Winnougan Nodes
Winnougan and WINT8 custom nodes for ComfyUI — prompt encoder, CLIP loader, power LoRA loader, INT8 diffuser loader, resolution pickers, and more.
Nodes (3)
The CLIP Loader for Quantized Text Encoders That Doesn't Silently Load Garbage
The INT8 Loader That Quantizes on the Fly — No Pre-Quantized File Required
The LoRA Loader That Actually Works on INT8 Models — No Dequantize Round-Trip
WINT8 Nodes A self-contained suite for working with INT8 quantized diffusion models. No dependency on int88 or any other external custom node — all quantization logic is built in. 🔥 WINT8 Diffuser Loader Loads INT8 quantized diffusion models with full control over the quantization pipeline. Features:
Tensorwise mode — one scale per weight tensor. Fast loading, minimal memory overhead Blockwise mode — one scale per 128×128 tile. Finer quantization granularity, better quality for models with activation outliers Sage Attention — optional replacement of ComfyUI's attention kernel with Sage Attention for reduced VRAM usage (requires sageattn package) QuaRot — optional Hadamard rotation to spread outliers before quantization, improving INT8 quality On-the-fly quantization — quantize fp16/bf16 checkpoints to INT8 at load time (for non-pre-quantized models) Model type presets — dedicated exclusion lists for flux2, z-image, chroma, wan, ltx2, qwen, ernie, hidream ensuring the right layers stay in full precision
Supported models: Flux2, Z-Image Turbo, Chroma, WAN, LTX2, Qwen, Ernie, HiDream, and any INT8 pre-quantized diffusion model 🔥 WINT8 Power LoRA Loader Multi-LoRA loader adapted for INT8 quantized models. Uses a dynamic forward hook instead of the standard ComfyUI weight patching path, so LoRAs apply correctly without needing to dequantize and re-quantize the entire model.
Same powerful multi-row UI as the Winnougan Power LoRA Loader Uses your existing standard LoRA files — no INT8-converted LoRAs needed Individual on/off toggles and strength controls per LoRA Global toggle all button Live LoRA search dialog Passes CLIP through unchanged
Self-Contained Architecture The WINT8 suite ships everything it needs internally: FilePurposewint8_quant.pyInt8TensorwiseOps + DynamicLoRAHookwint8_blockwise.pyInt8BlockwiseOps with 2D tile scalingwint8_fused_kernel.pyTriton fused INT8 kernels with torch._int_mm fallbackwint8_quarot.pyHadamard rotation (scipy optional, pure-torch fallback) Triton kernels are used automatically when available and fall back to torch._int_mm gracefully. QuaRot falls back to pure PyTorch if scipy is not installed.
ComfyUI/custom_nodes/
├── winnougan_nodes/
│ ├── __init__.py
│ ├── winnougan_prompt_encoder.py
│ ├── winnougan_clip_loader.py
│ ├── winnougan_power_lora_loader.py
│ └── js/
│ ├── winnougan_prompt_encoder.js
│ ├── winnougan_clip_loader.js
│ └── winnougan_power_lora_loader.js
└── wint8_nodes/
├── __init__.py
├── wint8_diffuser_loader.py
├── wint8_power_lora_loader.py
├── wint8_quant.py
├── wint8_blockwise.py
├── wint8_fused_kernel.py
├── wint8_quarot.py
└── js/
├── wint8_diffuser_loader.js
└── wint8_power_lora_loader.js
Requirements
- ComfyUI (latest)
- PyTorch 2.1+
triton(optional, for fused INT8 kernels — Linux native, Windows viatriton-windows)scipy(optional, for optimized Hadamard matrices)sageattn(optional, for Sage Attention in WINT8 Diffuser Loader)- ComfyUI-GGUF (optional, for GGUF CLIP loading in Winnougan CLIP Loader)
