{"id":7263,"date":"2026-05-28T17:26:03","date_gmt":"2026-05-28T09:26:03","guid":{"rendered":"https:\/\/crepal.ai\/blog\/?p=7263"},"modified":"2026-05-28T17:26:06","modified_gmt":"2026-05-28T09:26:06","slug":"sulphur-2-vs-wan","status":"publish","type":"post","link":"https:\/\/crepal.ai\/blog\/aivideo\/sulphur-2-vs-wan\/","title":{"rendered":"Sulphur 2 vs Wan: Which Model Fits Creators?"},"content":{"rendered":"\n

I’m Leo. I don’t write polished reviews. I run real projects through tools and tell you whether they’re worth your money. Someone in a Discord I’m in put it well last week: “I don’t care which model wins benchmarks. I care which one I can actually ship content with.”<\/p>\n\n\n\n

That’s the frame I’m using here. This sulphur 2 vs wan comparison isn’t about who has the better paper \u2014 it’s about which model fits a creator’s actual working conditions. I’ve run both. Here’s what I found.<\/p>\n\n\n\n

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What Each Model Is<\/h2>\n\n\n\n

Sulphur 2<\/strong> is a community finetune built on top of LTX Video 2.3<\/a>, Lightricks’ open-weight video diffusion architecture. It’s available as a .safetensors<\/code> checkpoint on the Sulphur 2 Hugging Face page<\/a>. The main focus of the finetune \u2014 based on what the community has observed \u2014 is improved visual quality on human subjects: skin tones, facial movement, that zone where most AI video still looks slightly wrong.<\/p>\n\n\n\n

It is not a from-scratch trained model, and the training methodology hasn’t been formally documented yet. Worth keeping in mind when you see people calling it a “base model.”<\/p>\n\n\n\n

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Wan<\/strong> \u2014 specifically Wan 2.1 \u2014 is a video generation model from Alibaba’s research team, released with open weights on Hugging Face<\/a>. It’s a heavier architecture than LTX-based models, with a 14B parameter variant that’s become the default reference point in community comparisons. It has stronger motion quality on complex scenes and a more complete public release with documented training details.<\/p>\n\n\n\n

Both run locally. Both are free to use. The differences are in how they perform and what hardware they need to do it.<\/p>\n\n\n\n

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Key Differences<\/h2>\n\n\n\n
<\/td>Sulphur 2<\/td>Wan 2.1<\/td><\/tr>
Base architecture<\/td>LTX Video 2.3 (Lightricks)<\/td>Wan 2.1 (Alibaba)<\/td><\/tr>
Release type<\/td>Community finetune<\/td>Official open-weight release<\/td><\/tr>
VRAM minimum<\/td>~12 GB<\/td>~16 GB (14B), ~8 GB (1.3B)<\/td><\/tr>
Inference speed<\/td>Faster<\/td>Slower on 14B<\/td><\/tr>
Human subject quality<\/td>Strong<\/td>Moderate<\/td><\/tr>
Complex motion<\/td>Limited<\/td>Stronger<\/td><\/tr>
Documentation<\/td>Thin<\/td>More complete<\/td><\/tr>
Image-to-video<\/td>Supported<\/td>Supported<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This table is based on my own runs and community comparisons as of early 2026. Treat it as directional, not final \u2014 both models are actively being updated.<\/p>\n\n\n\n

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Quality<\/h2>\n\n\n\n

This is where it gets nuanced, because “quality” means different things depending on what you’re shooting.<\/p>\n\n\n\n

On human subjects \u2014 faces, skin, subtle movement \u2014 Sulphur 2 has a real edge.<\/strong> I ran matched prompts on both: a close-up of a person in conversation, natural light, minimal camera movement. The output had noticeably better skin texture and less of the waxy, over-smoothed look that still plagues a lot of AI video. Wan 2.1 at the same settings produced faces that looked competent but slightly off in ways that are hard to describe but easy to spot.<\/p>\n\n\n\n

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On complex motion \u2014 crowds, dynamic camera work, fast action \u2014 Wan 2.1 is stronger.<\/strong> This is where the heavier architecture earns its VRAM cost. Per research on video diffusion temporal consistency<\/a>, maintaining coherence across frames during fast or complex motion is still one of the harder problems in this generation of models. Wan handles it better than LTX-derived checkpoints, and this model inherits that ceiling from its base.<\/p>\n\n\n\n

On non-human content<\/strong> \u2014 objects, environments, abstract scenes \u2014 I didn’t find a clear winner. Both produced usable results. Wan had slightly more “cinematic” motion on landscape shots; the finetune landed sharper on static or near-static scenes.<\/p>\n\n\n\n

If your content is primarily talking-head video, product demos with people, or lifestyle content: Sulphur 2. If you’re doing anything with significant motion complexity or you need variety across scene types: Wan 2.1.<\/p>\n\n\n\n

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Setup<\/h2>\n\n\n\n

This is an area where the sulphur 2 vs ltx and sulphur 2 vs wan comparisons diverge in a way that matters practically.<\/p>\n\n\n\n

Sulphur 2<\/strong> runs on ComfyUI<\/a> using LTX-compatible nodes. If you’re already set up for LTX Video, the adjustment is minimal \u2014 download the checkpoint, confirm your VAE is the LTX version, install ComfyUI-VideoHelperSuite, and you’re running. The workflow is ComfyUI-native.<\/p>\n\n\n\n

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Wan 2.1<\/strong> has more setup paths: it runs on ComfyUI via community nodes, but also has dedicated inference scripts and growing support in tools like Fooocus and dedicated Wan UIs. The 1.3B variant is significantly easier to run than the 14B \u2014 if you’re on 8\u201310 GB VRAM, that’s your entry point. The 14B is the version worth comparing on quality, but it’s a heavier lift to get running.<\/p>\n\n\n\n

Setup complexity, roughly: it’s the simpler path if you’re already in a ComfyUI workflow. Wan 2.1 (14B) is harder upfront but has more community documentation to fall back on.<\/p>\n\n\n\n

\u26a0\ufe0f Both model pages may update \u2014 verify current file names and node requirements before downloading.<\/em><\/p>\n\n\n\n

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Speed<\/h2>\n\n\n\n

Numbers from my setup: RTX 4080 16 GB, ComfyUI, 512\u00d7768 resolution, 33 frames, 30 steps.<\/p>\n\n\n\n

<\/td>Sulphur 2<\/td>Wan 2.1 (14B)<\/td><\/tr>
Generation time<\/td>~4 min<\/td>~11 min<\/td><\/tr>
VRAM usage (peak)<\/td>~13 GB<\/td>~15.5 GB<\/td><\/tr>
Usable at 12 GB?<\/td>Yes (fp8 + lowvram)<\/td>Tight, often crashes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

It runs roughly 2\u20133\u00d7 faster than Wan 2.1 at comparable quality settings. That gap compounds when you’re iterating \u2014 running six prompt variations in an afternoon is very different at 4 minutes per clip versus 11.<\/p>\n\n\n\n

The Wan 1.3B model closes that speed gap but at a significant quality cost. For creator use, the 1.3B is better treated as a drafting tool than a delivery tool.<\/p>\n\n\n\n

One note: these numbers are from a single setup on specific hardware. Community reports vary, especially on AMD cards and lower-VRAM configurations. Take them as relative comparisons rather than absolutes.<\/p>\n\n\n\n

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When to Choose Each<\/h2>\n\n\n\n

Choose Sulphur 2 if:<\/strong><\/p>\n\n\n\n