing (often $0.02-$0.08 per image) can quickly become unsustainable as volume grows
• Rate limits – APIs often enforce throttling, which restricts how many requests you can run in parallel
• Vendor lock-in – You're dependent on your provider's uptime, pricing, and roadmap
• Latency variability – Unpredictable performance during high-demand periods

The complexity of full custom infrastructure

On the other end of the spectrum is full custom infrastructure. This path gives you full control over the models, hardware, and orchestration, but it comes with its own list of tradeoffs:

• High setup complexity – Standing up GPU infrastructure, configuring autoscaling, and implementing orchestration layers requires deep DevOps and MLops experience.
• Significant upfront costs – Purchasing or renting high-performance machines adds up quickly, especially if you need flexibility.
• Ongoing maintenance – From OS patching to GPU driver updates to monitoring, you’ll own the full stack.
• Custom pipelines – You’ll likely need to build queueing, load balancing, retries, and fault tolerance into your system.
• Resource inefficiency – It’s easy to underutilize resources or overprovision in an attempt to stay ahead of load.
• Slow deployment cycles – Getting to production readiness can take months for small teams.

For large-scale teams with dedicated infrastructure engineers, this may be worth the investment. For everyone else, it’s usually a distraction.

Scaling smarter with ByteNite

ByteNite offers a third path, a platform designed to give teams the flexibility of custom infrastructure without the overhead of managing it. It’s a serverless container environment purpose-built for compute-intensive workloads like image generation.

No infrastructure, just jobs

ByteNite takes care of provisioning, scaling, and teardown of compute resources so you can stay focused on writing apps and processing data:

• On-demand scaling – Compute is spun up when needed and released after your job finishes.
• Per-job configuration – Define exactly how much CPU, memory, and (soon) GPU your job needs.
• Zero infrastructure management – No clusters to set up, scale, or monitor.

Use your own models and pipelines

ByteNite gives you full control over your code and model choices:

• Custom models – Use any model that fits your resource envelope, including fine-tuned variants or entirely custom inference stacks.
• Direct diffusers support – Leverage the Hugging Face diffusers library without worrying about infrastructure.
• Pipeline flexibility – Easily build batch processing pipelines, multi-stage workflows, or multi-prompt generation flows.

Built-in parallelism

Scaling across multiple prompts or generating multiple images per prompt is built into the platform:

• Fan-out support – ByteNite’s partitioners help you break a job into many parallel tasks.
• Stateless tasks – Each task runs independently, no shared memory or coordination required.
• Efficient distribution – Tasks are distributed across optimized, pre-warmed infrastructure to reduce cold start delays.

Image generation with Stable Diffusion on ByteNite

Let's explore how to implement Stable Diffusion image generation on ByteNite, allowing you to generate multiple images from the same prompt, simultaneously.

How it works

• You define a Partitioner that fans out multiple parallel tasks
• You create a PyTorch-based App that runs Stable Diffusion inference
• Generated images are saved to a temporary bucket for retrieval

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Here's a glimpse into the Stable Diffusion App implementation:

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def generate_image(prompt, output_path):
  # Extract the prompt from params
  print(f"Generating image for prompt: {prompt}")

  # Log the number of available CPU cores
  num_threads = os.cpu_count() or 1
  print(f"Available CPU cores: {num_threads}")

  # Determine the appropriate data type for CPU execution
  dtype = torch.bfloat16 if torch.has_mps else torch.float32

  # Load Stable Diffusion pipeline with CPU-compatible settings
  pipeline = StableDiffusionPipeline.from_pretrained(
      "runwayml/stable-diffusion-v1-5", 
      torch_dtype=dtype
   )
   pipeline.to("cpu")

  # Enable PyTorch CPU optimizations
  torch.set_float32_matmul_precision("high")

  # Ensure PyTorch uses all available CPU cores
  torch.set_num_threads(num_threads)
  torch.set_num_interop_threads(num_threads)

  # Run inference in no_grad mode
   with torch.inference_mode():
      print("Inference started")
      image = pipeline(prompt).images[0]

  # Save the output image
  image.save(output_path)

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After setting up your app and partitioner, launching a job is as simple as sending a POST request to our "Create a new job" endpoint with this request body:

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{
  "templateID": "img-gen-fiffusers-template",
  "dataSource": {
    "dataSourceDescriptor": "bypass"
  },
  "dataDestination": {
    "dataSourceDescriptor": "bucket"
  },
  "params": {
    "partitioner": {
      "num_replicas": 5
    },
    "app": {
      "prompt": "A peaceful sunset over the ocean, in a photorealistic style, with rich detail and vibrant lighting."
    }
  }
}

This job will generate 5 independent variations of the same prompt in parallel, without any manual infrastructure setup.

📗 Read Tutorial: Image Generation w/ Stable Diffusion - ByteNite Docs

The full tutorial shows you how to:

1. Set up a Stable Diffusion app with proper resource requirements (16 CPU cores, 32GB RAM)
2. Create and configure a fan-out partitioner for parallel processing
3. Launch jobs that generate multiple images simultaneously
4. Store results directly in a bucket

Check out the rest of our docs if you're interested in exploring the many building capabilities offered by ByteNite.

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