diff --git a/CHANGELOG.md b/CHANGELOG.md
index 63b86dbd5..53b02c6b4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,7 +16,7 @@
 - **SDNQ Quantization**  
   - Add group size support for convolutional layers  
   - Add quantized matmul support for for convolutional layers  
-  - Add 5-bit and 3-bit quantization support  
+  - Add 7-bit, 5-bit and 3-bit quantization support  
   - Fix forced FP32 with tensorwise FP8 matmul  
   - Fix PyTorch <= 2.4 compatibility with FP8 matmul  
   - Fix VAE with conv quant  
diff --git a/modules/sdnq/common.py b/modules/sdnq/common.py
index 70addbe7a..ccfdf1227 100644
--- a/modules/sdnq/common.py
+++ b/modules/sdnq/common.py
@@ -9,16 +9,16 @@ torch_version = float(torch.__version__[:3])
 
 dtype_dict = {
     "int8": {"min": -128, "max": 127, "num_bits": 8, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.int8, "is_unsigned": False, "is_integer": True},
-    "int7": {"min": -64, "max": 63, "num_bits": 6, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
+    "int7": {"min": -64, "max": 63, "num_bits": 7, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
     "int6": {"min": -32, "max": 31, "num_bits": 6, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
-    "int5": {"min": -16, "max": 15, "num_bits": 5, "target_dtype": torch.int8, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
+    "int5": {"min": -16, "max": 15, "num_bits": 5, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
     "int4": {"min": -8, "max": 7, "num_bits": 4, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
     "int3": {"min": -4, "max": 3, "num_bits": 3, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
     "int2": {"min": -2, "max": 1, "num_bits": 2, "target_dtype": CustomDtype.INT2, "torch_dtype": torch.int8, "storage_dtype": torch.uint8, "is_unsigned": False, "is_integer": True},
     "uint8": {"min": 0, "max": 255, "num_bits": 8, "target_dtype": torch.uint8, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
-    "uint7": {"min": 0, "max": 127, "num_bits": 6, "target_dtype": torch.uint8, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
+    "uint7": {"min": 0, "max": 127, "num_bits": 7, "target_dtype": torch.uint8, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
     "uint6": {"min": 0, "max": 63, "num_bits": 6, "target_dtype": torch.uint8, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
-    "uint5": {"min": 0, "max": 31, "num_bits": 5, "target_dtype": torch.uint8, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
+    "uint5": {"min": 0, "max": 31, "num_bits": 5, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
     "uint4": {"min": 0, "max": 15, "num_bits": 4, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
     "uint3": {"min": 0, "max": 7, "num_bits": 3, "target_dtype": CustomDtype.INT4, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
     "uint2": {"min": 0, "max": 3, "num_bits": 2, "target_dtype": CustomDtype.INT2, "torch_dtype": torch.uint8, "storage_dtype": torch.uint8, "is_unsigned": True, "is_integer": True},
@@ -31,7 +31,7 @@ dtype_dict = {
 dtype_dict["bool"] = dtype_dict["uint1"]
 
 use_tensorwise_fp8_matmul = torch_version < 2.5 or devices.backend in {"cpu", "openvino"} or (devices.backend == "cuda" and sys.platform == "win32" and torch_version <= 2.7 and torch.cuda.get_device_capability(devices.device) == (8,9))
-quantized_matmul_dtypes = ("int8", "int6", "int5", "int4", "int3", "int2", "float8_e4m3fn", "float8_e5m2")
+quantized_matmul_dtypes = ("int8", "int7", "int6", "int5", "int4", "int3", "int2", "float8_e4m3fn", "float8_e5m2")
 if devices.backend in {"cpu", "openvino"}:
     quantized_matmul_dtypes += ("float8_e4m3fnuz", "float8_e5m2fnuz")
 
diff --git a/modules/sdnq/decompressor.py b/modules/sdnq/decompressor.py
index 2c37f26b2..c85ffc017 100644
--- a/modules/sdnq/decompressor.py
+++ b/modules/sdnq/decompressor.py
@@ -139,12 +139,14 @@ class PackedINTSymmetricWeightsDecompressor(torch.nn.Module):
 
 decompressor_dict = {
     "int8": SymmetricWeightsDecompressor,
+    "int7": PackedINTSymmetricWeightsDecompressor,
     "int6": PackedINTSymmetricWeightsDecompressor,
     "int5": PackedINTSymmetricWeightsDecompressor,
     "int4": PackedINTSymmetricWeightsDecompressor,
     "int3": PackedINTSymmetricWeightsDecompressor,
     "int2": PackedINTSymmetricWeightsDecompressor,
     "uint8": AsymmetricWeightsDecompressor,
+    "uint7": PackedINTAsymmetricWeightsDecompressor,
     "uint6": PackedINTAsymmetricWeightsDecompressor,
     "uint5": PackedINTAsymmetricWeightsDecompressor,
     "uint4": PackedINTAsymmetricWeightsDecompressor,
diff --git a/modules/sdnq/packed_int.py b/modules/sdnq/packed_int.py
index 351dc7546..e4717086e 100644
--- a/modules/sdnq/packed_int.py
+++ b/modules/sdnq/packed_int.py
@@ -19,6 +19,25 @@ def unpack_int_symetric(packed_tensor: torch.CharTensor, shape: torch.Size, weig
     return result
 
