[Feature] Optimizations for JPEG input on NVIDIA GPU

python/sglang/srt/disaggregation/encode_server.py

@@ -435,8 +435,13 @@ def _load_single_item(
             return data
         try:
             if modality == Modality.IMAGE:
-                img, _ = load_image(data)
-                if discard_alpha_channel and img.mode != "RGB":
+                img, _ = load_image(data, False)
+                if (
+                    discard_alpha_channel
+                    and not isinstance(img, torch.Tensor)
+                    and img.mode != "RGB"
+                ):
+                    # Needed only when `img` is a PIL image
                     img = img.convert("RGB")
                 return img
             elif modality == Modality.VIDEO:

python/sglang/srt/multimodal/processors/base_processor.py

@@ -173,6 +173,7 @@ def get_combined_regex(self) -> re.Pattern:
 
 class BaseMultimodalProcessor(ABC):
     models = []
+    gpu_image_decode = True  # Enable GPU decoding by default
 
     def __init__(
         self, hf_config, server_args, _processor, transport_mode, *args, **kwargs
@@ -468,8 +469,9 @@ def get_estimated_frames_list(self, image_data):
 
         return estimated_frames_list
 
-    @staticmethod
+    @classmethod
     def _load_single_item(
+        cls,
         data,
         modality: Modality,
         frame_count_limit=None,
@@ -481,7 +483,8 @@ def _load_single_item(
 
         If data is processor_output or precomputed embedding, return directly.
 
-        Static method that can be pickled for multiprocessing"""
+        Class method that can be pickled for multiprocessing
+        """
         if isinstance(data, dict):
             data_format = data.get("format")
             if data_format in (
@@ -493,8 +496,13 @@ def _load_single_item(
                 return data
         try:
             if modality == Modality.IMAGE:
-                img, _ = load_image(data)
-                if discard_alpha_channel and img.mode != "RGB":
+                img, _ = load_image(data, cls.gpu_image_decode)
+                if (
+                    discard_alpha_channel
+                    and not isinstance(img, torch.Tensor)
+                    and img.mode != "RGB"
+                ):
+                    # Needed only when `img` is a PIL image
                     img = img.convert("RGB")
                 return img
             elif modality == Modality.VIDEO:
@@ -535,7 +543,7 @@ def _submit_mm_data_loading_tasks_simple(
                 type(data),
             )
             future = self.io_executor.submit(
-                BaseMultimodalProcessor._load_single_item,
+                self.__class__._load_single_item,
                 data,
                 modality,
                 None,  # frame_count_limit: no consider for fast path
@@ -595,7 +603,7 @@ def submit_data_loading_tasks(
 
                 futures.append(
                     self.io_executor.submit(
-                        BaseMultimodalProcessor._load_single_item,
+                        self.__class__._load_single_item,
                         data,
                         modality,
                         frame_count_limit,

python/sglang/srt/multimodal/processors/internvl.py

@@ -27,6 +27,7 @@
 
 class InternVLProcessor(BaseMultimodalProcessor):
     models = [InternVLChatModel, InternS1ForConditionalGeneration]
+    gpu_image_decode = False  # InternVL HF processor does not support tensor inputs
 
     IMAGENET_MEAN = [0.485, 0.456, 0.406]
     IMAGENET_STD = [0.229, 0.224, 0.225]

python/sglang/srt/multimodal/processors/kimi_k25.py

@@ -16,6 +16,7 @@
 # Compatible with KimiVLForConditionalGeneration
 class KimiK2_5VLImageProcessor(SGLangBaseProcessor):
     models = [KimiK25ForConditionalGeneration]
+    gpu_image_decode = False  # KimiK2.5VL HF processor does not support tensor inputs
 
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)

python/sglang/srt/multimodal/processors/kimi_vl.py

@@ -13,6 +13,7 @@
 # Compatible with KimiVLForConditionalGeneration
 class KimiVLImageProcessor(SGLangBaseProcessor):
     models = [KimiVLForConditionalGeneration]
+    gpu_image_decode = False  # KimiVL HF processor does not support tensor inputs
 
