Forge Neo vs ComfyUI in 2026
I've been running both of these tools daily for the past six months, and I still can't pick just one. Forge Neo gives me that immediate tactile feedback - sliders, buttons, instant results. ComfyUI gives me the control freak's dream - every single operation visible, saveable, shareable as a JSON file I can commit to git.
If you're trying to pick between them for Flux workflows in 2026, here's my honest experience: you'll probably end up installing both. But this page will help you figure out which one deserves your main dock icon - the one you open first when inspiration hits at midnight.
Both run locally, both handle NSFW content without cloud gatekeepers (your ethics, your licenses, your responsibility). The real question is whether you think in tabs or graphs.
The Quick Answer
Forge Neo for speed. ComfyUI for control. That's the one-line version. If you want to generate 50 variants of an idea in an evening, Forge Neo's tab workflow gets you there faster. If you want to build a pipeline you'll reuse for months, ComfyUI's graph architecture pays for itself in reproducibility.
Most enthusiasts I know (myself included) run both: Forge for ideation, Comfy for delivery. If you can only pick one, pick based on what you do more of. And if Forge's install process sounds tedious, LocalForge AI packages it without the Python dance - same engine, less friction.
What Makes Forge Neo Exciting Right Now
I'll be honest - the Neo branch has been shipping features at a pace that keeps pulling me back to the Gradio UI. Here's what landed as of February 2026:
- Flux.2-Klein support (4B and 9B variants) - lighter Flux models that run on smaller GPUs
- Flux Kontext - multi-image input via ImageStitch. I've been using this for consistent character work and it's genuinely useful
- Nunchaku SVDQ acceleration for Flux-dev - measurable speed improvement
- Mixed precision options: fp4mixed, fp8mixed, mxfp8, nvfp4, fp8_scaled. My 12 GB card runs models I couldn't touch six months ago
- Model zoo: Anima, Qwen-Image, Z-Image, Wan 2.2, Lumina, Chroma1-HD all work natively
- Faster installs via the
uvpackage manager - finally
The experience of clicking a tab, picking a model, tweaking a slider, and hitting Generate is still unmatched for rapid iteration. I can A/B test LoRA weights across 20 generations in the time it takes to wire half that in ComfyUI.
What Makes ComfyUI Irreplaceable
But then I need to ship something reproducible - and nothing touches ComfyUI for that:
- JSON workflows are artifacts. Same file = same graph = same output. I version-control my production workflows alongside code. When a client says "match what you sent in March," I checkout that commit and run it.
- Day-zero model support. New architectures hit ComfyUI as custom nodes within hours of release. Chroma, Wan 2.2, HunyuanVideo 1.5 - all available in Comfy before any tab-based UI catches up.
- Video generation. Multi-model motion pipelines that chain generation, interpolation, and upscaling. Tab UIs physically can't represent these workflows.
- Efficiency on complex tasks. 25% faster on ControlNet + upscale chains because Comfy only re-executes changed branches. My Forge equivalent recomputes everything.
- 1,500+ custom nodes. Whatever weird thing you need - someone already built a node for it.
The learning curve is real (I spent two weeks confused before it clicked), but once you internalize graph thinking, you can't go back to not seeing the dataflow.
Side-by-Side Comparison
| Dimension | Forge Neo | ComfyUI |
|---|---|---|
| Primary artifact | Gradio session + PNG metadata | JSON graph (version-controllable) |
| Iteration speed | Fast - click, tweak, generate | Slower setup, but re-runs are instant |
| Learning curve | 2-3 hours if you know WebUI | 2-4 weeks to become productive |
| Flux.2 support | Klein, Kontext, dev (Nunchaku) | All variants via nodes (day-zero) |
| Video workflows | Basic (Wan 2.2 via Neo) | Excellent (native multi-model chains) |
| Extension model | Python extensions (A1111 ecosystem) | Custom nodes + Manager |
| Reproducibility | Good (save PNG metadata) | Excellent (JSON = the workflow) |
| New model support | Fast (days-weeks after release) | Fastest (hours after release) |
| Mixed precision | Native (fp4/fp8/nvfp4) | Via GGUF nodes |
| API/automation | WebUI REST endpoints | Graph-native HTTP API |
| Community | Large (A1111 heritage) | Large and growing fast |
My Actual Workflow (What Two Tools Look Like in Practice)
Monday-Thursday (Forge Neo): Client sends a brief. I open Forge, load the model, start generating variants. Slider sweep on CFG, quick LoRA weight tests, send options within the hour. This is where Forge's speed culture shines - I'm not building infrastructure, I'm making pictures.
