Sulphur 2: Open-Source Uncensored AI Video Model

Open-source AI video generation took a noticeable step forward in early May 2026, when a model called Sulphur 2 appeared on Hugging Face and immediately drew attention inside the Stable Diffusion community. It is uncensored, it runs locally, and it is built on top of Lightricks' LTX 2.3 foundation model rather than being a brand-new architecture. For anyone who has hit the subscription caps, generation limits, or content filters of cloud video platforms, that combination is exactly what makes Sulphur 2 worth understanding.

This guide covers what Sulphur 2 is, how it was trained, what ships in the release, the hardware it actually needs, how it compares to Runway, Kling AI, Pika, and the community-made 10Eros, and how to run it locally. By the end you will know whether it fits your GPU, your workflow, and your goals.

TL;DR

For most power users in 2026, Sulphur 2 is the most flexible open option. Commercial platforms still win on ease of use. There is no overall winner, only the right tradeoff for your constraints.

What Is Sulphur 2?

Sulphur 2 is an open-source, uncensored AI video generation model released as open weights on Hugging Face in early May 2026. It is a fine-tune of Lightricks' LTX 2.3 foundation model, supports native text-to-video and image-to-video, and is designed to run locally on consumer GPUs without a hosted SaaS, subscription, or platform safety filters in the loop.

Sulphur 2 is an uncensored AI video generation model released as open weights on the SulphurAI/Sulphur-2-base Hugging Face repository by the SulphurAI community. The public release thread on r/StableDiffusion appeared in early May 2026, placing it firmly inside the May 2026 LTX 2.3 wave of open video models.

Three facts define what Sulphur 2 is:

1. It is a fine-tune, not a new architecture. Sulphur 2 is a derivative of Lightricks' DiT-based LTX 2.3 audio-video foundation model, described by its contributors as a drop-in replacement compatible with existing LTX 2.3 workflows.
2. It is uncensored. The training data was filtered only to exclude illegal content and 2D animation, so the model does not impose the safety filters common to commercial video platforms.
3. It runs locally. There is no hosted SaaS in the loop. You download the weights, point ComfyUI at them, and generate on your own hardware.

This puts Sulphur 2 in the same category as the broader open-source video stack we cover in our guide to the best AI 3D and video model generators, but focused specifically on unrestricted local video synthesis.

Sulphur 2 at a glance

How Sulphur 2 Was Built

The most useful details about Sulphur 2 are not in the marketing copy but in the contributor's public notes, which the Sulphur 2 reference page summarizes. Three design choices explain why the model behaves the way it does.

A realism-biased training set

According to the contributor's public release notes summarized on the Sulphur 2 reference page, training used approximately 125,000 video clips at 10 seconds and 24 frames per second, totaling roughly 500GB of data. Crucially, the only filtering was to exclude illegal content and 2D animation. The deliberate exclusion of animation was a realism decision: by removing stylized and cartoon footage, the model leans toward realistic motion, anatomy, and lighting rather than the smooth, exaggerated movement typical of anime-trained checkpoints.

This is also why Sulphur 2 is described as uncensored rather than merely open. The filtering philosophy is "remove what is illegal and what would hurt realism," not "remove what a platform policy disallows." Users are expected to handle their own compliance from there.

A drop-in fine-tune of LTX 2.3

Because Sulphur 2 is a derivative of Lightricks' LTX 2.3 foundation model rather than a from-scratch build, it inherits the upstream pipeline's input modes, sampler design, and node compatibility. The practical effect is that anyone already running LTX 2.3 workflows in ComfyUI can swap in the Sulphur 2 weights with minimal rewiring. The contributor explicitly states it works as a drop-in swap inside LTX 2.3 ComfyUI graphs.

Two sampling paths: base and distilled

Sulphur 2 ships two quality tiers that you mix and match:

• Base path: the full dev checkpoint, more stable motion and better anatomical fidelity.
• Distilled path: combine the dev base with the provided distill LoRAs to sample faster at the cost of some motion and anatomical fidelity.

This base-plus-distill pattern mirrors how LTX 2.3 itself ships ltx-2.3-22b-dev and ltx-2.3-22b-distilled, and it is the same tradeoff space any modern diffusion model exposes: quality versus speed versus VRAM.

