fix

my_pipeline.py

@@ -27,12 +27,7 @@ from diffusers.loaders import (
 from diffusers.models import AutoencoderKL
 from .scheduler.my_scheduler import MyScheduler
 from .unet.my_unet_model import MyUNetModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
+from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.utils import (
     USE_PEFT_BACKEND,
@@ -136,6 +131,7 @@ class MyPipeline(
             watermark output images. If not defined, it will default to True if the package is installed, otherwise no
             watermarker will be used.
     """
+
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
     _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
 
@@ -572,12 +568,7 @@ class MyPipeline(
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                LoRAXFormersAttnProcessor,
-                LoRAAttnProcessor2_0,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory
@@ -972,4 +963,4 @@ class MyPipeline(
         # Offload all models
         self.maybe_free_model_hooks()
 
-        return (image,)
+        return (image,)