Friday (ComfyUI): Client approves a direction. I build (or load) the production pipeline in Comfy - exact ControlNet stack, specific upscale chain, inpaint nodes for fixes. Save the JSON. This graph is now the deliverable's source of truth. If they want revisions in three months, I run the same file.
Weekend (both): Experimenting. New model drops? Comfy first (faster to wire a test). Want to vibe with 100 generations? Forge. This is the fun part.
Performance: What I've Actually Measured
On my RTX 4070 Ti (12 GB):
- SDXL 1024 txt2img: Forge ~5.5 sec, ComfyUI ~7.8 sec (simple graph)
- SDXL + ControlNet + hires fix: Forge ~18 sec, ComfyUI ~14 sec (Comfy wins on complex chains)
- Flux-dev FP8 512: Forge ~12 sec (Nunchaku), ComfyUI ~15 sec (standard nodes)
Forge wins simple generation. ComfyUI wins complex pipelines. Neither is "faster" - it depends on what you're doing.
VRAM: Same Models, Same Limits
Your GPU doesn't care which UI renders the buttons. Same model = same VRAM:
- 8 GB: SDXL works in both with attention slicing. Flux needs GGUF Q4 (~9 GB) - Comfy's nodes handle this cleanly; Forge Neo's mixed precision helps too.
- 12 GB: Comfortable territory. Flux FP8 works. Both UIs shine here.
- 16-24 GB: Freedom. Pick by workflow, not survival.
The difference: Comfy makes VRAM usage visible in the graph (you can see duplicate loaders). Forge hides it until your task manager screams.
Flux Specifically: Where Each Tool Lands
Flux workflows are where this choice gets interesting:
Forge Neo's Flux story: Load model, pick precision, generate. Flux.2-Klein (4B/9B) runs great. Kontext for multi-image is a genuine workflow improvement for character consistency. Nunchaku acceleration is measurable. If your Flux work is "generate images with Flux," Neo handles it beautifully.
ComfyUI's Flux story: Wire the text encoders, VAE, model loader, sampler - all visible. More setup, but you see exactly what's happening. When something breaks, you know which node failed. Custom quantization paths, experimental samplers, and bleeding-edge GGUF options all land as nodes first.
My take: simple Flux generation = Forge. Complex Flux pipelines (ControlNet stacking, multi-pass, video) = Comfy.
The Extension vs Node Ecosystem
Forge Neo extensions are Python scripts. The A1111 ecosystem is massive - ControlNet variants, prompt tools, upscalers, aesthetic scorers. The downside is torch bump fragility. Every CUDA update risks breaking something.
ComfyUI custom nodes are also Python, but scoped differently. The Manager makes install easy. The downside is name collisions (three packs all shipping their own KSampler variant) and the "works on my machine" problem with shared graphs.
Both ecosystems are vibrant. Forge's is older and broader. ComfyUI's is newer and moves faster on bleeding-edge model support.
When Forge Neo Wins Outright
- You're iterating fast and don't need a repeatable pipeline
- You live in extensions that haven't been ported to Comfy nodes yet
- You want Flux Kontext for multi-image character work
- You hate graphs and love clicking buttons
- A client needs options in an hour, not a pipeline in a week
When ComfyUI Wins Outright
- You need provable reproducibility (same JSON = same output)
- You're building video generation workflows
- You want day-zero support for every new model architecture
- You need complex multi-model chains (generation + ControlNet + upscale + inpaint)
- Your automation speaks graphs and HTTP API calls
The "Both" Strategy (What Most Enthusiasts Actually Do)
Share one models directory between both installs. Symlink if your OS allows it. Never duplicate 20+ GB checkpoints.
Use Forge for:
- Fast ideation sessions
- LoRA weight A/B testing
- Quick client previews
- Flux Kontext character consistency
Use Comfy for:
- Production delivery pipelines
- Video generation workflows
- Anything you want to reproduce later
- Complex ControlNet + inpaint stacks
Document the handoff point clearly. When an idea graduates from "Forge experiment" to "Comfy pipeline," write down the settings that produced the approved version.
Who Should Use What
- Forge Neo: Your daily driver if you value speed, tactile iteration, and the WebUI extension ecosystem. Best for rapid ideation and Flux Kontext workflows.
- ComfyUI: Your delivery tool if you value reproducibility, video pipelines, and day-zero model support. Best for production work and complex chains.
- Both: What most enthusiasts end up doing within a month. Share models, split workflows by purpose.
- LocalForge AI: The packaged Forge option if Python setup steals your creating time. Same engine, less friction.
Bottom Line
You'll install both. The question is which one opens when you double-click your "generate art" shortcut. If your work is mostly fast iteration with occasional complex pipelines, make Forge your default. If your work is mostly repeatable pipelines with occasional fast experiments, make Comfy your default. Either way, you're going to have a great time.