What's in the Release

Sulphur 2 is distributed as a complete release, not a single checkpoint. That distinction matters because it is the reason the operational story feels closer to a configured stack than a raw model file. The release contains five components.

Base weights

Two dev checkpoints form the core of the model:

• sulphur_dev_bf16.safetensors: approximately 46GB, the full-precision path for maximum quality.
• sulphur_dev_fp8mixed.safetensors: approximately 29GB, the memory-efficient path for tighter VRAM budgets.

Distill LoRAs

Two LoRA files enable the faster distilled sampling path:

• sulphur_distil_bf16.safetensors
• sulphur_lora_rank_768.safetensors

These are intended to be combined with the dev base to trade some quality for speed, and the contributor notes you should use either the LoRAs or the full models, not both at the same time.

ComfyUI workflows

Four official JSON workflows ship with the release, covering every common combination out of the box:

• ltx23_i2v base (image-to-video, full quality)
• ltx23_i2v distilled (image-to-video, fast)
• ltx23_t2v base (text-to-video, full quality)
• ltx23_t2v distilled (text-to-video, fast)

Prompt enhancer

A separate prompt enhancer based on Qwen 3.5 9B is bundled alongside the video weights. It ships as GGUF plus an mmproj file, and it is intended to run locally through LM Studio so that prompt rewriting stays on-device alongside the video stack. The simplest setup is to create a Sulphur/promptenhancer folder inside your LM Studio models directory, drop the GGUF and mmproj files in, and then chat with it directly: there is no system prompt, you just send the text (and optionally an image) you want enhanced.

This prompt-enhancer approach is worth emphasizing. On commercial platforms, prompt rewriting is opaque and happens server-side. With Sulphur 2, the enhancer is a discrete, inspectable model you can run, modify, or skip entirely. For a deeper look at why that kind of transparency matters for production pipelines, see our review of Trellis 2 in VFX production.

Hardware Requirements

Sulphur 2 is a large video diffusion model, and the hardware story is the single biggest gating factor for who can actually use it. Here is what the numbers and community testing indicate.

Recommended setup

• 24GB+ VRAM preferred for the full bf16 dev checkpoint at comfortable resolutions.
• 16GB VRAM workable with the fp8-mixed dev checkpoint and the distilled LoRAs.
• High system RAM.
• NVIDIA CUDA support (the model targets CUDA; Apple Silicon "mps" is supported by the Diffusers path but is not the primary target).
• Multi-GPU setups are ideal for heavy, high-resolution workflows.

Low-VRAM strategies

Users with lower VRAM GPUs can still run Sulphur 2, but with compromises:

• Use the fp8-mixed dev checkpoint instead of bf16.
• Stack the distill LoRAs for faster, lighter sampling.
• Reduce output resolution.
• Generate shorter clips (fewer frames).
• Extract a smaller LoRA from the full model. One community member reports making a rank-32 version of the Sulphur 2 base that works as a lightweight LoRA.

On the Reddit release and comparison threads, a tester reports running the fp8 distilled path locally on a 4060 16GB, and the consensus is that 8GB is only realistic with aggressive quantization and very short clips. This is not currently optimized for low-end laptops, entry-level GPUs, or casual systems.

If you are coming from the 3D generation side and want to compare VRAM expectations, our ComfyUI Trellis node package comparison covers a similar hardware-optimization conversation for image-to-3D models.

Sulphur 2 vs Runway, Kling AI, Pika, and 10Eros

The most common search around Sulphur 2 is "how does it compare," so here is the honest comparison across the dimensions that decide which tool you open tonight.

The comparison matrix

When to pick Sulphur 2

Choose Sulphur 2 when you want full local control, unrestricted experimentation, deep workflow customization, no subscription lock-in, and integration flexibility. It targets ComfyUI users, AI video enthusiasts, researchers, open-source developers, and advanced workflow creators.

When to pick a commercial platform

Choose Runway, Kling AI, or Pika when you value easier interfaces, simpler workflows, faster setup, and a beginner-friendly experience. The tradeoff is that you accept generation caps, safety filters, subscription pricing, and limited customization.

