Stable Diffusion and FLUX: My Local AI Image Setup

In short: Stable Diffusion and FLUX are the open-source alternative to cloud image generators like Midjourney and DALL-E 3. You run them on your own hardware, so your data stays local and you keep full control over the model. With ComfyUI, LoRA and ControlNet, they handle production image generation entirely off-cloud.

I installed my first Stable Diffusion instance on my own machine back in November 2022, just after the public release of SD 1.4. Since then I've lived through every version — SD 1.5, SDXL, SD3 — and then migrated to Black Forest Labs' FLUX.1 [dev] in September 2024. Today I generate roughly 200 images a week locally on an RTX 4070 Ti, both for my own articles and for clients who want to keep their visuals off-cloud. Here's what I've learned along the way.

Stable Diffusion and FLUX are the open source alternative to cloud services like Midjourney and DALL-E 3. Their fundamental difference: you run them on your own hardware, your data stays local, and you have total control over the model.

Stable Diffusion vs FLUX

These two ecosystems coexist in 2026:

Stable Diffusion (SDXL, SD3):

• The longest history and the largest community
• A massive ecosystem of fine-tuned models (Civitai)
• Very mature LoRA, ControlNet, inpainting
• Runs on a GPU with 6-8 GB VRAM minimum

FLUX (Black Forest Labs):

• Released in 2024, image quality superior to SDXL
• Better prompt comprehension
• Fewer community fine-tunes (a growing ecosystem)
• FLUX.1 Schnell (fast), Dev (balanced), Pro (cloud) versions

Available Interfaces

Installation is made easier by graphical front-ends:

• ComfyUI: the most powerful, node-based workflow
• Automatic1111 (A1111): the historical one, simpler
• Forge: an A1111 fork optimized for performance
• InvokeAI: clean interface, good UX

Use Cases Where Open Source Wins

You should seriously consider Stable Diffusion or FLUX if:

• Absolute confidentiality: product images, sensitive internal visuals
• Massive volume: a local GPU access conditions you can amortize vs cloud access plan
• Customization: fine-tuning a model on your own visuals (brand identity, recurring characters)
• Pipeline integration: a local API inside your own applications

For everyday use cases with no confidentiality constraint, DALL-E 3 or Adobe Firefly are simpler.

Tying It In With Other Tools

A complete workflow can combine:

• FLUX for image generation
• Whisper or ElevenLabs for narration
• SurferSEO for the SEO copy

My Current Setup and What It Actually access conditions

For the curious, here's my configuration: an RTX 4070 Ti 12 GB (bought second-hand in 2024), a Ryzen 7 5800X, 32 GB of RAM, and a 2 TB NVMe SSD dedicated to models. The total access conditions of the workstation is reasonable next to two years of stacked cloud access plan for the same volume.

On this config, FLUX.1 [dev] generates a 1024×1024 image in roughly 25 to 30 seconds with a standard sampler. SDXL does it in 8 seconds. Latency barely matters for my workflow — I kick off a batch of 20 images and come back ten minutes later.

The Fine-Tuning Trap: Don't Start There

A lot of beginners want to immediately fine-tune a model on their own images. That's the mistake I made in 2023. Six months later I had a mediocre model that was overfitting on my data.

The right progression: (1) master standard prompting, (2) explore the LoRAs already available on Civitai — there are more than 100,000 of them — (3) test ControlNet for composition control, (4) and only then consider a fine-tune or a custom LoRA on a clean dataset.

ControlNet: The Secret Weapon for Composition

ControlNet is what turns Stable Diffusion from a random generator into a production tool. With ControlNet OpenPose I can impose a precise pose on a character. With ControlNet Canny I can reproduce the composition of an existing image while changing the style. With ControlNet Depth I can rebuild a scene from a depth map.

For professional visuals that demand consistency from one image to the next — product series, storyboards, mockups — ControlNet is what makes open source competitive with Midjourney.

My Comparative Take After Two Years

If I had to sum it up in one line: for pure photographic quality on a single image, Midjourney is still ahead. For volume, control, and confidentiality, FLUX running locally is unbeatable. For artistic style, the SDXL Civitai ecosystem offers a variety you won't find anywhere else.

How I actually use them today: FLUX for the visuals on technical articles (where precision matters), SDXL with an artistic LoRA for conceptual illustrations, and Midjourney kept on the side for the hero images that need a cinematic render.

Compliance and Licensing: Check First

Stable Diffusion and FLUX aren't exactly open source in the GPL sense. They're models with specific licenses:

• Stable Diffusion XL: CreativeML Open RAIL++-M, which permits commercial use with restrictions on content
• FLUX.1 [dev]: non-commercial license by default. For commercial use, that means FLUX.1 [pro] on cloud or a dedicated commercial license

For my clients, I systematically check this point up front. The license determines whether you're allowed to monetize the images you generate.

Our Read for Trust-Vault

The open source nature means the Trust Score applies differently: there's no responsible publisher in the traditional sense. The responsibility falls on the organization that deploys and uses the model. That's both the strength (total control) and the weakness (you handle compliance yourself).

For image generation tools, see our Image Generation category.