Local AI Image and Video Generation in 2026: Models, Hardware, and Setup

AI image generation has matured from a novelty into a production tool. In 2026, running Stable Diffusion 3.5, Flux, and specialized models locally produces results that match or exceed cloud services like Midjourney and DALL-E 3 — with complete control over style, licensing, and privacy.

The hardware requirements have dropped significantly. FP8 quantization cuts Stable Diffusion 3.5 Large from 18GB to 11GB VRAM, making it runnable on mid-range 12GB GPUs. Flux models work at 6-16GB with quantization. NVIDIA's NVFP4 optimizations on RTX 50-series cards deliver up to 3x performance boosts. And ComfyUI has become the dominant workflow tool, with a node-based interface that makes complex pipelines accessible.

The new frontier is video generation. Open models like LTX-2 and HunyuanVideo 1.5 can generate synchronized audio+video at up to 4K resolution on consumer GPUs. It's early days — generation is slow and results are inconsistent — but the trajectory is clear.

Unlike LLMs where VRAM determines model size, image generation VRAM requirements depend on resolution, model architecture, and batch size. Here's what each tier can do:

VRAM	What You Can Run	Example GPUs
6-8 GB	SD 1.5, SDXL (512-768px), Flux Dev (quantized)	RTX 4060, RX 7600, Arc B580
10-12 GB	SDXL (1024px), SD 3.5 Medium, Flux Dev (FP8)	RTX 4070, RTX 3060 12GB
16 GB	SD 3.5 Large (FP8), Flux (full), ControlNet + IP-Adapter	RTX 5070 Ti, RTX 5080, RX 9070 XT
24 GB	Everything at full quality, large batch sizes, training LoRAs	RTX 3090, RTX 4090
32 GB	SD 3.5 Large FP16, Flux FP16, video gen, fine-tuning	RTX 5090

Performance benchmarks

Generating a 1024×1024 image with SD 3.5 Large:

• RTX 5090: ~18 seconds (FP16, no quantization needed)
• RTX 4090: ~34 seconds (FP8 quantization)
• RTX 3090: ~50 seconds (FP8 quantization)
• RTX 5070 Ti: ~55 seconds (FP8 quantization)
• RX 9070 XT: ~65 seconds (ONNX/DirectML, less optimized)

NVIDIA GPUs have a significant advantage for image generation due to CUDA acceleration in ComfyUI and the SD ecosystem. AMD support has improved through ROCm and DirectML, but expect 30-50% slower generation times at equivalent VRAM tiers.

Stable Diffusion 3.5 Large — the new standard

Stability AI's latest flagship model produces photorealistic images with excellent prompt adherence and fine detail. The "Large" variant uses a 8B parameter MMDiT architecture. At FP8 quantization, it needs 11GB VRAM — fitting on RTX 4070 and above. Full FP16 needs 18GB. Image quality rivals Midjourney v6 for photorealism and exceeds it for prompt accuracy.

Flux — the artist's choice

Black Forest Labs' Flux models are the go-to for artistic and stylized output. Flux Dev is the most popular variant — it produces images with exceptional composition and style diversity. Full FP16 needs ~24GB; FP8/NF4 quantized versions run on 8-16GB GPUs. Flux excels at creative prompts, character consistency, and artistic styles where SD 3.5 sometimes feels "too clean."

SDXL — still the most accessible

SDXL runs on 8GB GPUs, has the largest community of LoRAs, embeddings, and fine-tuned checkpoints, and generates 1024×1024 images in under 10 seconds on modern hardware. If you have limited VRAM or want access to the biggest ecosystem of custom models and styles, SDXL remains a strong choice despite being older.

Specialized models

• SD 3.5 Medium — 2.5B params, fits on 8GB GPUs, 80% of Large quality
• Kolors — excellent for anime and illustration styles
• Playground v3 — strong prompt adherence, competitive with Flux

ComfyUI is the dominant tool for local image and video generation in 2026. Its node-based interface lets you build complex pipelines — chaining models, ControlNet, IP-Adapter, upscalers, and post-processing into repeatable workflows.

Why ComfyUI over other tools

• Node-based workflow — visual graph editor where each step is a node. No coding required, but infinitely flexible. Save and share workflows as JSON files.
• Memory efficiency — ComfyUI loads and unloads models intelligently, letting you use multiple models in one pipeline without running out of VRAM.
• NVFP4/FP8 support — on RTX 50-series, ComfyUI leverages hardware FP4/FP8 for up to 3x speedups with minimal quality loss.
• Video generation — ComfyUI is the primary tool for running LTX-2, AnimateDiff, and other video generation models locally.
• Massive community — thousands of custom nodes for everything from face swapping to 3D generation to batch processing.

Getting started

Clone ComfyUI from GitHub, install PyTorch with CUDA support, and drop model checkpoints into the models/ directory. Launch the server and open the web UI. Start with a simple text-to-image workflow, then explore community workflow packs for advanced pipelines. The ComfyUI Manager extension auto-installs required custom nodes from shared workflows.

Local AI video generation went from impossible to practical in early 2026. Several open models now produce usable video on consumer hardware:

LTX-2 — the breakthrough model

Released January 2026, LTX-2 from Lightricks is the first open model with synchronized audio and video generation. Key specs: 19B parameters, native 4K at 50 FPS, up to 20 seconds of video. It runs on RTX 4090/5090 hardware through ComfyUI. The quality is cinematic — significantly ahead of older models like AnimateDiff.

HunyuanVideo 1.5 — runs on 14GB VRAM

Tencent's HunyuanVideo 1.5 uses 8.3B parameters and runs on as little as 14GB VRAM with model offloading. Quality is a step below LTX-2, but the lower hardware requirements make it the most accessible video generation model. Generates 5-10 second clips at 720p.

Other video models

• Wan 2.2 — cinematic quality using MoE diffusion, needs 24GB+
• CogVideoX — runs on 12GB VRAM, 6-second clips at 480p
• AnimateDiff — lightweight, runs on 8GB, good for short animations and loops

Hardware tiers for video generation

VRAM	What You Can Generate
8 GB	AnimateDiff short loops, Wan 2.1 (small)
12 GB	CogVideoX 480p, LTX-Video (basic)
16 GB	HunyuanVideo 1.5 (offloaded), SVD 576p
24 GB	LTX-2 (optimized), Wan 2.2, most models
32 GB	LTX-2 (full quality 4K), all current models

Video generation is the most VRAM-hungry local AI workload. If you're serious about it, an RTX 4090 (24GB) is the minimum practical investment, and the RTX 5090 (32GB) with FP4 support is the ideal choice in 2026.

LoRA (Low-Rank Adaptation) lets you fine-tune image models on your own images — teaching the model a specific art style, character, or product look with as few as 15-30 training images. The resulting LoRA file is small (typically 10-200MB) and applies on top of any base model.

Hardware requirements for LoRA training

• Minimum: 12GB VRAM for SDXL LoRAs, 16GB for SD 3.5 / Flux LoRAs
• Recommended: 24GB (RTX 3090/4090) for comfortable training with larger batch sizes
• Training time: 1,500-3,000 steps typically takes 30-90 minutes on an RTX 4090

Tools for LoRA training

Kohya_ss remains the most popular training tool with its GUI interface. SimpleTuner is gaining traction for Flux and SD 3.5 LoRAs with simpler configuration. Both run locally and produce LoRA files compatible with ComfyUI, Automatic1111, and other tools.

LoRA training is one area where NVIDIA GPUs are essentially mandatory — the training ecosystem relies heavily on CUDA, bitsandbytes, and Flash Attention, none of which have mature AMD or Apple Silicon support.