Rename Lumina(2)Text2ImgPipeline -> Lumina(2)Pipeline

docs/source/en/api/pipelines/lumina.md

@@ -58,10 +58,10 @@ Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fa
 First, load the pipeline:
 
 ```python
-from diffusers import LuminaText2ImgPipeline
+from diffusers import LuminaPipeline
 import torch
 
-pipeline = LuminaText2ImgPipeline.from_pretrained(
+pipeline = LuminaPipeline.from_pretrained(
 	"Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
 ).to("cuda")
 ```
@@ -86,11 +86,11 @@ image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit w
 
 Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
 
-Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`LuminaText2ImgPipeline`] for inference with bitsandbytes.
+Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`LuminaPipeline`] for inference with bitsandbytes.
 
 ```py
 import torch
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, Transformer2DModel, LuminaText2ImgPipeline
+from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, Transformer2DModel, LuminaPipeline
 from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel
 
 quant_config = BitsAndBytesConfig(load_in_8bit=True)
@@ -109,7 +109,7 @@ transformer_8bit = Transformer2DModel.from_pretrained(
     torch_dtype=torch.float16,
 )
 
-pipeline = LuminaText2ImgPipeline.from_pretrained(
+pipeline = LuminaPipeline.from_pretrained(
     "Alpha-VLLM/Lumina-Next-SFT-diffusers",
     text_encoder=text_encoder_8bit,
     transformer=transformer_8bit,
@@ -122,9 +122,9 @@ image = pipeline(prompt).images[0]
 image.save("lumina.png")
 ```
 
-## LuminaText2ImgPipeline
+## LuminaPipeline
 
-[[autodoc]] LuminaText2ImgPipeline
+[[autodoc]] LuminaPipeline
 	- all
 	- __call__
 

docs/source/en/api/pipelines/lumina2.md

@@ -36,14 +36,14 @@ Single file loading for Lumina Image 2.0 is available for the `Lumina2Transforme
 
 ```python
 import torch
-from diffusers import Lumina2Transformer2DModel, Lumina2Text2ImgPipeline
+from diffusers import Lumina2Transformer2DModel, Lumina2Pipeline
 
 ckpt_path = "https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0/blob/main/consolidated.00-of-01.pth"
 transformer = Lumina2Transformer2DModel.from_single_file(
     ckpt_path, torch_dtype=torch.bfloat16
 )
 
-pipe = Lumina2Text2ImgPipeline.from_pretrained(
+pipe = Lumina2Pipeline.from_pretrained(
     "Alpha-VLLM/Lumina-Image-2.0", transformer=transformer, torch_dtype=torch.bfloat16
 )
 pipe.enable_model_cpu_offload()
@@ -60,7 +60,7 @@ image.save("lumina-single-file.png")
 GGUF Quantized checkpoints for the `Lumina2Transformer2DModel` can be loaded via `from_single_file` with the `GGUFQuantizationConfig` 
 
 ```python
-from diffusers import Lumina2Transformer2DModel, Lumina2Text2ImgPipeline, GGUFQuantizationConfig 
+from diffusers import Lumina2Transformer2DModel, Lumina2Pipeline, GGUFQuantizationConfig 
 
 ckpt_path = "https://huggingface.co/calcuis/lumina-gguf/blob/main/lumina2-q4_0.gguf"
 transformer = Lumina2Transformer2DModel.from_single_file(
@@ -69,7 +69,7 @@ transformer = Lumina2Transformer2DModel.from_single_file(
     torch_dtype=torch.bfloat16,
 )
 
