Re-submit: Fix: Proper RGBA -> RGB conversion for PIL images.

benchmarks/benchmark_dataset.py

@@ -35,6 +35,7 @@
 from vllm.lora.request import LoRARequest
 from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.image import convert_image_mode
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
 
 logger = logging.getLogger(__name__)
@@ -257,7 +258,7 @@ def process_image(image: Any) -> Mapping[str, Any]:
     if isinstance(image, dict) and "bytes" in image:
         image = Image.open(BytesIO(image["bytes"]))
     if isinstance(image, Image.Image):
-        image = image.convert("RGB")
+        image = convert_image_mode(image, "RGB")
         with io.BytesIO() as image_data:
             image.save(image_data, format="JPEG")
             image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")

examples/offline_inference/qwen2_5_omni/only_thinker.py

@@ -11,6 +11,7 @@
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
+from vllm.multimodal.image import convert_image_mode
 from vllm.utils import FlexibleArgumentParser
 
 
@@ -45,7 +46,8 @@ def get_mixed_modalities_query() -> QueryResult:
                 "audio":
                 AudioAsset("mary_had_lamb").audio_and_sample_rate,
                 "image":
-                ImageAsset("cherry_blossom").pil_image.convert("RGB"),
+                convert_image_mode(
+                    ImageAsset("cherry_blossom").pil_image, "RGB"),
                 "video":
                 VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
             },

examples/offline_inference/vision_language.py

@@ -19,6 +19,7 @@
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
 from vllm.lora.request import LoRARequest
+from vllm.multimodal.image import convert_image_mode
 from vllm.utils import FlexibleArgumentParser
 
 
@@ -1096,8 +1097,8 @@ def get_multi_modal_input(args):
     """
     if args.modality == "image":
         # Input image and question
-        image = ImageAsset("cherry_blossom") \
-            .pil_image.convert("RGB")
+        image = convert_image_mode(
+            ImageAsset("cherry_blossom").pil_image, "RGB")
         img_questions = [
             "What is the content of this image?",
             "Describe the content of this image in detail.",

tests/models/multimodal/generation/test_interleaved.py

@@ -4,6 +4,7 @@
 
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
+from vllm.multimodal.image import convert_image_mode
 
 models = ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"]
 
@@ -26,8 +27,9 @@ def test_models(vllm_runner, model, dtype: str, max_tokens: int) -> None:
     give the same result.
     """
 
-    image_cherry = ImageAsset("cherry_blossom").pil_image.convert("RGB")
-    image_stop = ImageAsset("stop_sign").pil_image.convert("RGB")
+    image_cherry = convert_image_mode(
+        ImageAsset("cherry_blossom").pil_image, "RGB")
+    image_stop = convert_image_mode(ImageAsset("stop_sign").pil_image, "RGB")
     images = [image_cherry, image_stop]
     video = VideoAsset(name="baby_reading", num_frames=16).np_ndarrays
 

tests/models/multimodal/generation/test_phi4mm.py

@@ -12,7 +12,7 @@
 
 from vllm.assets.image import ImageAsset
 from vllm.lora.request import LoRARequest
-from vllm.multimodal.image import rescale_image_size
+from vllm.multimodal.image import convert_image_mode, rescale_image_size
 from vllm.platforms import current_platform
 from vllm.sequence import SampleLogprobs
 
@@ -267,7 +267,7 @@ def test_vision_speech_models(hf_runner, vllm_runner, model, dtype: str,
 
     # use the example speech question so that the model outputs are reasonable
     audio = librosa.load(speech_question, sr=None)
-    image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+    image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
 
     inputs_vision_speech = [
         (

tests/models/test_oot_registration.py

@@ -4,6 +4,7 @@
 
 from vllm import LLM, SamplingParams
 from vllm.assets.image import ImageAsset
+from vllm.multimodal.image import convert_image_mode
 
 from ..utils import create_new_process_for_each_test
 
@@ -58,7 +59,7 @@ def test_oot_registration_embedding(
             assert all(v == 0 for v in output.outputs.embedding)
 
 
-image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+image = convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB")
 
 
 @create_new_process_for_each_test()

tests/multimodal/test_image.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageChops
+
+from vllm.multimodal.image import convert_image_mode
+
+ASSETS_DIR = Path(__file__).parent / "assets"
+assert ASSETS_DIR.exists()
+
+
+def test_rgb_to_rgb():
+    # Start with an RGB image.
+    original_image = Image.open(ASSETS_DIR / "image1.png").convert("RGB")
+    converted_image = convert_image_mode(original_image, "RGB")
+
+    # RGB to RGB should be a no-op.
+    diff = ImageChops.difference(original_image, converted_image)
+    assert diff.getbbox() is None
+
+
+def test_rgba_to_rgb():
+    original_image = Image.open(ASSETS_DIR / "rgba.png")
+    original_image_numpy = np.array(original_image)
+
+    converted_image = convert_image_mode(original_image, "RGB")
+    converted_image_numpy = np.array(converted_image)
+
+    for i in range(original_image_numpy.shape[0]):
+        for j in range(original_image_numpy.shape[1]):
+            # Verify that all transparent pixels are converted to white.
+            if original_image_numpy[i][j][3] == 0:
+                assert converted_image_numpy[i][j][0] == 255
+                assert converted_image_numpy[i][j][1] == 255
+                assert converted_image_numpy[i][j][2] == 255

tests/multimodal/test_utils.py

@@ -10,6 +10,7 @@
 import pytest
 from PIL import Image, ImageChops
 
+from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import PlaceholderRange
 from vllm.multimodal.utils import (MediaConnector,
                                    merge_and_sort_multimodal_metadata)
@@ -53,7 +54,7 @@ def get_supported_suffixes() -> tuple[str, ...]:
 
 
 def _image_equals(a: Image.Image, b: Image.Image) -> bool:
-    return (np.asarray(a) == np.asarray(b.convert(a.mode))).all()
+    return (np.asarray(a) == np.asarray(convert_image_mode(b, a.mode))).all()
 
