[model][NPU] Add NPU fusion operator patch to Zimage model to improve performance

diffsynth/pipelines/z_image.py

@@ -6,7 +6,7 @@ from einops import rearrange
 import numpy as np
 from typing import Union, List, Optional, Tuple, Iterable, Dict
 
-from ..core.device.npu_compatible_device import get_device_type
+from ..core.device.npu_compatible_device import get_device_type, IS_NPU_AVAILABLE
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..core.data.operators import ImageCropAndResize
@@ -63,6 +63,7 @@ class ZImagePipeline(BasePipeline):
         model_configs: list[ModelConfig] = [],
         tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
         vram_limit: float = None,
+        enable_npu_patch: bool = True,
     ):
         # Initialize pipeline
         pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
@@ -84,6 +85,8 @@ class ZImagePipeline(BasePipeline):
         
         # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()
+        # NPU patch
+        apply_npu_patch(enable_npu_patch)
         return pipe
     
     
@@ -667,3 +670,19 @@ def model_fn_z_image_turbo(
     x = rearrange(x, "C B H W -> B C H W")
     x = -x
     return x
+
+
+def apply_npu_patch(enable_npu_patch: bool=True):
+    if IS_NPU_AVAILABLE and enable_npu_patch:
+        from ..models.general_modules import RMSNorm
+        from transformers.models.qwen3.modeling_qwen3 import Qwen3RMSNorm
+        from ..models.z_image_dit import Attention
+        from ..core.npu_patch.npu_fused_operator import (
+            rms_norm_forward_npu, 
+            rms_norm_forward_transformers_npu,
+            rotary_emb_Zimage_npu
+        )
+
+        RMSNorm.forward = rms_norm_forward_npu
+        Qwen3RMSNorm.forward = rms_norm_forward_transformers_npu
+        Attention.apply_rotary_emb = rotary_emb_Zimage_npu