 
+def pack_uint7(tensor: torch.Tensor) -> torch.Tensor:
+    if tensor.dtype != torch.uint8:
+        raise RuntimeError(f"Invalid tensor dtype {tensor.type}. torch.uint8 type is supported.")
+    packed_tensor = tensor.contiguous().reshape(-1, 8)
+    packed_tensor = torch.stack(
+        (
+            torch.bitwise_or(packed_tensor[:, 0], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 1), 128)),
+            torch.bitwise_or(packed_tensor[:, 1], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 2), 128)),
+            torch.bitwise_or(packed_tensor[:, 2], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 3), 128)),
+            torch.bitwise_or(packed_tensor[:, 3], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 4), 128)),
+            torch.bitwise_or(packed_tensor[:, 4], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 5), 128)),
+            torch.bitwise_or(packed_tensor[:, 5], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 6), 128)),
+            torch.bitwise_or(packed_tensor[:, 6], torch.bitwise_and(torch.bitwise_left_shift(packed_tensor[:, 7], 7), 128)),
+        ),
+        dim=-1
+    )
+    return packed_tensor
+
+
 def pack_uint6(tensor: torch.Tensor) -> torch.Tensor:
     if tensor.dtype != torch.uint8:
         raise RuntimeError(f"Invalid tensor dtype {tensor.type}. torch.uint8 type is supported.")
@@ -109,6 +128,41 @@ def pack_uint2(tensor: torch.Tensor) -> torch.Tensor:
     return packed_tensor
 
 
+def unpack_uint7(packed_tensor: torch.Tensor, shape: torch.Size) -> torch.Tensor:
+    result = torch.stack(
+        (
+            torch.bitwise_and(packed_tensor[:, 0], 127),
+            torch.bitwise_and(packed_tensor[:, 1], 127),
+            torch.bitwise_and(packed_tensor[:, 2], 127),
+            torch.bitwise_and(packed_tensor[:, 3], 127),
+            torch.bitwise_and(packed_tensor[:, 4], 127),
+            torch.bitwise_and(packed_tensor[:, 5], 127),
+            torch.bitwise_and(packed_tensor[:, 6], 127),
+            torch.bitwise_or(
+                torch.bitwise_or(
+                    torch.bitwise_or(
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 0], 1), 64),
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 1], 2), 32),
+                    ),
+                    torch.bitwise_or(
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 2], 3), 16),
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 3], 4), 8),
+                    ),
+                ),
+                torch.bitwise_or(
+                    torch.bitwise_or(
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 4], 5), 4),
+                        torch.bitwise_and(torch.bitwise_right_shift(packed_tensor[:, 5], 6), 2),
+                    ),
+                    torch.bitwise_right_shift(packed_tensor[:, 6], 7),
+                ),
+            )
+        ),
+        dim=-1
+    ).reshape(shape)
+    return result
+
+
 def unpack_uint6(packed_tensor: torch.Tensor, shape: torch.Size) -> torch.Tensor:
     result = torch.stack(
         (
@@ -199,11 +253,13 @@ def unpack_uint2(packed_tensor: torch.Tensor, shape: torch.Size) -> torch.Tensor
 
 
 packed_int_function_dict = {
+    "int7": {"pack": pack_uint7, "unpack": unpack_uint7},
     "int6": {"pack": pack_uint6, "unpack": unpack_uint6},
     "int5": {"pack": pack_uint5, "unpack": unpack_uint5},
     "int4": {"pack": pack_uint4, "unpack": unpack_uint4},
     "int3": {"pack": pack_uint3, "unpack": unpack_uint3},
     "int2": {"pack": pack_uint2, "unpack": unpack_uint2},
+    "uint7": {"pack": pack_uint7, "unpack": unpack_uint7},
     "uint6": {"pack": pack_uint6, "unpack": unpack_uint6},
     "uint5": {"pack": pack_uint5, "unpack": unpack_uint5},
     "uint4": {"pack": pack_uint4, "unpack": unpack_uint4},
diff --git a/modules/shared.py b/modules/shared.py
index c05039363..0e7099c38 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -518,7 +518,7 @@ options_templates.update(options_section(("quantization", "Quantization Settings
     "sdnq_quantize_sep": OptionInfo("<h2>SDNQ: SD.Next Quantization</h2>", "", gr.HTML),
     "sdnq_quantize_weights": OptionInfo([], "Quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "Transformer", "VAE", "TE", "Video", "LLM", "ControlNet"], "visible": native}),
     "sdnq_quantize_mode": OptionInfo("pre", "Quantization mode", gr.Dropdown, {"choices": ["pre", "post"], "visible": native}),
-    "sdnq_quantize_weights_mode": OptionInfo("int8", "Quantization type", gr.Dropdown, {"choices": ["int8", "float8_e4m3fn", "int6", "int5", "uint4", "uint3", "uint2", "float8_e5m2", "float8_e4m3fnuz", "float8_e5m2fnuz", "uint8", "uint6", "uint5", "int4", "int3", "int2", "uint1"], "visible": native}),
+    "sdnq_quantize_weights_mode": OptionInfo("int8", "Quantization type", gr.Dropdown, {"choices": ["int8", "float8_e4m3fn", "int7", "int6", "int5", "uint4", "uint3", "uint2", "float8_e5m2", "float8_e4m3fnuz", "float8_e5m2fnuz", "uint8", "uint7", "uint6", "uint5", "int4", "int3", "int2", "uint1"], "visible": native}),
     "sdnq_quantize_weights_group_size": OptionInfo(0, "Group size", gr.Slider, {"minimum": -1, "maximum": 4096, "step": 1, "visible": native}),
     "sdnq_quantize_conv_layers": OptionInfo(False, "Quantize the convolutional layers", gr.Checkbox, {"visible": native}),
     "sdnq_decompress_compile": OptionInfo(devices.has_triton(), "Decompress using torch.compile", gr.Checkbox, {"visible": native}),
diff --git a/wiki b/wiki
index f01d19390..88b282df8 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit f01d19390603551f4713015f7842cae21e70f6f0
+Subproject commit 88b282df8d42ff396d5e925b5d167ffd6d791d7e