diff --git a/README.md b/README.md
index 96341f9..dec0976 100644
--- a/README.md
+++ b/README.md
@@ -3,11 +3,15 @@
 
 ## Setup
 
-First, download and set up the repo:
+First, clone the repo **with submodules** — the SC kernels live in the
+[`scmp_kernels`](https://github.com/CrucibleComputingGroup/scmp_kernels)
+submodule:
 
 ```bash
-git clone https://github.com/Juanerx/Q-DiT.git
-cd Q-DiT
+git clone --recurse-submodules https://github.com/CrucibleComputingGroup/scmp_diffusion.git
+cd scmp_diffusion
+# if you forgot --recurse-submodules:
+git submodule update --init --recursive
 ```
 Then create the environment and install required packages:
 ```bash
@@ -15,8 +19,12 @@ conda create -n qdit python=3.8
 conda activate qdit
 pip install -r requirements.txt
 pip install .
+pip install -e ./scmp_kernels   # SC matmul kernels (Triton, needs a CUDA GPU)
 ```
 
+> SC mode imports `scmp_kernels` (`sc_matmul`, `scmp_kernels.mp`); standard
+> GPTQ/static quantization works without it.
+
 
 ## Usage
 
@@ -43,7 +51,7 @@ SC mode replaces floating-point matrix multiplications with stochastic computing
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 0.5 --qklayerwise 1.0 --avlayerwise 0.2 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --image-size 256 --num-sampling-steps 100 --batch-size 16 \
     --ckpt pretrained_models/DiT-XL-2-256x256.pt
 ```
@@ -59,7 +67,6 @@ python scripts/quant_sc_main.py \
 | `--mlplayerwise` | float | 0.0 | Fraction of blocks to use SC for MLP fc1 and fc2 (0-1). |
 | `--inputprojlayerwise` | float | 0.0 | Fraction of blocks to use SC for QKV input projection (0-1). |
 | `--sc_prec` | int | 8 | SC precision in bits. Sets `stoc_len = 2^sc_prec` (e.g., 8 → stoc_len=256). |
-| `--sc_enable` | flag | false | Use enable-signal SC multiplication (compact kernel) instead of standard XNOR/AND. |
 | `--sc_noise_model` | flag | false | Replace real SC kernels with a fast analytical noise surrogate. See [SC Noise Model](#sc-noise-model-fast-surrogate) below. |
 | `--sc_noise_local_correction` | float | 0.15 | Variance correction for per-row/per-batch scaled matmuls (MLP, AV, QK, input proj). Recommended: 0.10 - 0.15. |
 | `--sc_noise_global_correction` | float | 0.60 | Variance correction for per-tensor scaled matmuls (output proj). Recommended: 0.10 - 0.20. |
@@ -71,6 +78,14 @@ python scripts/quant_sc_main.py \
 | `--mp` | flag | false | Enable per-token-row mixed precision for QK/AV. Assigns different stoc_len levels to different token rows based on runtime importance. |
 | `--mp_levels` | str | `256,128,64,32` | Comma-separated stoc_len levels for MP, sorted descending. |
 | `--mp_fractions` | str | None | Comma-separated fractions of rows per MP level (must sum to 1). Default: equal fractions (1/N each). |
+| `--adaptive_mp` | flag | false | Enable adaptive mixed precision with timestep-aware thresholds (rows ranked by a runtime metric; thresholds slide with diffusion progress). Tune with `--mp_alpha` (sensitivity to timestep progress) and `--mp_beta` (base threshold offset). Per-operator overrides exist (`--mp_alpha_qk`, `--mp_beta_av`, …). |
+| `--adaptive_mp_table` | str | None | Path to a calibrated adaptive-MP threshold JSON table (precomputed thresholds instead of `--mp_alpha`/`--mp_beta`). |
+| `--range_mp` | flag | false | Enable range-based mixed precision: assign `stoc_len` per group by its weight (max−min) range. Configure with `--range_mp_levels` and `--range_mp_threshold` (normalized 0–1; higher → more groups at lower precision; per-operator `--range_mp_threshold_qk` etc.). |
+| `--sc_fixed_level_prec` | flag | false | Keep kernel `sc_prec` fixed to `--sc_prec` for all MP `stoc_len` levels (instead of deriving `sc_prec` per level). |
+
+> **MP modes are mutually exclusive.** Pick one of `--mp` (static row fractions),
+> `--adaptive_mp` (metric- and timestep-aware), or `--range_mp` (weight-range based).
+> All three only activate for blocks/timesteps where QK/AV/MLP SC is already enabled.
 
 #### Examples
 
@@ -79,7 +94,7 @@ python scripts/quant_sc_main.py \
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 0.5 --qklayerwise 1.0 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --image-size 256 --num-sampling-steps 100 --batch-size 16 \
     --ckpt pretrained_models/DiT-XL-2-256x256.pt
 ```
@@ -89,7 +104,7 @@ python scripts/quant_sc_main.py \
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 1.0 --qklayerwise 0.8 --avlayerwise 0.8 --mlplayerwise 0.8 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --image-size 256 --num-sampling-steps 100 --batch-size 16 \
     --ckpt pretrained_models/DiT-XL-2-256x256.pt
 ```
@@ -99,7 +114,7 @@ python scripts/quant_sc_main.py \
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 0.5 --qklayerwise 1.0 --avlayerwise 0.5 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --save_sc_config my_config.json \
     --image-size 256 --num-sampling-steps 100 --batch-size 16 \
     --ckpt pretrained_models/DiT-XL-2-256x256.pt
@@ -116,7 +131,7 @@ Rows/heads are bucketed into N discrete `stoc_len` levels using quantile boundar
 
 **Quick test (4 levels, equal fractions):**
 ```bash
-python scripts/quant_sc_main.py --wbits 8 --abits 8 --w_sym --a_sym --timewise 0.5 --qklayerwise 0.5 --avlayerwise 0.5 --sc_prec 8 --sc_enable --mp --mp_levels 256,128,64,32
+python scripts/quant_sc_main.py --wbits 8 --abits 8 --w_sym --a_sym --timewise 0.5 --qklayerwise 0.5 --avlayerwise 0.5 --sc_prec 8 --mp --mp_levels 256,128,64,32
 ```
 
 **Custom fractions (aggressive — only 10% at full precision):**
@@ -124,7 +139,7 @@ python scripts/quant_sc_main.py --wbits 8 --abits 8 --w_sym --a_sym --timewise 0
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 0.5 --qklayerwise 0.5 --avlayerwise 0.5 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --mp --mp_levels 256,128,64,32 --mp_fractions 0.1,0.2,0.3,0.4
 ```
 
