coolthor/Sulphur-2-distilled-Q4_K_M-GGUF

text-to-video model by coolthor

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GGUF Q4KM conversion of the distilled diffusion transformer from SulphurAI/Sulphur-2-base, packaged with the BF16 text/audio auxiliary components, the full LTX video VAE, and a validated ComfyUI API workflow. This package is designed for older NVIDIA GPUs without native NVFP4 tensor cores. On a modified RTX 2080 Ti 22 GB, it was 2.68× faster and used 3.65 GiB less peak VRAM than the existing Sulphur NVFP4 checkpoint in the same controlled workflow. This is a modified quantized redistribution, not an official SulphurAI or Lightricks release. GGUF tensor layout: 2,672 F32, 28 BF16, 1,376 Q4K, and 368 Q6K tensors. The auxiliary checkpoint contains exactly 4 text-projection, 102 audio-VAE, and 1,227 vocoder tensors, all BF16. The Gemma-3-12B text encoder is not included. Obtain gemma312Bitfp4mixed.safetensors from the upstream LTX/Sulphur text-encoder distribution. Controlled workflow: Q4KM was 2.683× faster, reduced wall time by 62.7%, and saved 3,742 MiB / 3.65 GiB (17.2%) peak VRAM. A separate…

coolthor/Sulphur-2-distilled-Q4_K_M-GGUF on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Sulphur 2 distilled — GGUF Q4KM for ComfyUI
  2. Files
  3. RTX 2080 Ti benchmark
  4. Visual and media validation
  5. Requirements
  6. Installation
  7. Distilled settings
  8. Honest caveats
  9. Conversion notes
  10. Credits
  11. License

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Model overview

coolthor/Sulphur-2-distilled-Q4_K_M-GGUF is a text-to-video model repository published by coolthor on Hugging Face. The source reports the gguf library.

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Model facts

Task
text-to-video
Library
gguf
Recent downloads (30 days)
2,734
Cumulative likes
2
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
other
Access
Not gated by Hugging Face
Created
2026-07-22T04:53:41.000Z
Last modified
2026-07-22T05:11:32.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: SulphurAI/Sulphur-2-base

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-09-29T06:39:07.699Z. Daily imports are snapshots, not real-time monitoring.

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