Merge 96e5287a72 into 25757a53c9

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

@@ -1087,6 +1087,51 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             orig_dtype=MixedPrecisionOps._compute_dtype,
                             orig_shape=(self.out_features, self.in_features),
                         )
+                    elif self.quant_format == "svdquant_w4a4":
+                        # SVDQuant W4A4: per-group weight scales + low-rank correction
+                        # (proj_down, proj_up) + activation smoothing (smooth_factor)
+                        wscales = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys)
+                        proj_down = self._load_scale_param(state_dict, prefix, "proj_down", device, manually_loaded_keys)
+                        proj_up = self._load_scale_param(state_dict, prefix, "proj_up", device, manually_loaded_keys)
+                        smooth_factor = self._load_scale_param(state_dict, prefix, "smooth_factor", device, manually_loaded_keys)
+                        act_unsigned = bool(layer_conf.get("act_unsigned", False))
+
+                        # Early Qwen-Image conversion artifacts did not persist the
+                        # fused GELU -> fc2 unsigned-activation flag. Those layers
+                        # are the second linear in the feed-forward block.
+                        if not act_unsigned and (
+                            layer_name.endswith(".img_mlp.net.2") or layer_name.endswith(".txt_mlp.net.2")
+                        ):
+                            act_unsigned = True
+
+                        if any(t is None for t in (wscales, proj_down, proj_up, smooth_factor)):
+                            raise ValueError(f"Missing SVDQuant W4A4 parameters for layer {layer_name}")
+
+                        params = layout_cls.Params(
+                            scale=wscales,
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                            proj_down=proj_down,
+                            proj_up=proj_up,
+                            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}")
 
@@ -1136,6 +1181,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     quant_conf = {"format": self.quant_format}
                     if self._full_precision_mm_config:
                         quant_conf["full_precision_matrix_mult"] = True
+                    if bool(getattr(getattr(self.weight, "_params", None), "act_unsigned", False)):
+                        quant_conf["act_unsigned"] = True
                     sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
 
                     input_scale = getattr(self, 'input_scale', None)
@@ -1193,18 +1240,24 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 
                 # Inference path (unchanged)
                 if _use_quantized:
+                    # Some layouts (e.g. SVDQuant W4A4) do activation quantization
+                    # inside their fused kernel and cannot pre-quantize a float
+                    # tensor up-front. Skip the input wrapping for those.
+                    layout_cls = get_layout_class(self.layout_type)
+                    layout_quantizes_input = getattr(layout_cls, "QUANTIZES_INPUT", True)
 
-                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
+                    if layout_quantizes_input:
+                        # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
+                        input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
 
-                    # Fall back to non-quantized for non-2D tensors
-                    if input_reshaped.ndim == 2:
-                        reshaped_3d = input.ndim == 3
-                        # dtype is now implicit in the layout class
-                        scale = getattr(self, 'input_scale', None)
-                        if scale is not None:
-                            scale = comfy.model_management.cast_to_device(scale, input.device, None)
-                        input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
+                        # Fall back to non-quantized for non-2D tensors
+                        if input_reshaped.ndim == 2:
+                            reshaped_3d = input.ndim == 3
+                            # dtype is now implicit in the layout class
+                            scale = getattr(self, 'input_scale', None)
+                            if scale is not None:
+                                scale = comfy.model_management.cast_to_device(scale, input.device, None)
+                            input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
 
                 output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))
 

comfy/quant_ops.py

@@ -47,6 +47,12 @@ except ImportError as e:
     class _CKNvfp4Layout:
         pass
 
+    class _CKSVDQuantW4A4Layout:
+        pass
+
+    class _CKAWQW4A16Layout:
+        pass
+
     def register_layout_class(name, cls):
         pass
 
@@ -65,6 +71,26 @@ if not _CK_MXFP8_AVAILABLE:
     class _CKMxfp8Layout:
         pass
 
+_CK_SVDQUANT_W4A4_AVAILABLE = False
+if _CK_AVAILABLE:
+    try:
+        from comfy_kitchen.tensor import TensorCoreSVDQuantW4A4Layout as _CKSVDQuantW4A4Layout
+        _CK_SVDQUANT_W4A4_AVAILABLE = True
+    except ImportError:
+        logging.info("comfy_kitchen does not expose SVDQuant W4A4 layout; int4 SVDQuant checkpoints will not be supported.")
+        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
 
 # ==============================================================================
@@ -172,6 +198,21 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
     FP8_DTYPE = torch.float8_e5m2
 
 
+# SVDQuant W4A4 — pre-quantized offline (no runtime quantize), pass through the
+# kitchen-registered layout class unchanged. Comfy-side extension reserved in
+# case per-layer input scales or other Comfy-specific metadata are added later.
+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
 
@@ -186,6 +227,10 @@ register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
 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": {
@@ -214,6 +259,22 @@ if _CK_MXFP8_AVAILABLE:
         "group_size": 32,
     }
 
+if _CK_SVDQUANT_W4A4_AVAILABLE:
+    QUANT_ALGOS["svdquant_w4a4"] = {
+        "storage_t": torch.int8,
+        "parameters": {"weight_scale", "proj_down", "proj_up", "smooth_factor"},
+        "comfy_tensor_layout": "TensorCoreSVDQuantW4A4Layout",
+        "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
@@ -222,10 +283,12 @@ if _CK_MXFP8_AVAILABLE:
 __all__ = [
     "QuantizedTensor",
     "QuantizedLayout",
+    "TensorCoreAWQW4A16Layout",
     "TensorCoreFP8Layout",
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",
     "TensorCoreNVFP4Layout",
+    "TensorCoreSVDQuantW4A4Layout",
     "QUANT_ALGOS",
     "register_layout_op",
 ]