Stable Diffusion vs Flux in 2026 — Which Model Should You Use?
Complete comparison of SD 1.5, SDXL, SD 3.5, Flux 1, and Flux 2 for local image generation. VRAM requirements, quality, ecosystem maturity, and which model to choose based on your hardware budget.
The lineage from Stable Diffusion to Flux represents four years of rapid progress in local image generation. Each generation brought quality improvements but also higher hardware requirements. This guide maps the full evolution and helps you choose the right model for your GPU and workflow.
The Evolution: CompVis to Black Forest Labs
The history of open-source image generation follows a clear thread:
2022 — Stable Diffusion 1.5 (CompVis / Stability AI): The model that launched local image generation. A 0.86B parameter UNet architecture that could run on consumer GPUs with just 4GB VRAM. It democratized AI art overnight.
2023 — Stable Diffusion XL (Stability AI): A major leap to 2.6B parameters with a dual UNet architecture and dual text encoders (CLIP-G + CLIP-L). Dramatically better quality and composition, needing 8GB VRAM.
2024 — Stable Diffusion 3.5 (Stability AI): Shifted to an MMDiT (Multi-Modal Diffusion Transformer) architecture with triple text encoders (CLIP-G, CLIP-L, T5-XXL). The 2.5B parameter model produced better text and composition but required 18GB VRAM.
2024-2025 — Flux 1 (Black Forest Labs): Created by key former Stability AI researchers. A 12B parameter DiT with T5-XXL and CLIP-L encoders. Set a new quality bar for open models at 33GB VRAM (FP16) or 17GB (FP8).
2025-2026 — Flux 2 (Black Forest Labs): Refined the Flux architecture with improved training and better prompt adherence. Similar hardware requirements to Flux 1 with measurably better output quality.
Quality Progression
Each generation represents a visible quality improvement:
| Generation | Photorealism | Text Rendering | Composition | Prompt Following |
|---|---|---|---|---|
| SD 1.5 | Fair | Poor | Fair | Fair |
| SDXL | Good | Poor | Good | Good |
| SD 3.5 | Very Good | Good | Very Good | Very Good |
| Flux 1 | Excellent | Excellent | Excellent | Excellent |
| Flux 2 | Excellent | Excellent | Excellent | Excellent |
The biggest single jump was from SDXL to Flux. SD 3.5 sits in between — better than SDXL but not as refined as Flux in most benchmarks.
VRAM Requirements Compared
This is the most important practical consideration. More quality costs more memory:
| Model | Parameters | VRAM (FP16) | VRAM (Optimized) | Minimum GPU |
|---|---|---|---|---|
| SD 1.5 | 0.86B | 4 GB | 3 GB | GTX 1060 6GB |
| SDXL | 2.6B | 8 GB | 7 GB | RTX 3060 8GB |
| SD 3.5 Large | 2.5B | 18 GB | 18 GB | RTX 4090 |
| Flux 1/2 Dev | 12B | 33 GB | 12 GB (GGUF Q4) | RTX 4060 Ti 16GB |
| Flux 1/2 Schnell | 12B | 33 GB | 12 GB (GGUF Q4) | RTX 4060 Ti 16GB |
Note that SD 3.5 at 18GB has no widely-used quantized versions, making it awkward for GPUs between 12GB and 24GB. Flux GGUF quantization via city96 on HuggingFace is well-supported and brings the requirement down to 12GB with acceptable quality loss.
Ecosystem Maturity
Ecosystem depth — LoRAs, ControlNets, fine-tunes, and community tools — often matters more than raw model quality:
| Model | LoRAs (CivitAI) | ControlNets | Community Fine-Tunes | ComfyUI Support |
|---|---|---|---|---|
| SD 1.5 | 10,000+ | 8+ types | Hundreds | Excellent |
| SDXL | 5,000+ | 5+ types | Dozens | Excellent |
| SD 3.5 | ~50 | 1 type | Very few | Good |
| Flux 1 | ~500 | 3 types | Growing | Excellent |
| Flux 2 | ~200 | 3 types | Growing | Excellent |
SD 1.5 and SDXL have ecosystems that took years to build. Flux is growing quickly but cannot match that depth yet. SD 3.5 never gained meaningful community traction.
For anime generation, SDXL fine-tunes like Animagine XL 3.1 and Pony Diffusion V6 XL remain the gold standard. For photorealism and general-purpose generation, Flux has taken the lead.
Which Model to Choose Based on Your Hardware
4-6 GB VRAM (GTX 1060, RTX 3050): Your only realistic option is SD 1.5. It runs well on minimal hardware and has the deepest ecosystem. Consider anime fine-tunes or specialized models for your use case.
8-12 GB VRAM (RTX 3060, RTX 3070, RTX 4060): SDXL is the sweet spot. Best balance of quality, speed, and ecosystem. You get access to thousands of LoRAs and reliable ControlNet support.
12-16 GB VRAM (RTX 4060 Ti 16GB, RTX 4070 Ti Super): Flux with GGUF Q4 quantization becomes viable. You get significantly better quality than SDXL at the cost of a smaller ecosystem and slower generation. Consider running SDXL for LoRA-dependent workflows and Flux for quality-critical work.
24 GB or more VRAM (RTX 4090, RTX 5090, A100): Flux at FP8 is the clear winner. Fast generation, excellent quality, growing ecosystem. No reason to use older models unless you need specific SDXL LoRAs.
Speed Comparison on RTX 4090
All tests at 1024x1024 resolution with default step counts:
| Model | Steps | Time | Images per Minute |
|---|---|---|---|
| SD 1.5 | 25 | ~2.5 sec | ~24 |
| SDXL | 30 | ~4.5 sec | ~13 |
| SD 3.5 Large | 28 | ~8 sec | ~7 |
| Flux Dev (FP8) | 28 | ~12 sec | ~5 |
| Flux Schnell (FP8) | 4 | ~2 sec | ~30 |
Flux Schnell is remarkably fast due to its 4-step distilled inference. For iterative workflows where speed matters, Schnell offers the best quality-per-second ratio of any current model.
The Verdict: A Three-Tier Recommendation
For maximum quality: Use Flux 2 Dev. It produces the best images currently available from open models. Requires 17GB VRAM at FP8 or 12GB with GGUF Q4.
For the best ecosystem: Use SDXL. Thousands of LoRAs, robust ControlNet support, and mature community tooling. Requires 8GB VRAM.
For budget hardware: Use SD 1.5. Still produces good results on 4GB GPUs, especially with specialized fine-tunes and LoRAs.
SD 3.5 occupies an awkward middle ground — more VRAM than SDXL, less quality than Flux, minimal ecosystem. Unless you have a specific reason to use it, skip SD 3.5 in favor of either SDXL or Flux.
Check hardware compatibility for any of these models on our diffusion model calculator, or compare specific models in our image model browser.