[Flux.1] improve pos embed for ascend npu by computing on npu

src/diffusers/models/transformers/transformer_flux.py

@@ -22,7 +22,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, is_torch_npu_available, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, 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
@@ -717,11 +717,7 @@ def forward(
             img_ids = img_ids[0]
 
         ids = torch.cat((txt_ids, img_ids), dim=0)
-        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)
+        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")

src/diffusers/models/transformers/transformer_flux2.py

@@ -21,7 +21,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, is_torch_npu_available, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin
 from ..attention_dispatch import dispatch_attention_fn
@@ -835,14 +835,8 @@ def forward(
         if txt_ids.ndim == 3:
             txt_ids = txt_ids[0]
 
-        if is_torch_npu_available():
-            freqs_cos_image, freqs_sin_image = self.pos_embed(img_ids.cpu())
-            image_rotary_emb = (freqs_cos_image.npu(), freqs_sin_image.npu())
-            freqs_cos_text, freqs_sin_text = self.pos_embed(txt_ids.cpu())
-            text_rotary_emb = (freqs_cos_text.npu(), freqs_sin_text.npu())
-        else:
-            image_rotary_emb = self.pos_embed(img_ids)
-            text_rotary_emb = self.pos_embed(txt_ids)
+        image_rotary_emb = self.pos_embed(img_ids)
+        text_rotary_emb = self.pos_embed(txt_ids)
         concat_rotary_emb = (
             torch.cat([text_rotary_emb[0], image_rotary_emb[0]], dim=0),
             torch.cat([text_rotary_emb[1], image_rotary_emb[1]], dim=0),

src/diffusers/models/transformers/transformer_longcat_image.py

@@ -21,7 +21,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import is_torch_npu_available, logging
+from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -499,11 +499,7 @@ def forward(
         encoder_hidden_states = self.context_embedder(encoder_hidden_states)
 
         ids = torch.cat((txt_ids, img_ids), dim=0)
-        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)
+        image_rotary_emb = self.pos_embed(ids)
 
         for index_block, block in enumerate(self.transformer_blocks):
             if torch.is_grad_enabled() and self.gradient_checkpointing and self.use_checkpoint[index_block]:

src/diffusers/models/transformers/transformer_ovis_image.py

@@ -21,7 +21,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import is_torch_npu_available, logging
+from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -530,11 +530,7 @@ def forward(
             img_ids = img_ids[0]
 
         ids = torch.cat((txt_ids, img_ids), dim=0)
-        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)
+        image_rotary_emb = self.pos_embed(ids)
 
         for index_block, block in enumerate(self.transformer_blocks):
             if torch.is_grad_enabled() and self.gradient_checkpointing: