fix deprecations

Author: sayakpaul

examples/dreambooth/train_dreambooth_lora_lumina2.py

@@ -48,7 +48,7 @@
 from diffusers import (
     AutoencoderKL,
     FlowMatchEulerDiscreteScheduler,
-    Lumina2Text2ImgPipeline,
+    Lumina2Pipeline,
     Lumina2Transformer2DModel,
 )
 from diffusers.optimization import get_scheduler
@@ -898,7 +898,7 @@ def main(args):
         cur_class_images = len(list(class_images_dir.iterdir()))
 
         if cur_class_images < args.num_class_images:
-            pipeline = Lumina2Text2ImgPipeline.from_pretrained(
+            pipeline = Lumina2Pipeline.from_pretrained(
                 args.pretrained_model_name_or_path,
                 torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
                 revision=args.revision,
@@ -990,7 +990,7 @@ def main(args):
     text_encoder.to(dtype=torch.bfloat16)
 
     # Initialize a text encoding pipeline and keep it to CPU for now.
-    text_encoding_pipeline = Lumina2Text2ImgPipeline.from_pretrained(
+    text_encoding_pipeline = Lumina2Pipeline.from_pretrained(
         args.pretrained_model_name_or_path,
         vae=None,
         transformer=None,
@@ -1034,7 +1034,7 @@ def save_model_hook(models, weights, output_dir):
                 # make sure to pop weight so that corresponding model is not saved again
                 weights.pop()
 
-            Lumina2Text2ImgPipeline.save_lora_weights(
+            Lumina2Pipeline.save_lora_weights(
                 output_dir,
                 transformer_lora_layers=transformer_lora_layers_to_save,
             )
@@ -1050,7 +1050,7 @@ def load_model_hook(models, input_dir):
             else:
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
-        lora_state_dict = Lumina2Text2ImgPipeline.lora_state_dict(input_dir)
+        lora_state_dict = Lumina2Pipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
             f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
@@ -1473,7 +1473,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
         if accelerator.is_main_process:
             if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
                 # create pipeline
-                pipeline = Lumina2Text2ImgPipeline.from_pretrained(
+                pipeline = Lumina2Pipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
                     transformer=accelerator.unwrap_model(transformer),
                     revision=args.revision,
@@ -1503,14 +1503,14 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
             transformer = transformer.to(weight_dtype)
         transformer_lora_layers = get_peft_model_state_dict(transformer)
 
-        Lumina2Text2ImgPipeline.save_lora_weights(
+        Lumina2Pipeline.save_lora_weights(
             save_directory=args.output_dir,
             transformer_lora_layers=transformer_lora_layers,
         )
 
         # Final inference
         # Load previous pipeline
-        pipeline = Lumina2Text2ImgPipeline.from_pretrained(
+        pipeline = Lumina2Pipeline.from_pretrained(
             args.pretrained_model_name_or_path,
             revision=args.revision,
             variant=args.variant,

tests/lora/test_lora_layers_lumina2.py

@@ -23,7 +23,7 @@
 from diffusers import (
     AutoencoderKL,
     FlowMatchEulerDiscreteScheduler,
-    Lumina2Text2ImgPipeline,
+    Lumina2Pipeline,
     Lumina2Transformer2DModel,
 )
 from diffusers.utils.testing_utils import floats_tensor, is_torch_version, require_peft_backend, skip_mps, torch_device
@@ -36,7 +36,7 @@
 
 @require_peft_backend
 class Lumina2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
-    pipeline_class = Lumina2Text2ImgPipeline
+    pipeline_class = Lumina2Pipeline
     scheduler_cls = FlowMatchEulerDiscreteScheduler
     scheduler_classes = [FlowMatchEulerDiscreteScheduler]
     scheduler_kwargs = {}

tests/pipelines/lumina/test_lumina_nextdit.py

@@ -10,7 +10,6 @@
     FlowMatchEulerDiscreteScheduler,
     LuminaNextDiT2DModel,
     LuminaPipeline,
-    LuminaText2ImgPipeline,
 )
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
@@ -107,7 +106,7 @@ def test_xformers_attention_forwardGenerator_pass(self):
 
     def test_deprecation_raises_warning(self):
         with self.assertWarns(FutureWarning) as warning:
-            _ = LuminaText2ImgPipeline(**self.get_dummy_components()).to(torch_device)
+            _ = LuminaPipeline(**self.get_dummy_components()).to(torch_device)
         warning_message = str(warning.warnings[0].message)
         assert "renamed to `LuminaPipeline`" in warning_message