Attept 1 to fix tests.

tests/lora/test_lora_layers_af.py

@@ -15,13 +15,19 @@
 import sys
 import unittest
 
+import torch
 from transformers import AutoTokenizer, T5EncoderModel
 
 from diffusers import (
     AuraFlowPipeline,
+    AuraFlowTransformer2DModel,
     FlowMatchEulerDiscreteScheduler,
 )
-from diffusers.utils.testing_utils import is_peft_available, require_peft_backend
+from diffusers.utils.testing_utils import (
+    floats_tensor,
+    is_peft_available,
+    require_peft_backend,
+)
 
 
 if is_peft_available():
@@ -49,8 +55,9 @@ class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
         "joint_attention_dim": 32,
         "caption_projection_dim": 32,
         "out_channels": 4,
-        "pos_embed_max_size": 32,
+        "pos_embed_max_size": 64,
     }
+    transformer_cls = AuraFlowTransformer2DModel
     tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
     text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
 
@@ -71,3 +78,26 @@ class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     @property
     def output_shape(self):
         return (1, 64, 64, 3)
+
+    def get_dummy_inputs(self, with_generator=True):
+        batch_size = 1
+        sequence_length = 10
+        num_channels = 4
+        sizes = (32, 32)
+
+        generator = torch.manual_seed(0)
+        noise = floats_tensor((batch_size, num_channels) + sizes)
+        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
+
+        pipeline_inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "num_inference_steps": 4,
+            "guidance_scale": 0.0,
+            "height": 8,
+            "width": 8,
+            "output_type": "np",
+        }
+        if with_generator:
+            pipeline_inputs.update({"generator": generator})
+
+        return noise, input_ids, pipeline_inputs