FLUX Dev Image Generation on inference.sh

FLUX Dev by Pruna is a text-to-image generation model optimized for speed and cost efficiency. Available on inference.sh at $0.005 per image, it is the most affordable image generation option on the platform — roughly 16x cheaper than Gemini Flash Image and 40x cheaper than GPT Image 2 at high quality. With 52 paying users relying on it for bulk generation workloads, FLUX Dev hits a sweet spot for developers who need solid image quality at high volume without breaking the budget.

FLUX Dev is based on Black Forest Labs' FLUX architecture, optimized by Pruna for faster inference with configurable speed modes. It produces coherent, aesthetically pleasing images from text prompts with good adherence to composition instructions. While it does not offer the editing capabilities or search grounding of more expensive models, it excels at pure text-to-image generation where cost and throughput are the primary concerns.

what it does

FLUX Dev generates images from text descriptions. You provide a prompt describing the image you want, and the model returns a generated image. It supports multiple aspect ratios, configurable inference steps for quality/speed tradeoff, guidance strength for prompt adherence, and speed optimization modes that let you choose between maximum quality and fastest generation.

This is a focused tool — it does text-to-image generation well and cheaply. It does not support image editing, inpainting, or multi-image reference workflows. If you need those capabilities, look at GPT Image 2 or Gemini Flash Image. If you need volume, iteration speed, and low cost, FLUX Dev is the right choice.

key features

Extreme affordability — At $0.005 per image, you can generate 200 images for a dollar. This makes batch workflows, A/B testing, and high-volume generation economically viable in ways that premium models cannot match.

Speed modes — Three optimization levels let you trade quality for speed. The fastest mode reduces generation time significantly for workflows where rapid iteration matters more than maximum fidelity.

Configurable inference steps — Control the number of denoising steps (default 28). More steps mean higher quality but slower generation. Fewer steps give faster results with acceptable quality for many use cases.

Guidance control — The guidance parameter controls how strictly the model follows your prompt versus exploring creative interpretation. Higher guidance means literal prompt adherence; lower values give the model more creative freedom.

Flexible aspect ratios — Standard aspect ratios from 1:1 to 16:9 and portrait orientations. Generate square social posts, widescreen banners, or vertical mobile content.

Seed control — Specify a random seed for reproducible results. Generate the same image repeatedly for consistency, or use -1 for random variation.

Output format options — Choose between JPEG and WebP output with configurable quality compression (default 80).

use cases

Bulk content generation — Generate hundreds or thousands of images for content libraries, stock photo alternatives, or training data. The $0.005/image cost makes volume workflows practical.

Rapid prototyping — Iterate on visual concepts quickly and cheaply. Generate 50 variations of an idea for pennies, then use a premium model for final production assets.

A/B testing visuals — Generate multiple versions of ad creatives, thumbnails, or marketing images. Test them against each other without worrying about per-image cost.

Placeholder and mockup generation — Create realistic placeholder images for designs, wireframes, and prototypes. Better than generic stock photos, cheaper than custom photography.

Automated pipelines — Build systems that generate images programmatically at scale. Blog illustration bots, social media content generators, and dynamic creative systems all benefit from the low per-unit cost.

Seed-based consistency — Use fixed seeds to generate consistent imagery across related content. Create visual series where elements remain stable across variations.

how to run

belt CLI

Basic text-to-image:

1belt app run pruna/flux-dev --input '{"prompt": "A photorealistic close-up of raindrops on a green leaf, macro photography, shallow depth of field"}'

Widescreen banner with high guidance:

1belt app run pruna/flux-dev --input '{"prompt": "Minimal geometric abstract art, deep navy and gold, clean lines, modern gallery wall", "aspect_ratio": "16:9", "guidance": 8.0}'

Fast generation with reduced steps:

1belt app run pruna/flux-dev --input '{"prompt": "Cozy coffee shop interior, warm lighting, bookshelves, rain on the windows", "num_inference_steps": 15, "speed_mode": "fast"}'

Reproducible output with fixed seed:

1belt app run pruna/flux-dev --input '{"prompt": "Portrait of a medieval knight in ornate armor, dramatic side lighting, dark background", "seed": 42, "aspect_ratio": "2:3"}'

High-quality with maximum steps:

1belt app run pruna/flux-dev --input '{"prompt": "Underwater photograph of a coral reef at golden hour, sunbeams filtering through clear water, tropical fish", "num_inference_steps": 40, "guidance": 7.5, "image_size": 1536}'

API

1curl -X POST https://api.inference.sh/v1/apps/pruna/flux-dev/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "prompt": "Isometric view of a tiny Japanese garden with a stone lantern, koi pond, and maple tree, soft afternoon light, miniature diorama style",6    "aspect_ratio": "1:1",7    "guidance": 7.0,8    "num_inference_steps": 28,9    "output_format": "jpg",10    "output_quality": 9011  }'

input parameters

output

The app returns:

• image — URL to the generated image file hosted on inference.sh cloud storage.
• output_meta — Metadata including actual resolution, seed used (useful for reproduction), inference steps, and billing details.

pricing

$0.005 per image — flat rate regardless of resolution, aspect ratio, or number of inference steps.

Cost comparisons for 1,000 images:

• FLUX Dev: $5.00
• Gemini Flash Image (1K): $80.00
• GPT Image 2 (medium): $24.00
• GPT Image 2 (high): $210.00

This makes FLUX Dev the clear choice for any workflow where you need volume.

when to use this vs alternatives

Choose FLUX Dev when cost is your primary concern, you need bulk generation, you want fast iteration on concepts, or your workflow is pure text-to-image without editing requirements.

Choose Gemini Flash Image when you need image editing, text rendering within images, Google Search grounding, or higher baseline quality for individual hero images.

Choose GPT Image 2 when you need mask-based inpainting, flexible arbitrary dimensions, or OpenAI's distinctive aesthetic for premium assets.

Choose Qwen Image 2 when you need complex text-heavy images like infographics, slides, or document-style outputs.

FAQ

How does the quality compare to more expensive models?

FLUX Dev produces good-quality images suitable for most applications — web content, social media, mockups, and prototypes. For hero images, marketing finals, or cases where maximum fidelity matters, premium models like GPT Image 2 (high) or Gemini Flash Image at 4K will produce noticeably better results. The quality is more than sufficient for volume workflows where individual image perfection is not required.

What does the speed_mode parameter do?

Speed mode controls how aggressively the model optimizes for generation speed. Faster modes reduce inference time but may sacrifice some detail and coherence. For iteration and prototyping, fast mode is ideal. For final outputs, use the default balanced mode or increase inference steps.

Can I get reproducible results?

Yes, set the seed parameter to any fixed integer. The same seed combined with the same prompt and parameters will produce the same image every time. This is useful for creating consistent series, debugging prompts, or sharing exact generation parameters with collaborators.

What is the guidance parameter?

Guidance (also called classifier-free guidance scale) controls how literally the model follows your prompt. Values around 7.0 (the default) give a good balance. Higher values (10-15) force the model to match your description very closely but can reduce image naturalness. Lower values (3-5) give the model more creative freedom but may drift from your intent.

Does FLUX Dev support image editing or inpainting?

No. FLUX Dev is a pure text-to-image generator. It does not accept input images for editing, masks for inpainting, or reference images for style matching. If you need those capabilities, use GPT Image 2 or Gemini Flash Image instead.