-pipe = Lumina2Text2ImgPipeline.from_pretrained(
+pipe = Lumina2Pipeline.from_pretrained(
     "Alpha-VLLM/Lumina-Image-2.0", transformer=transformer, torch_dtype=torch.bfloat16
 )
 pipe.enable_model_cpu_offload()
@@ -80,8 +80,8 @@ image = pipe(
 image.save("lumina-gguf.png")
 ```
 
-## Lumina2Text2ImgPipeline
+## Lumina2Pipeline
 
-[[autodoc]] Lumina2Text2ImgPipeline
+[[autodoc]] Lumina2Pipeline
   - all
   - __call__

scripts/convert_lumina_to_diffusers.py

@@ -5,7 +5,7 @@
 from safetensors.torch import load_file
 from transformers import AutoModel, AutoTokenizer
 
-from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline
+from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaPipeline
 
 
 def main(args):
@@ -115,7 +115,7 @@ def main(args):
         tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
         text_encoder = AutoModel.from_pretrained("google/gemma-2b")
 
-        pipeline = LuminaText2ImgPipeline(
+        pipeline = LuminaPipeline(
             tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler
         )
         pipeline.save_pretrained(args.dump_path)

src/diffusers/__init__.py

@@ -349,7 +349,9 @@
             "LEditsPPPipelineStableDiffusionXL",
             "LTXImageToVideoPipeline",
             "LTXPipeline",
+            "Lumina2Pipeline",
             "Lumina2Text2ImgPipeline",
+            "LuminaPipeline",
             "LuminaText2ImgPipeline",
             "MarigoldDepthPipeline",
             "MarigoldIntrinsicsPipeline",
@@ -859,7 +861,9 @@
             LEditsPPPipelineStableDiffusionXL,
             LTXImageToVideoPipeline,
             LTXPipeline,
+            Lumina2Pipeline,
             Lumina2Text2ImgPipeline,
+            LuminaPipeline,
             LuminaText2ImgPipeline,
             MarigoldDepthPipeline,
             MarigoldIntrinsicsPipeline,

src/diffusers/pipelines/__init__.py

@@ -261,8 +261,8 @@
     )
     _import_structure["latte"] = ["LattePipeline"]
     _import_structure["ltx"] = ["LTXPipeline", "LTXImageToVideoPipeline"]
-    _import_structure["lumina"] = ["LuminaText2ImgPipeline"]
-    _import_structure["lumina2"] = ["Lumina2Text2ImgPipeline"]
+    _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
+    _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
     _import_structure["marigold"].extend(
         [
             "MarigoldDepthPipeline",
@@ -611,8 +611,8 @@
             LEditsPPPipelineStableDiffusionXL,
         )
         from .ltx import LTXImageToVideoPipeline, LTXPipeline
-        from .lumina import LuminaText2ImgPipeline
-        from .lumina2 import Lumina2Text2ImgPipeline
+        from .lumina import LuminaPipeline, LuminaText2ImgPipeline
+        from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
         from .marigold import (
             MarigoldDepthPipeline,
             MarigoldIntrinsicsPipeline,

src/diffusers/pipelines/auto_pipeline.py

@@ -69,8 +69,8 @@
 )
 from .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline
 from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
-from .lumina import LuminaText2ImgPipeline
-from .lumina2 import Lumina2Text2ImgPipeline
+from .lumina import LuminaPipeline
+from .lumina2 import Lumina2Pipeline
 from .pag import (
     HunyuanDiTPAGPipeline,
     PixArtSigmaPAGPipeline,
@@ -141,8 +141,8 @@
         ("flux", FluxPipeline),
         ("flux-control", FluxControlPipeline),
         ("flux-controlnet", FluxControlNetPipeline),
-        ("lumina", LuminaText2ImgPipeline),
-        ("lumina2", Lumina2Text2ImgPipeline),
+        ("lumina", LuminaPipeline),
+        ("lumina2", Lumina2Pipeline),
         ("cogview3", CogView3PlusPipeline),
         ("cogview4", CogView4Pipeline),
     ]

src/diffusers/pipelines/lumina/__init__.py

@@ -22,7 +22,7 @@
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["pipeline_lumina"] = ["LuminaText2ImgPipeline"]
+    _import_structure["pipeline_lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -32,7 +32,7 @@
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
-        from .pipeline_lumina import LuminaText2ImgPipeline
+        from .pipeline_lumina import LuminaPipeline, LuminaText2ImgPipeline
 
 else:
     import sys

src/diffusers/pipelines/lumina/pipeline_lumina.py

@@ -30,6 +30,7 @@
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
     BACKENDS_MAPPING,