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)

python/sglang/srt/multimodal/processors/llava.py

@@ -33,6 +33,7 @@ class LlavaImageProcessor(BaseMultimodalProcessor):
         LlavaQwenForCausalLM,
         LlavaMistralForCausalLM,
     ]
+    gpu_image_decode = False  # Llava processes loaded image as PIL image explicitly
 
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)
@@ -49,7 +50,7 @@ def _process_single_image_task(
 
         try:
             url = image_data.url if isinstance(image_data, ImageData) else image_data
-            image, image_size = load_image(url)
+            image, image_size = load_image(url, False)
             if image_size is not None:
                 # It is a video with multiple images
                 image_hash = hash(url)

python/sglang/srt/multimodal/processors/minicpm.py

@@ -19,6 +19,7 @@
 class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
     models = [MiniCPMV, MiniCPMO]
     support_dynamic_frame_expansion = True
+    gpu_image_decode = False  # MiniCPM HF processor does not support tensor inputs
 
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)

python/sglang/srt/multimodal/processors/nano_nemotron_vl.py

@@ -35,6 +35,9 @@
 
 class NanoNemotronVLImageProcessor(BaseMultimodalProcessor):
     models = [NemotronH_Nano_VL_V2]
+    gpu_image_decode = (
+        False  # NanoNemotronVL processes loaded image as PIL image explicitly
+    )
 
     def __init__(self, hf_config, server_args, _image_processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _image_processor, *args, **kwargs)

python/sglang/srt/multimodal/processors/pixtral.py

@@ -19,6 +19,7 @@
 
 class PixtralProcessor(BaseMultimodalProcessor):
     models = [PixtralVisionModel, PixtralForConditionalGeneration]
+    gpu_image_decode = False  # Pixtral processes loaded image as PIL image explicitly
 
     PAD_TOKEN = "<pad>"
     DEFAULT_IMAGE_TOKEN = "[IMG]"

python/sglang/srt/utils/common.py

@@ -86,6 +86,7 @@
 from torch import nn
 from torch.library import Library
 from torch.utils._contextlib import _DecoratorContextManager
+from torchvision.io import decode_jpeg
 from typing_extensions import Literal
 
 from sglang.srt.environ import envs
@@ -763,64 +764,109 @@ class ImageData:
     max_dynamic_patch: Optional[int] = None
 
 
+image_extension_names = (".png", ".jpg", ".jpeg", ".webp", ".gif")
+
+
+def is_jpeg_with_cuda(image_bytes: bytes = b"", gpu_image_decode: bool = True) -> bool:
+    """
+    Check three conditions:
+    1. whether CUDA is available.
+    2. whether input is recognized as JPEG.
+    3. whether GPU image decode is enabled (some models such as CPM forcibly disable this).
+    """
+    if not is_cuda() or not gpu_image_decode:
+        return False
+    if image_bytes != b"":
+        return image_bytes.startswith(b"\xff\xd8") and image_bytes.endswith(b"\xff\xd9")
+    return False
+
+
+def _load_image(
+    image_bytes: bytes = b"",
+    image_file: str = "",
+    gpu_image_decode: bool = True,
+) -> Union[torch.Tensor, Image.Image]:
+    """
+    Try to decode JPEG with nvJPEG on GPU and return a torch device tensor,
+    otherwise fallback to decode with PIL on CPU and return a PIL Image.
+    Keep the fallback path since nvJPEG may fail on some JPEG images that are not strictly compliant with the standard, while PIL is more tolerant.
+    """
+    if image_file != "":
+        image_bytes = get_image_bytes(image_file)
+    if is_jpeg_with_cuda(image_bytes, gpu_image_decode):
+        try:
+            encoded_image = torch.frombuffer(image_bytes, dtype=torch.uint8)
+            image_tensor = decode_jpeg(encoded_image, device="cuda")
+            return image_tensor
+        except Exception as e:
+            logger.warning(
+                f"Failed to decode JPEG on GPU, falling back to CPU. Error: {e}"
+            )
+    return Image.open(BytesIO(image_bytes))
+
+
 def load_image(
     image_file: Union[Image.Image, str, ImageData, bytes],
-) -> tuple[Image.Image, tuple[int, int]]:
+    gpu_image_decode: bool = True,
+) -> tuple[Union[torch.Tensor, Image.Image], Optional[tuple[int, int]]]:
+    """
+    Load image from multiple input formats, including:
+    ImageData, PIL Image, bytes, URL, file path, or base64 string.
+    """
     if isinstance(image_file, ImageData):
         image_file = image_file.url
 
