Add AWQ W4A16 (modulation) integration with comfy-kitchen

Wires comfy-kitchen's TensorCoreAWQW4A16Layout (introduced on
feat/awq-w4a16-modulation) into ComfyUI's MixedPrecisionOps so checkpoints
that tag modulation linears with comfy_quant.format = "awq_w4a16" get
their (qweight, weight_scale, weight_zero) loaded into the kitchen layout
class instead of being dequantized to bf16 plain Linear at conversion time.

quant_ops.py:
- detect TensorCoreAWQW4A16Layout availability and stub it out for the
  no-kitchen fallback (mirrors the SVDQuant W4A4 pattern)
- register the layout class + add "awq_w4a16" to QUANT_ALGOS
  (storage_t = int8 packed uint4, parameters = {weight_scale, weight_zero},
   default group_size = 64)

ops.py: add the awq_w4a16 branch in MixedPrecisionOps.Linear._load_from_state_dict
that constructs Params(scale, zeros, group_size, ...) and wraps qweight
into a QuantizedTensor — F.linear then dispatches to ck.gemv_awq_w4a16
via the layout's aten handlers.

Pairs with comfy-kitchen feat/awq-w4a16-modulation. Targets the ~10 GB
inflation in Qwen-Image-Edit kitchen-native checkpoints, where the
modulation linears (img_mod.1 / txt_mod.1) currently dominate disk + VRAM
because they're materialized as plain bf16 Linear during conversion.

comfy/ops.py

@@ -1026,6 +1026,22 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             smooth_factor=smooth_factor,
                             act_unsigned=act_unsigned,
                         )
+                    elif self.quant_format == "awq_w4a16":
+                        # AWQ W4A16: int4 weight, fp16/bf16 activation. Used for
+                        # the modulation linears (img_mod.1 / txt_mod.1) so they
+                        # stay int4 in checkpoint + VRAM rather than getting
+                        # dequantized to bf16 at conversion time (~10 GB saving).
+                        wscales = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys)
+                        wzeros = self._load_scale_param(state_dict, prefix, "weight_zero", device, manually_loaded_keys)
+                        if wscales is None or wzeros is None:
+                            raise ValueError(f"Missing AWQ W4A16 parameters for layer {layer_name}")
+                        params = layout_cls.Params(
+                            scale=wscales,
+                            zeros=wzeros,
+                            group_size=int(layer_conf.get("group_size", qconfig.get("group_size", 64))),
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                        )
                     else:
                         raise ValueError(f"Unsupported quantization format: {self.quant_format}")
 

comfy/quant_ops.py

@@ -40,6 +40,9 @@ except ImportError as e:
     class _CKSVDQuantW4A4Layout:
         pass
 
+    class _CKAWQW4A16Layout:
+        pass
+
     def register_layout_class(name, cls):
         pass
 
@@ -68,6 +71,16 @@ if _CK_AVAILABLE:
         class _CKSVDQuantW4A4Layout:
             pass
 
+_CK_AWQ_W4A16_AVAILABLE = False
+if _CK_AVAILABLE:
+    try:
+        from comfy_kitchen.tensor import TensorCoreAWQW4A16Layout as _CKAWQW4A16Layout
+        _CK_AWQ_W4A16_AVAILABLE = True
+    except ImportError:
+        logging.info("comfy_kitchen does not expose AWQ W4A16 layout; int4 AWQ modulation checkpoints will fall back to bf16-dequantized layers.")
+        class _CKAWQW4A16Layout:
+            pass
+
 import comfy.float
 
 # ==============================================================================
@@ -182,6 +195,14 @@ class TensorCoreSVDQuantW4A4Layout(_CKSVDQuantW4A4Layout):
     pass
 
 
+# AWQ W4A16 — pre-quantized offline (no runtime quantize) via the kitchen
+# eager `gemv_awq_w4a16` op. Used for modulation linears (img_mod.1 /
+# txt_mod.1) on Qwen-Image-Edit and similar topologies where keeping the
+# weight at int4 saves ~10 GB of VRAM vs the bf16-dequantized fallback.
+class TensorCoreAWQW4A16Layout(_CKAWQW4A16Layout):
+    pass
+
+
 # Backward compatibility alias - default to E4M3
 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
 
@@ -198,6 +219,8 @@ if _CK_MXFP8_AVAILABLE:
     register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 if _CK_SVDQUANT_W4A4_AVAILABLE:
     register_layout_class("TensorCoreSVDQuantW4A4Layout", TensorCoreSVDQuantW4A4Layout)
+if _CK_AWQ_W4A16_AVAILABLE:
+    register_layout_class("TensorCoreAWQW4A16Layout", TensorCoreAWQW4A16Layout)
 
 QUANT_ALGOS = {
     "float8_e4m3fn": {
@@ -234,6 +257,14 @@ if _CK_SVDQUANT_W4A4_AVAILABLE:
         "group_size": 64,
     }
 
+if _CK_AWQ_W4A16_AVAILABLE:
+    QUANT_ALGOS["awq_w4a16"] = {
+        "storage_t": torch.int8,
+        "parameters": {"weight_scale", "weight_zero"},
+        "comfy_tensor_layout": "TensorCoreAWQW4A16Layout",
+        "group_size": 64,
+    }
+
 
 # ==============================================================================
 # Re-exports for backward compatibility
@@ -242,6 +273,7 @@ if _CK_SVDQUANT_W4A4_AVAILABLE:
 __all__ = [
     "QuantizedTensor",
     "QuantizedLayout",
+    "TensorCoreAWQW4A16Layout",
     "TensorCoreFP8Layout",
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",