AI Workflow

AI Image & Video Generation

creative

Local image and video generation with FLUX.1, SDXL, and Stable Diffusion 3. Run ComfyUI workflows with LoRAs, ControlNet, and upscaling: no cloud credits, no content filters, no rate limits. VRAM is the bottleneck: FLUX.1 at full quality needs 24GB, but Q4 quantization brings it to 8GB GPUs.

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Concurrent VRAM

13 GB

Peak VRAM

13 GB

Min Bandwidth

400 GB/s

Models

Memory

VRAM Breakdown

How the 13 GB concurrent VRAM is used.

Switched (Loaded As Needed)

These share VRAM with the largest concurrent model. Only one runs at a time.

FLUX.1 Dev(primary image generation)

13 GB

Q8_0

Stable Diffusion XL 1.0(image generation with loras)

6.5 GB

FP16

Stable Diffusion 3 Medium(fast image generation)

5 GB

FP16

Buying Priority

What matters most for this workflow

This workflow fits on surprisingly modest hardware, so the main decision is whether you want the cheapest workable setup or enough headroom to keep the experience snappy.

Practical Tradeoff

How to think about the hardware

Treat this as a workflow where convenience and control matter more than raw ROI. Local hardware still makes sense, but the win is predictable latency and ownership, not just monthly cost savings.

Return on Investment

Local vs API Costs

Typical Monthly API Cost

$60/mo

Break-Even Point

15 months

Annual Savings

~$576/yr

Based on ~1000 images/month via Midjourney ($30/mo) or DALL-E 3 API ($0.04/image = $40/mo). Local generation is unlimited once hardware is purchased. Electricity cost ~$10/mo at 4hr/day GPU usage. Mid-Range Workstation at ~$1,400. Break-even assumes moderate usage; heavy users (5000+ images/mo) break even in 3-4 months.

Hardware