Omni: Account whole LoRA weights in ComfyUI memory budgets

omni/ComfyUI-OmniXPU/README.md

@@ -18,6 +18,7 @@ No workflow or model-pipeline replacement is required.
 |---|---|
 | Kitchen XPU backend | INT8/QTensor operations, FP8 QDQ and stochastic rounding, SVDQuant, AdaLN, four RoPE APIs, and ConvRot |
 | ComfyUI adapter | Attention routing, LayerNorm/RMSNorm class integration, the remaining FP8 model/factory bridge, and fused Lumina/Z-Image INT8 FFN wiring |
+| Memory adapter | Cached whole-LoRA model budgets plus optional DynamicVRAM per-layer XPU staging measurements |
 | Legacy fix | Global `F.interpolate` and `torch.median`/`torch.nanmedian` workarounds; disabled by default |
 
 RoPE, generic INT8 linear dispatch, and the old FP8 negative-zero wrapper are
@@ -45,6 +46,7 @@ OMNIXPU_ATTENTION=0         # Disable the attention adapter
 OMNIXPU_NORM=0              # Disable the norm adapter
 OMNIXPU_FP8_GEMM=0          # Disable the temporary FP8 model/factory adapter
 OMNIXPU_INT8_FFN=0          # Disable fused Lumina/Z-Image INT8 FFN wiring
+OMNIXPU_LORA_MEMORY=0       # Disable cached whole-LoRA budgets and staging logs
 ```
 
 Validated sub-routes can be disabled independently:
@@ -91,6 +93,18 @@ Dispatch decisions and fallback reasons:
 OMNIXPU_DEBUG_VERBOSE=1 python main.py
 ```
 
+LoRA weights are measured once when the LoRA node executes. Unique tensor sizes
+are cached in a `ModelPatcher` attachment, inherited by clones, accumulated for
+stacked LoRAs, and added to both `memory_required` and an explicitly supplied
+`minimum_memory_required`. The base model's `model_size()` semantics stay
+unchanged. Model loads read the cached attachment instead of rescanning patches.
+DynamicVRAM layer scanning is disabled by default. To diagnose every LoRA
+staging operation, including its XPU state and any failure, enable:
+
+```bash
+OMNIXPU_LORA_MEMORY_TRACE=1 python main.py
+```
+
 Set tracing variables before startup. The **OmniXPU Status** node reports:
 
 - GPU and `omni_xpu_kernel` capabilities;