Improve pos embed for Flux.1 inference on Ascend NPU (#12534)

improve pos embed for ascend npu
Co-authored-by: felix01.yu <felix01.yu@vipshop.com>

src/diffusers/models/transformers/transformer_flux.py

@@ -22,7 +22,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, is_torch_npu_available, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -717,7 +717,11 @@ class FluxTransformer2DModel(
             img_ids = img_ids[0]
 
         ids = torch.cat((txt_ids, img_ids), dim=0)
-        image_rotary_emb = self.pos_embed(ids)
+        if is_torch_npu_available():
+            freqs_cos, freqs_sin = self.pos_embed(ids.cpu())
+            image_rotary_emb = (freqs_cos.npu(), freqs_sin.npu())
+        else:
+            image_rotary_emb = self.pos_embed(ids)
 
         if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
             ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")