10Eros_Quants

10Eros_v1.4 — INT8 Quantizations

INT8 quantizations of 10Eros_v1.4 (bf16), a fine-tune derived from LTX-2 (Lightricks). Two variants provided, both using row-wise scaling with heuristic layer exclusion.

Files

Both load correctly with standard ComfyUI INT8-aware loaders — no special custom nodes required.

Details

• Source: 10Eros_v1.4_bf16.safetensors (~40GB)
• Tool: convert-to-quant (ctq), by SilverOxides
• Format: ComfyUI-compatible (--comfy_quant), quant metadata embedded
• Tensors: 5,947 original → 1,774 weights quantized, 304 skipped (heuristically excluded as poor quantization candidates) → 8,887 final tensor count (both variants)

Which One Should I Use?

• int8_row_heur: Good default. Clean output, no known issues.
• int8_row_convrot: ConvRot's Hadamard rotation typically produces measurably better quantization fidelity, especially on layers with high dynamic range. If you have the VRAM/patience to reproduce it yourself, worth trying first; both are provided here so you can compare directly.

Early testing found no meaningful quality gap between the two on standard SFW/NSFW test generations — pick based on what performs best for your specific use case.

Usage

Drop either file into ComfyUI/models/diffusion_models/, refresh node definitions, load via a standard ComfyUI diffusion model loader that supports --comfy_quant metadata (e.g. Load Diffusion Model (Quantized)).

License

This is a derivative of LTX-2 and inherits the LTX-2 Community License Agreement. Any use is subject to that license's terms — see link above.

Notes

Early INT8 conversions for LTX-based models — no LTX-specific preset yet exists in convert-to-quant; --heur (heuristic layer skipping) was used in place of a dedicated preset. ConvRot variant required an 80GB VRAM environment (RunPod A100 SXM) to complete due to memory demands on the largest embedding layers — not reproducible on typical consumer GPUs (tested: OOMs on 16GB).