@@ -133,7 +148,7 @@ python scripts/quant_sc_main.py \
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 0.5 --qklayerwise 0.5 --avlayerwise 0.5 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --mp --mp_levels 256,64 --mp_fractions 0.3,0.7
 ```
 
@@ -172,7 +187,7 @@ python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 1 --qklayerwise 1.0 --avlayerwise 1.0 \
     --projlayerwise 1.0 --mlplayerwise 1.0 --inputprojlayerwise 1.0 \
-    --sc_prec 8 --sc_enable --sc_noise_model \
+    --sc_prec 8 --sc_noise_model \
     --image-size 256 --num-sampling-steps 100 --batch-size 16
 ```
 
@@ -182,7 +197,7 @@ python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 1 --qklayerwise 1.0 --avlayerwise 1.0 \
     --projlayerwise 1.0 --mlplayerwise 1.0 --inputprojlayerwise 1.0 \
-    --sc_prec 8 --sc_enable --sc_noise_model \
+    --sc_prec 8 --sc_noise_model \
     --adaptive_mp --mp_levels 256,128,64,32,16 \
     --mp_alpha 0.3 --mp_beta 0.1 \
     --image-size 256 --num-sampling-steps 100 --batch-size 16
@@ -210,7 +225,7 @@ python scripts/quant_sc_main.py \
 
 #### Behavior
 
-- Orthogonal to `--sc_enable` (both can be on simultaneously)
+- Orthogonal to the real SC kernels — the surrogate replaces them, leaving the rest of the quant pipeline unchanged
 - All SC dispatch paths are supported: uniform, adaptive MP, range MP, per-head mixed
 - The real SC path is completely untouched when `--sc_noise_model` is not set
 - Uses `torch.compile` for kernel fusion; first ~5 iterations are slow (JIT warmup), then steady state ~12 it/s at batch=8 on RTX PRO 6000
@@ -333,7 +348,7 @@ Non-power-of-2 values (e.g., 181 for int7.5) are supported. The quantization gri
 python scripts/quant_sc_main.py \
     --wbits 8 --abits 8 --w_sym --a_sym \
     --timewise 1.0 --qklayerwise 1.0 --avlayerwise 1.0 \
-    --sc_prec 8 --sc_enable \
+    --sc_prec 8 \
     --sc_config path/to/config.json \
     --image-size 256 --num-sampling-steps 100 --batch-size 16 \
     --ckpt pretrained_models/DiT-XL-2-256x256.pt
@@ -380,7 +395,7 @@ Results are saved to `../results/<NNN>-qdit_sc_<params>/`:
 
 - **Do NOT combine `--static` with `--a_sym`** — the static quantizer only supports asymmetric activation quantization. Use `--a_sym` without `--static`, or `--static` without `--a_sym`.
 - DiT-XL/2 has **28 blocks** and **16 attention heads** with head_dim=72.
-- The `--sc_enable` flag selects the compact enable-signal kernel path, which is required for mixed-precision early termination.
+- SC matmuls now always run through the compact **enable-signal** kernel path (via `scmp_kernels.sc_matmul`). The old `--sc_enable` toggle and the legacy XNOR/AND path have been removed — enable-signal is canonical and is what makes mixed-precision early termination possible.
 
 
 ## BibTeX