v1.3.0 - INT8 ConvRot stages + disk-backed branch streaming

Pre-quantized INT8 ConvRot stages are now supported natively. A stage whose linear_branch weights were quantized with ComfyUI's own int8-convrot serialization (<layer>.weight int8 + <layer>.weight_scale + <layer>.comfy_quant JSON) loads as real Comfy Kitchen QuantizedTensor/TensorWiseINT8Layout weights — convrot metadata intact, nothing dequantized at load time, and F.linear dispatches straight into the Kitchen INT8 kernel (2.7x faster per dispatched matmul). A ready-made quantized 8-step stage is at drbaph/vdn-minimax-h3-int8-convrot-comfyui (branch 4.3 -> 2.2 GB, ~4.7 GB lower peak VRAM while loading, A/B-rendered visually identical); make your own from any stage with tools/quantize_vdn_branch_int8.py.

Branch weights are now disk-backed. branch_weights: stream reads each block's weights straight from the safetensors file to the GPU on demand instead of materializing the full branch in system RAM first — the node no longer adds gigabytes of committed RAM. This also removes the VRAM pressure that made comfy offload the base model into pinned RAM on 32 GB cards; prefer stream over cache_gpu unless you have VRAM to spare.

• int8 A/B + ref2v demo videos in assets/; BF16-vs-INT8 timing table in both READMEs
• docs: lora_mode merge-default guidance, hardware reality check, fl2v/ref2v tested note (EN/ZH)
• verified: bitwise parity tests vs the materialized path, pytest 10/10, live A/B renders

No workflow changes needed.