 
 @pytest.mark.asyncio

vllm/benchmarks/datasets.py

@@ -13,7 +13,6 @@
 TODO: Implement CustomDataset to parse a JSON file and convert its contents into
 SampleRequest instances, similar to the approach used in ShareGPT.
 """
-
 import base64
 import io
 import json
@@ -33,6 +32,7 @@
 from vllm.lora.request import LoRARequest
 from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.image import convert_image_mode
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
 
 logger = logging.getLogger(__name__)
@@ -259,7 +259,7 @@ def process_image(image: Any) -> Mapping[str, Any]:
     if isinstance(image, dict) and 'bytes' in image:
         image = Image.open(BytesIO(image['bytes']))
     if isinstance(image, Image.Image):
-        image = image.convert("RGB")
+        image = convert_image_mode(image, "RGB")
         with io.BytesIO() as image_data:
             image.save(image_data, format="JPEG")
             image_base64 = base64.b64encode(

vllm/model_executor/models/internvl.py

@@ -23,6 +23,7 @@
                                                    InternVisionPatchModel)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargs, NestedTensors)
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
@@ -77,7 +78,7 @@ class InternVLImageEmbeddingInputs(TypedDict):
 def build_transform(input_size: int):
     MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
     return T.Compose([
-        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
         T.Resize((input_size, input_size),
                  interpolation=T.InterpolationMode.BICUBIC),
         T.ToTensor(),

vllm/model_executor/models/skyworkr1v.py

@@ -24,6 +24,7 @@
                                                    InternVisionPatchModel)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargs, NestedTensors)
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
@@ -78,7 +79,7 @@ class SkyworkR1VImageEmbeddingInputs(TypedDict):
 def build_transform(input_size: int):
     MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
     return T.Compose([
-        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
         T.Resize((input_size, input_size),
                  interpolation=T.InterpolationMode.BICUBIC),
         T.ToTensor(),

vllm/multimodal/hasher.py

@@ -10,6 +10,7 @@
 from PIL import Image
 
 from vllm.logger import init_logger
+from vllm.multimodal.image import convert_image_mode
 
 if TYPE_CHECKING:
     from vllm.inputs import TokensPrompt
@@ -35,7 +36,8 @@ def serialize_item(cls, obj: object) -> bytes:
             return np.array(obj).tobytes()
 
         if isinstance(obj, Image.Image):
-            return cls.item_to_bytes("image", np.array(obj.convert("RGBA")))
+            return cls.item_to_bytes("image",
+                                     np.array(convert_image_mode(obj, "RGBA")))
         if isinstance(obj, torch.Tensor):
             return cls.item_to_bytes("tensor", obj.numpy())
         if isinstance(obj, np.ndarray):

vllm/multimodal/image.py

@@ -22,6 +22,25 @@ def rescale_image_size(image: Image.Image,
     return image
 
 
+# TODO: Support customizable background color to fill in.
+def rgba_to_rgb(
+    image: Image.Image, background_color=(255, 255, 255)) -> Image.Image:
+    """Convert an RGBA image to RGB with filled background color."""
+    assert image.mode == "RGBA"
+    converted = Image.new("RGB", image.size, background_color)
+    converted.paste(image, mask=image.split()[3])  # 3 is the alpha channel
+    return converted
+
+
+def convert_image_mode(image: Image.Image, to_mode: str):
+    if image.mode == to_mode:
+        return image
+    elif image.mode == "RGBA" and to_mode == "RGB":
+        return rgba_to_rgb(image)
+    else:
+        return image.convert(to_mode)
+
+
 class ImageMediaIO(MediaIO[Image.Image]):
 
     def __init__(self, *, image_mode: str = "RGB") -> None:
@@ -32,15 +51,15 @@ def __init__(self, *, image_mode: str = "RGB") -> None:
     def load_bytes(self, data: bytes) -> Image.Image:
         image = Image.open(BytesIO(data))
         image.load()
-        return image.convert(self.image_mode)
+        return convert_image_mode(image, self.image_mode)
 
     def load_base64(self, media_type: str, data: str) -> Image.Image:
         return self.load_bytes(base64.b64decode(data))
 
     def load_file(self, filepath: Path) -> Image.Image:
         image = Image.open(filepath)
         image.load()
-        return image.convert(self.image_mode)
+        return convert_image_mode(image, self.image_mode)
 
     def encode_base64(
         self,
@@ -51,7 +70,7 @@ def encode_base64(
         image = media
 
         with BytesIO() as buffer:
-            image = image.convert(self.image_mode)
+            image = convert_image_mode(image, self.image_mode)
             image.save(buffer, image_format)
             data = buffer.getvalue()
 

vllm/transformers_utils/processors/ovis.py

@@ -33,6 +33,8 @@
                                            Unpack)
 from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
 
+from vllm.multimodal.image import convert_image_mode
+
 __all__ = ['OvisProcessor']
 IGNORE_ID = -100
 
@@ -361,8 +363,8 @@ def _get_best_grid(img, side):
                 # pick the partition with maximum covering_ratio and break the tie using #sub_images
                 return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
 
-        if convert_to_rgb and image.mode != 'RGB':
-            image = image.convert('RGB')
+        if convert_to_rgb:
+            image = convert_image_mode(image, 'RGB')
 
 
         sides = self.get_image_size()