-    image = image_size = None
+    image = None
+    image_size: Optional[tuple[int, int]] = None
     if isinstance(image_file, Image.Image):
         image = image_file
         image_size = (image.width, image.height)
     elif isinstance(image_file, bytes):
-        image = Image.open(BytesIO(image_file))
-    elif image_file.startswith("http://") or image_file.startswith("https://"):
-        timeout = int(os.getenv("REQUEST_TIMEOUT", "3"))
-        response = requests.get(image_file, stream=True, timeout=timeout)
-        try:
-            response.raise_for_status()
-            image = Image.open(response.raw)
-            image.load()  # Force loading to avoid issues after closing the stream
-        finally:
-            response.close()
-    elif image_file.startswith("file://"):
-        image_file = unquote(urlparse(image_file).path)
-        image = Image.open(image_file)
-    elif image_file.lower().endswith(("png", "jpg", "jpeg", "webp", "gif")):
-        image = Image.open(image_file)
-    elif image_file.startswith("data:"):
-        image_file = image_file.split(",")[1]
-        image = Image.open(BytesIO(pybase64.b64decode(image_file, validate=True)))
-    elif isinstance(image_file, str):
-        image = Image.open(BytesIO(pybase64.b64decode(image_file, validate=True)))
+        image = _load_image(image_bytes=image_file, gpu_image_decode=gpu_image_decode)
+    elif isinstance(image_file, str) and image_file.startswith(("http://", "https://")):
+        image = _load_image(image_file=image_file, gpu_image_decode=gpu_image_decode)
+    elif isinstance(image_file, str) and image_file.startswith("file://"):
+        image = _load_image(
+            image_file=unquote(urlparse(image_file).path),
+            gpu_image_decode=gpu_image_decode,
+        )
+    elif isinstance(image_file, str) and image_file.lower().endswith(
+        image_extension_names
+    ):
+        image = _load_image(image_file=image_file, gpu_image_decode=gpu_image_decode)
+    elif isinstance(image_file, str) and image_file.startswith("data:"):
+        image = _load_image(image_file=image_file, gpu_image_decode=gpu_image_decode)
+    elif isinstance(
+        image_file, str
+    ):  # Other formats, try to decode as base64 by default
+        image = _load_image(image_file=image_file, gpu_image_decode=gpu_image_decode)
     else:
         raise ValueError(f"Invalid image: {image_file}")
-
     return image, image_size
 
 
-def get_image_bytes(image_file: Union[str, bytes]):
+def get_image_bytes(image_file: Union[str, bytes]) -> bytes:
+    """Normalize various image inputs into raw bytes."""
     if isinstance(image_file, bytes):
         return image_file
-    elif image_file.startswith("http://") or image_file.startswith("https://"):
+    if image_file.startswith(("http://", "https://")):
         timeout = int(os.getenv("REQUEST_TIMEOUT", "3"))
         response = requests.get(image_file, timeout=timeout)
-        return response.content
-    elif image_file.startswith("file://"):
-        image_file = unquote(urlparse(image_file).path)
-        with open(image_file, "rb") as f:
-            return f.read()
-    elif image_file.lower().endswith(("png", "jpg", "jpeg", "webp", "gif")):
+        try:
+            response.raise_for_status()
+            result = response.content
+        finally:
+            response.close()
+        return result
+    if image_file.startswith(("file://", "/")):
         with open(image_file, "rb") as f:
             return f.read()
-    elif image_file.startswith("data:"):
-        image_file = image_file.split(",")[1]
+    if isinstance(image_file, str) and image_file.startswith("data:"):
+        _, encoded = image_file.split(",", 1)
+        return pybase64.b64decode(encoded, validate=True)
+    if isinstance(image_file, str):
         return pybase64.b64decode(image_file, validate=True)
-    elif isinstance(image_file, str):
-        return pybase64.b64decode(image_file, validate=True)
-    else:
-        raise NotImplementedError(f"Invalid image: {image_file}")
+    raise NotImplementedError(f"Invalid image: {image_file}")
 
 
 def _normalize_video_input(