The 10Eros question

10Eros deserves a note because it is not a separate model family; it is the same LTX 2.3 base, maintained as a community branch by the contributor known as TenStrip (Reddit user Ten__Strip). In community testing, 10Eros is reported to produce stronger facial consistency in image-to-video and a clearer understanding of certain unrestricted content. Its main drawback is that it fine-tunes on LTX2.3-DEV, which reintroduces some caption artifacts. Because 10Eros ships without GGUF or LoRA variants, many users find it harder to deploy than Sulphur 2, which is exactly why Sulphur's distribution as a full release with quantized variants is a real advantage.

The core tension across all of these is simple: flexibility versus simplicity. Sulphur 2 asks for technical setup knowledge, GPU hardware, workflow understanding, and patience for optimization. Commercial platforms ask for money and accept censorship. Neither is wrong; they optimize for different users.

How to Run Sulphur 2 Locally

The recommended deployment path for Sulphur 2 is ComfyUI plus the native LTX Video nodes documented at docs.ltx.video, not the auto-generated Diffusers snippets on the Hugging Face model page, which are convenient for quick experiments but are not the contributor's intended production path.

Step 1: Choose a weight format

Pick based on your VRAM:

• sulphur_dev_bf16.safetensors (~46GB) for maximum quality on 24-32GB VRAM.
• sulphur_dev_fp8mixed.safetensors (~29GB) for tighter memory budgets while staying on the official dev path.

Step 2: Optionally stack a distill LoRA

Combine the dev base with sulphur_distil_bf16.safetensors or sulphur_lora_rank_768.safetensors for faster sampling. Accept that the distilled variant is more likely to show motion and anatomical artifacts in exchange for speed and lower VRAM.

Step 3: Import a workflow

Import one of the four official ComfyUI JSON workflows (ltx23_i2v base or distilled, ltx23_t2v base or distilled) depending on whether you want text-to-video or image-to-video, and whether you want full quality or the fast path. These ship ready to import, so you are wiring a configured graph rather than building one from scratch.

Step 4: Set up the prompt enhancer (optional)

If you want on-device prompt rewriting, place the Qwen 3.5 9B GGUF and mmproj files in a Sulphur/promptenhancer folder inside your LM Studio models directory, then load the enhancer and send it your prompt text and optional image.

Step 5: Generate

Render at a resolution and clip length your VRAM can sustain, and iterate. Because everything runs locally, there are no generation caps and no queue waits beyond your own hardware speed.

The contributor notes the current workflows reference sulphur_final internally. The guidance is to use the distill LoRA or the full models, not both at the same time.

If you are new to ComfyUI model installs, the patterns in our Windows RTX 50 install guide for Trellis 2 transfer directly to Sulphur 2, since both live in the same ComfyUI ecosystem.

Strengths, Limitations, and Who It's For

No model is purely good news. Here is the honest breakdown.

Strengths

• Completely local generation: private, uncapped, no cloud dependency.
• Realism-biased motion: smoother camera motion, better continuity, more realistic human animation than many open predecessors, thanks to the animation-excluded training set.
• Native T2V and I2V: plus the other LTX 2.3 input formats inherited from upstream.
• Full workflow ecosystem: ComfyUI workflows, LoRA integrations, custom nodes, quantized variants, and a local prompt enhancer.
• Drop-in for LTX 2.3: minimal rewiring for anyone already in that ecosystem.
• Uncensored: no platform safety filters, for users who need unrestricted output.

Limitations

• High hardware requirements: 24GB+ VRAM preferred, 16GB workable with compromises, 8GB only with heavy quantization.
• Setup complexity: this is not a one-click app; it targets power users.
• Occasional instability: community testers report some instability typical of heavily fine-tuned open models.
• Specialized behavior: because it is heavily fine-tuned for realism, it may sacrifice some general-purpose capability compared to broader video models.
• Prompt enhancer is extra setup: useful, but it is a separate model you must configure in LM Studio rather than a built-in feature.

Results always depend heavily on prompting, workflows, hardware, and optimization settings.

Who it's for

Sulphur 2 is built for ComfyUI users, AI video enthusiasts, researchers, open-source developers, and advanced workflow creators. It is ideal for cinematic AI filmmaking, realistic human animation, image-to-video generation, experimental AI storytelling, advanced ComfyUI workflows, and local AI video research. It is not ideal for beginners, low-end GPUs, one-click generation, or casual creators.

