From 7eba007b5bef014fd8b5d4b4f6e5e5f3c2dad340 Mon Sep 17 00:00:00 2001
From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>
Date: Mon, 16 Dec 2024 16:05:35 +0100
Subject: [PATCH 1/3] Skip `task` for `diffusers` and `sentence-transformers`
 in `HuggingFaceHandler`

---
 src/huggingface_inference_toolkit/handler.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/huggingface_inference_toolkit/handler.py b/src/huggingface_inference_toolkit/handler.py
index 66f4acdc..33cfa011 100644
--- a/src/huggingface_inference_toolkit/handler.py
+++ b/src/huggingface_inference_toolkit/handler.py
@@ -36,9 +36,13 @@ def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
         inputs = data.pop("inputs", data)
         parameters = data.pop("parameters", {})
 
-        # sentence transformers pipelines do not have the `task` arg
-        if any(isinstance(self.pipeline, v) for v in SENTENCE_TRANSFORMERS_TASKS.values()):
-            return self.pipeline(**inputs) if isinstance(inputs, dict) else self.pipeline(inputs)  # type: ignore
+        # diffusers and sentence transformers pipelines do not have the `task` arg
+        if not hasattr(self.pipeline, "task"):
+            return (  # type: ignore
+                self.pipeline(**inputs, **parameters)
+                if isinstance(inputs, dict)
+                else self.pipeline(inputs, **parameters)
+            )
 
         if self.pipeline.task == "question-answering":
             if not isinstance(inputs, dict):

From 111b90007706cc492f4858ee344c69654b5f3667 Mon Sep 17 00:00:00 2001
From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>
Date: Mon, 16 Dec 2024 16:33:56 +0100
Subject: [PATCH 2/3] Remove unused `SENTENCE_TRANSFORMERS_TASKS` import

---
 src/huggingface_inference_toolkit/handler.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/huggingface_inference_toolkit/handler.py b/src/huggingface_inference_toolkit/handler.py
index 33cfa011..e241f727 100644
--- a/src/huggingface_inference_toolkit/handler.py
+++ b/src/huggingface_inference_toolkit/handler.py
@@ -3,7 +3,6 @@
 from typing import Any, Dict, Literal, Optional, Union
 
 from huggingface_inference_toolkit.const import HF_TRUST_REMOTE_CODE
-from huggingface_inference_toolkit.sentence_transformers_utils import SENTENCE_TRANSFORMERS_TASKS
 from huggingface_inference_toolkit.utils import (
     check_and_register_custom_pipeline_from_directory,
     get_pipeline,

From 8b7321ae212c819639a55ccbad9d4dfe4e610096 Mon Sep 17 00:00:00 2001
From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>
Date: Mon, 16 Dec 2024 16:51:13 +0100
Subject: [PATCH 3/3] Fix `HuggingFaceHandler` for `diffusers` and
 `sentence-transformers`

---
 src/huggingface_inference_toolkit/handler.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/huggingface_inference_toolkit/handler.py b/src/huggingface_inference_toolkit/handler.py
index e241f727..e3c721d1 100644
--- a/src/huggingface_inference_toolkit/handler.py
+++ b/src/huggingface_inference_toolkit/handler.py
@@ -3,6 +3,7 @@
 from typing import Any, Dict, Literal, Optional, Union
 
 from huggingface_inference_toolkit.const import HF_TRUST_REMOTE_CODE
+from huggingface_inference_toolkit.sentence_transformers_utils import SENTENCE_TRANSFORMERS_TASKS
 from huggingface_inference_toolkit.utils import (
     check_and_register_custom_pipeline_from_directory,
     get_pipeline,
@@ -37,6 +38,12 @@ def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
 
         # diffusers and sentence transformers pipelines do not have the `task` arg
         if not hasattr(self.pipeline, "task"):
+            # sentence transformers paramters not supported yet
+            if any(isinstance(self.pipeline, v) for v in SENTENCE_TRANSFORMERS_TASKS.values()):
+                return (  # type: ignore
+                    self.pipeline(**inputs) if isinstance(inputs, dict) else self.pipeline(inputs)
+                )
+            # diffusers does support kwargs
             return (  # type: ignore
                 self.pipeline(**inputs, **parameters)
                 if isinstance(inputs, dict)