+    deprecate,
     is_bs4_available,
     is_ftfy_available,
     is_torch_xla_available,
@@ -60,11 +61,9 @@
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import LuminaText2ImgPipeline
+        >>> from diffusers import LuminaPipeline
 
-        >>> pipe = LuminaText2ImgPipeline.from_pretrained(
-        ...     "Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
-        ... )
+        >>> pipe = LuminaPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16)
         >>> # Enable memory optimizations.
         >>> pipe.enable_model_cpu_offload()
 
@@ -134,7 +133,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class LuminaText2ImgPipeline(DiffusionPipeline):
+class LuminaPipeline(DiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Lumina-T2I.
 
@@ -932,3 +931,23 @@ def __call__(
             return (image,)
 
         return ImagePipelineOutput(images=image)
+
+
+class LuminaText2ImgPipeline(LuminaPipeline):
+    def __init__(
+        self,
+        transformer: LuminaNextDiT2DModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: GemmaPreTrainedModel,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
+    ):
+        deprecation_message = "`LuminaText2ImgPipeline` has been renamed to `LuminaPipeline` and will be removed in a future version. Please use `LuminaPipeline` instead."
+        deprecate("diffusers.pipelines.lumina.pipeline_lumina.LuminaText2ImgPipeline", "0.34", deprecation_message)
+        super().__init__(
+            transformer=transformer,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+        )

src/diffusers/pipelines/lumina2/__init__.py

@@ -22,7 +22,7 @@
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["pipeline_lumina2"] = ["Lumina2Text2ImgPipeline"]
+    _import_structure["pipeline_lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -32,7 +32,7 @@
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
-        from .pipeline_lumina2 import Lumina2Text2ImgPipeline
+        from .pipeline_lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
 
 else:
     import sys

src/diffusers/pipelines/lumina2/pipeline_lumina2.py

@@ -25,6 +25,7 @@
 from ...models.transformers.transformer_lumina2 import Lumina2Transformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -47,9 +48,9 @@
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import Lumina2Text2ImgPipeline
+        >>> from diffusers import Lumina2Pipeline
 
-        >>> pipe = Lumina2Text2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", torch_dtype=torch.bfloat16)
+        >>> pipe = Lumina2Pipeline.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", torch_dtype=torch.bfloat16)
         >>> # Enable memory optimizations.
         >>> pipe.enable_model_cpu_offload()
 
@@ -133,7 +134,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class Lumina2Text2ImgPipeline(DiffusionPipeline, Lumina2LoraLoaderMixin):
+class Lumina2Pipeline(DiffusionPipeline, Lumina2LoraLoaderMixin):
     r"""
     Pipeline for text-to-image generation using Lumina-T2I.
 
@@ -767,3 +768,23 @@ def __call__(
             return (image,)
 
         return ImagePipelineOutput(images=image)
+
+
+class Lumina2Text2ImgPipeline(Lumina2Pipeline):
+    def __init__(
+        self,
+        transformer: Lumina2Transformer2DModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: Gemma2PreTrainedModel,
+        tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
+    ):
+        deprecation_message = "`Lumina2Text2ImgPipeline` has been renamed to `Lumina2Pipeline` and will be removed in a future version. Please use `Lumina2Pipeline` instead."
+        deprecate("diffusers.pipelines.lumina2.pipeline_lumina2.Lumina2Text2ImgPipeline", "0.34", deprecation_message)
+        super().__init__(
+            transformer=transformer,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+        )

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1217,6 +1217,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class Lumina2Pipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class Lumina2Text2ImgPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 
@@ -1232,6 +1247,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class LuminaPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class LuminaText2ImgPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]