NPU adaption for FLUX

examples/controlnet/train_controlnet_flux.py

@@ -473,9 +473,6 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
     )
-    parser.add_argument(
-        "--enable_npu_flash_attention", action="store_true", help="Whether or not to use npu flash attention."
-    )
     parser.add_argument(
         "--set_grads_to_none",
         action="store_true",
@@ -970,13 +967,6 @@ def load_model_hook(models, input_dir):
         accelerator.register_save_state_pre_hook(save_model_hook)
         accelerator.register_load_state_pre_hook(load_model_hook)
 
-    if args.enable_npu_flash_attention:
-        if is_torch_npu_available():
-            logger.info("npu flash attention enabled.")
-            flux_transformer.enable_npu_flash_attention()
-        else:
-            raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu devices.")
-
     if args.enable_xformers_memory_efficient_attention:
         if is_xformers_available():
             import xformers

src/diffusers/models/transformers/transformer_flux.py

@@ -29,6 +29,7 @@
     FluxAttnProcessor2_0,
     FluxAttnProcessor2_0_NPU,
     FusedFluxAttnProcessor2_0,
+    FusedFluxAttnProcessor2_0_NPU,
 )
 from ...models.modeling_utils import ModelMixin
 from ...models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
@@ -140,7 +141,10 @@ def __init__(
         self.norm1_context = AdaLayerNormZero(dim)
 
         if hasattr(F, "scaled_dot_product_attention"):
-            processor = FluxAttnProcessor2_0()
+            if is_torch_npu_available():
+                processor = FluxAttnProcessor2_0_NPU()
+            else:
+                processor = FluxAttnProcessor2_0()
         else:
             raise ValueError(
                 "The current PyTorch version does not support the `scaled_dot_product_attention` function."
@@ -405,7 +409,10 @@ def fuse_qkv_projections(self):
             if isinstance(module, Attention):
                 module.fuse_projections(fuse=True)
 
-        self.set_attn_processor(FusedFluxAttnProcessor2_0())
+        if is_torch_npu_available():
+            self.set_attn_processor(FusedFluxAttnProcessor2_0_NPU())
+        else:
+            self.set_attn_processor(FusedFluxAttnProcessor2_0())
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
     def unfuse_qkv_projections(self):