The Bigger Picture: Open-Source AI Video in 2026

Sulphur 2 is interesting on its own, but it is more interesting as a signal. Open-source video models are becoming powerful enough for serious filmmaking workflows, and the pace is set less by any single checkpoint than by the ecosystem around it: workflows, custom nodes, LoRAs, quantization, and community experimentation.

That ecosystem-first dynamic is exactly how open-source AI evolves rapidly. A model like Sulphur 2 ships, the community immediately builds tiled upscale samplers, rank-32 LoRA extractions, vertical-aspect-ratio fixes, and I2V merges, and the practical capability of the stack advances faster than any single release could move on its own. The contributor TenStrip, for example, was already iterating on a tiled upscale sampler that fixes vertical aspect ratios and improves motion quality on upscale, the kind of incremental tooling that compounds across a community.

For creators already working in the Stable Diffusion ecosystem, the transition into video feels increasingly familiar. The same ComfyUI instincts, the same LoRA mental model, the same quantization tradeoffs apply. That continuity is a large part of why a release like Sulphur 2 can gain traction so quickly.

Frequently Asked Questions

What is Sulphur 2?

Sulphur 2 is an open-source, uncensored AI video generation model released as open weights on Hugging Face by the SulphurAI community in early May 2026. It is a fine-tune of Lightricks' LTX 2.3 foundation model and supports native text-to-video and image-to-video on consumer GPUs.

Is Sulphur 2 free to use?

Yes. Sulphur 2 is distributed as open weights at no cost. You need your own GPU and the ComfyUI runtime, but there is no subscription, no per-generation fee, and no cloud SaaS in the loop. Always verify the current license on the Hugging Face repository before commercial use.

How much VRAM does Sulphur 2 need?

24GB or more VRAM is preferred for the full bf16 dev checkpoint. With the fp8-mixed dev checkpoint, distilled LoRAs, and reduced resolution, community testers have run it on 16GB cards such as a 4060. 8GB is only realistic with heavy quantization and very short clips.

Is Sulphur 2 really uncensored?

Its training data was filtered only to exclude illegal content and 2D animation, so the model does not apply the safety filters common to commercial platforms. That makes it unrestricted, but users remain fully responsible for complying with local law and platform policies.

What is the difference between Sulphur 2 base and distilled?

The base checkpoint (sulphur_dev_bf16, about 46GB) is more stable and higher quality. The distilled path (sulphur_dev_fp8mixed about 29GB, plus the distill LoRAs) samples faster and uses less VRAM but trades some anatomical and motion fidelity for speed.

Who made Sulphur 2?

Sulphur 2 was built by the SulphurAI community. The public release was posted by contributor FusionCow, with testing and merging help from TenStrip, silveroxide (s1lv3rc01n), and the Musubi Tuner project. Two anonymous funders financed the original model and the version 2 release.

How does Sulphur 2 compare to Runway and Kling AI?

Sulphur 2 offers full local control, unrestricted output, and workflow customization with no subscription, but requires technical setup and serious GPU hardware. Runway and Kling AI provide easier interfaces and zero setup but impose generation caps, safety filters, and subscription costs. There is no overall winner. Pick by whether you prioritize flexibility or simplicity.

Can I run Sulphur 2 on a laptop?

Only with significant compromises. A 16GB laptop GPU can run the fp8-mixed distilled path at reduced resolution and shorter clip lengths. 8GB laptop GPUs require aggressive quantization and are not a practical target for this model.

Next Steps

Sulphur 2 represents one of the most interesting developments in open-source AI video generation right now, but it is a power-user tool, not a product. If you have the GPU and the ComfyUI fluency, it is worth the setup. If you want zero-friction generation, stay with a commercial platform.

To go deeper on the surrounding open-source generation stack:

• Best AI 3D and video model generators: the wider landscape of open and hosted generators.
• Trellis 2 ComfyUI complete guide: install and workflow patterns that transfer directly to Sulphur 2.
• Trellis 2 in VFX production review: why transparency and local control matter in production pipelines.

Grab the weights on Hugging Face, import an official workflow into ComfyUI, and start generating locally.