Forge vs AUTOMATIC1111 - Power-User Comparison (2026)
You have a working A1111 install. You've heard Forge is faster. You want the decision made on data, not Discord hype. This page gives you that: measured speed differences by GPU tier, real VRAM behavior with SDXL, extension compatibility as of the Gradio 4 era, and the exact migration steps to move without losing your workflow. Both repos share Gradio heritage - same tabs, same checkpoint dropdown, same 127.0.0.1:7860. The difference is under the hood: Forge rewires the UNet pipeline for aggressive memory management and ships ControlNet built-in. A1111 stays on the conservative path with 162K GitHub stars of community extensions behind it. We'll tell you which to pick and when staying on A1111 is the rational call, not the tribal one.
The Quick Answer
Key Takeaway - May 2026
- New installs: Use Forge. It's the actively maintained fork with native Flux/SD3 support and 30-45% speed gains on 8 GB cards.
- Existing A1111 installs: Migrate only when a specific pain point justifies it - OOM errors, missing Flux support, or a dead extension.
- High-end GPUs (24 GB): The speed difference shrinks to 3-6%. Migrate for features, not performance.
- Or use LocalForge AI for Forge pre-configured with zero CUDA matching.
Feature Comparison
| Category | Forge (July 2025 branch) | AUTOMATIC1111 v1.10.1 (Feb 2025) |
|---|---|---|
| GitHub stars | ~12.4K | ~162K |
| Last significant update | July 2025 (Gradio 4 + Flux GGUF) | v1.10.0 July 2024 (SD3 support) |
| Speed gain (8 GB VRAM) | 30-45% faster | Baseline |
| Speed gain (6 GB VRAM) | 60-75% faster | Baseline |
| Speed gain (24 GB VRAM) | 3-6% faster | Baseline |
| SDXL max resolution (8 GB) | Up to 6553x6553 with offload | ~1536x1536 before OOM |
| Batch size (8 GB, SDXL) | 4-6x larger | Baseline |
| Flux support | Native (NF4, GGUF Q8/Q5/Q4) | None |
| SD3 support | Yes | Yes (v1.10.0+) |
| ControlNet | Built-in, no extension needed | Extension (sd-webui-controlnet) |
| Extension compatibility | ~70% of A1111 extensions | Full ecosystem (~300+ extensions) |
| Gradio version | 4.39+ | 3.x (stable, no canvas features) |
| UI canvas/inpainting | Pressure-sensitive brush, zoom, alpha | Basic inpaint mask |
| Memory management | Dynamic UNet offload, NeverOOM | Manual flags (--medvram, --lowvram) |
Migration Steps
Step 1 - Inventory Your Dependencies
Open your A1111 extensions folder. List what you actually use - not what you installed eighteen months ago. Check each against Forge's compatibility discussion (GitHub #1754). Known broken: some legacy LoRA trainers, old Deforum versions, and soft inpainting behaves differently (blurrier results). Known working: adetailer, prompt expansion, tagger, most LoRA/LyCORIS loaders.
Step 2 - Snapshot Your Current State
Pin your A1111 commit hash: git log -1 --format="%H". Zip your config.json, ui-config.json, and styles.csv. Your models/ directory stays untouched - Forge reads the same .safetensors files. Don't delete anything yet.
Step 3 - Clone Forge to a Separate Directory
Never install Forge on top of A1111. Clone lllyasviel/stable-diffusion-webui-forge to a new folder. Point --ckpt-dir and --lora-dir at your existing model paths via launch args or symlinks. Pin your Forge commit hash before you upgrade mid-project - git log -1 --format="%H" goes in your notes.
Step 4 - Match Your CUDA Bundle
Forge's one-click packages ship paired CUDA/PyTorch builds (latest binary: February 2025). For RTX 30/40 series, use the CUDA 12.1 + PyTorch 2.1 bundle. Check your NVIDIA driver version first: nvidia-smi shows the max supported CUDA. If you're building from source, match the PyTorch wheel to your driver's CUDA ceiling.
Step 5 - Configure Performance Flags
Forge's GPU Weight slider defaults to 100% - this keeps models in VRAM. On 8 GB cards, set it to 30-50% (counterintuitively lower = faster, because it frees VRAM for computation). Key launch flags:
--cuda-stream- 15-25% speedup on RTX 30XX/40XX, slightly riskier--pin-shared-memory- additional 20%+ when paired with cuda-stream--always-offload-from-vram- slower but lets 6 GB cards run SDXL
Don't stack all flags at once. Add one, benchmark five generations, then add the next.
Step 6 - Validate With a Controlled Benchmark
Lock these variables: prompt, seed, resolution (1024x1024 for SDXL), steps (20), sampler (Euler a). Generate five images on A1111, five on Forge. Compare wall time with a stopwatch or time prefix. Check peak VRAM in Task Manager or nvidia-smi. Expected result on RTX 3060 12 GB: Forge ~24s vs A1111 ~35s for SDXL 1024x1024 at 20 steps.
Step 7 - Migrate Extensions One at a Time
Install your first extension, restart Forge, generate one image. If it works, add the next. If it breaks, check the Forge extension replacement list (GitHub #1754) for a fork. ControlNet is already built-in - don't install the extension on top of it or you'll get conflicts.
Troubleshooting
- Forge is slower than A1111: Check the GPU Weight slider. At 100% on an 8 GB card, Forge thrashes between RAM and VRAM. Drop it to 30-50%.
- Extensions crash on launch: Most failures are Gradio 3 vs 4 incompatibilities. Look for a
-forgefork of the extension or check if the author merged Gradio 4 support. - Double VAE load producing weird contrast: Forge loads the checkpoint's baked VAE by default. If you also select a VAE in the UI dropdown, they conflict. Set UI VAE to "None" unless you specifically need an external VAE.
- CUDA out of memory despite "NeverOOM": NeverOOM works by offloading to RAM, which means slower generation - not infinite VRAM. If you need speed, reduce resolution or enable
--always-offload-from-vramand accept the tradeoff. - Soft inpainting looks blurry: Known issue. Forge's inpainting pipeline handles denoising differently. For precision inpainting work, keep an A1111 install available or use the Gradio 4 canvas brush with manual masking.
Who Should Use What
- Pick Forge if: You're starting fresh, running SDXL/Flux on 6-12 GB VRAM, or hitting OOM errors on A1111. The speed gains are real and the migration cost is one afternoon.
- Stay on A1111 if: You depend on a specific unmaintained extension that breaks under Forge, your org scripts against a pinned A1111 commit, or you run soft inpainting workflows that need pixel-accurate results.
- Consider reForge if: You want Forge's performance but need bleeding-edge patches the main repo hasn't merged. Panchovix/stable-diffusion-webui-reForge tracks a "Forge2" branch with additional fixes.
- Skip both and use ComfyUI if: You want node-based workflows, maximum Flux flexibility, or you're building automation pipelines that outgrow the Gradio tab model.
Bottom Line
Forge is A1111's successor for performance-sensitive work. The benchmarks are real: 30-45% faster on mid-range GPUs, native Flux support, built-in ControlNet, and dynamic VRAM management that makes 8 GB cards usable for SDXL. The tradeoff is ~30% of A1111 extensions don't work yet and soft inpainting regresses. For new installs in 2026, start with Forge. For existing A1111 setups, migrate when a specific problem justifies the afternoon of work - not because someone on Reddit said so.