[Inference Providers] add image-to-image support for Replicate provider

docs/source/en/guides/inference.md

@@ -218,7 +218,7 @@ For more details, refer to the [Inference Providers pricing documentation](https
 | [`~InferenceClient.fill_mask`]                      | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.image_classification`]           | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.image_segmentation`]             | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
-| [`~InferenceClient.image_to_image`]                 | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
+| [`~InferenceClient.image_to_image`]                 | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ✅         | ❌         | ❌        |
 | [`~InferenceClient.image_to_text`]                  | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |
 | [`~InferenceClient.object_detection`]               | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |  | ❌ |
 | [`~InferenceClient.question_answering`]             | ❌                 | ❌        | ❌      | ❌      | ❌              | ❌            | ❌    | ✅            | ❌          | ❌                | ❌         | ❌         | ❌         | ❌        |

src/huggingface_hub/inference/_client.py

@@ -1338,6 +1338,7 @@ def image_to_image(
             api_key=self.token,
         )
         response = self._inner_post(request_parameters)
+        response = provider_helper.get_response(response, request_parameters)
         return _bytes_to_image(response)
 
     def image_to_text(self, image: ContentT, *, model: Optional[str] = None) -> ImageToTextOutput:

src/huggingface_hub/inference/_common.py

@@ -18,6 +18,7 @@
 import io
 import json
 import logging
+import mimetypes
 from contextlib import contextmanager
 from dataclasses import dataclass
 from pathlib import Path
@@ -197,6 +198,17 @@ def _b64_encode(content: ContentT) -> str:
         return base64.b64encode(data_as_bytes).decode()
 
 
+def _as_url(content: ContentT, default_mime_type: str) -> str:
+    if isinstance(content, str) and (content.startswith("https://") or content.startswith("http://")):
+        return content
+
+    mime_type = (
+        mimetypes.guess_type(content, strict=False)[0] if isinstance(content, (str, Path)) else None
+    ) or default_mime_type
+    encoded_data = _b64_encode(content)
+    return f"data:{mime_type};base64,{encoded_data}"
+
+
 def _b64_to_image(encoded_image: str) -> "Image":
     """Parse a base64-encoded string into a PIL Image."""
     Image = _import_pil_image()

src/huggingface_hub/inference/_generated/_async_client.py

@@ -1384,6 +1384,7 @@ async def image_to_image(
             api_key=self.token,
         )
         response = await self._inner_post(request_parameters)
+        response = provider_helper.get_response(response, request_parameters)
         return _bytes_to_image(response)
 
     async def image_to_text(self, image: ContentT, *, model: Optional[str] = None) -> ImageToTextOutput:

src/huggingface_hub/inference/_providers/__init__.py

@@ -34,7 +34,7 @@
 from .novita import NovitaConversationalTask, NovitaTextGenerationTask, NovitaTextToVideoTask
 from .nscale import NscaleConversationalTask, NscaleTextToImageTask
 from .openai import OpenAIConversationalTask
-from .replicate import ReplicateTask, ReplicateTextToImageTask, ReplicateTextToSpeechTask
+from .replicate import ReplicateImageToImageTask, ReplicateTask, ReplicateTextToImageTask, ReplicateTextToSpeechTask
 from .sambanova import SambanovaConversationalTask, SambanovaFeatureExtractionTask
 from .together import TogetherConversationalTask, TogetherTextGenerationTask, TogetherTextToImageTask
 
@@ -141,6 +141,7 @@
         "conversational": OpenAIConversationalTask(),
     },
     "replicate": {
+        "image-to-image": ReplicateImageToImageTask(),
         "text-to-image": ReplicateTextToImageTask(),
         "text-to-speech": ReplicateTextToSpeechTask(),
         "text-to-video": ReplicateTask("text-to-video"),

src/huggingface_hub/inference/_providers/replicate.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional, Union
 
 from huggingface_hub.hf_api import InferenceProviderMapping
-from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._common import RequestParameters, _as_dict, _as_url
 from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
 from huggingface_hub.utils import get_session
 
@@ -70,3 +70,21 @@ def _prepare_payload_as_dict(
         payload: Dict = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)  # type: ignore[assignment]
         payload["input"]["text"] = payload["input"].pop("prompt")  # rename "prompt" to "text" for TTS
         return payload
+
+
+class ReplicateImageToImageTask(ReplicateTask):
+    def __init__(self):
+        super().__init__("image-to-image")
+
+    def _prepare_payload_as_dict(
+        self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
+    ) -> Optional[Dict]:
+        image_url = _as_url(inputs, default_mime_type="image/jpeg")
+
+        payload: Dict[str, Any] = {"input": {"input_image": image_url, **filter_none(parameters)}}
+
+        mapped_model = provider_mapping_info.provider_id
+        if ":" in mapped_model:
+            version = mapped_model.split(":", 1)[1]
+            payload["version"] = version
+        return payload

tests/test_inference_client.py

@@ -47,7 +47,11 @@
 )
 from huggingface_hub.errors import HfHubHTTPError, ValidationError
 from huggingface_hub.inference._client import _open_as_binary
-from huggingface_hub.inference._common import _stream_chat_completion_response, _stream_text_generation_response
+from huggingface_hub.inference._common import (
+    _as_url,
+    _stream_chat_completion_response,
+    _stream_text_generation_response,
+)
 from huggingface_hub.inference._providers import get_provider_helper
 from huggingface_hub.inference._providers.hf_inference import _build_chat_completion_url
 
@@ -1163,3 +1167,28 @@ def test_chat_completion_url_resolution(
         assert request_params.url == expected_request_url
         assert request_params.json is not None
         assert request_params.json.get("model") == expected_payload_model
+
+
+@pytest.mark.parametrize(
+    "content_input, default_mime_type, expected, is_exact_match",
+    [
+        ("https://my-url.com/cat.gif", "image/jpeg", "https://my-url.com/cat.gif", True),
+        ("assets/image.png", "image/jpeg", "data:image/png;base64,", False),
+        (Path("assets/image.png"), "image/jpeg", "data:image/png;base64,", False),
+        ("assets/image.foo", "image/jpeg", "data:image/jpeg;base64,", False),
+        (b"some image bytes", "image/jpeg", "data:image/jpeg;base64,c29tZSBpbWFnZSBieXRlcw==", True),
+        (io.BytesIO(b"some image bytes"), "image/jpeg", "data:image/jpeg;base64,c29tZSBpbWFnZSBieXRlcw==", True),
+    ],
+)
+def test_as_url(content_input, default_mime_type, expected, is_exact_match, tmp_path: Path):
+    if isinstance(content_input, (str, Path)) and not str(content_input).startswith("http"):
+        file_path = tmp_path / content_input
+        file_path.parent.mkdir(exist_ok=True, parents=True)
+        file_path.touch()
+        content_input = file_path
+
+    result = _as_url(content_input, default_mime_type)
+    if is_exact_match:
+        assert result == expected
+    else:
+        assert result.startswith(expected)

tests/test_inference_providers.py

@@ -42,7 +42,11 @@
 from huggingface_hub.inference._providers.novita import NovitaConversationalTask, NovitaTextGenerationTask
 from huggingface_hub.inference._providers.nscale import NscaleConversationalTask, NscaleTextToImageTask
 from huggingface_hub.inference._providers.openai import OpenAIConversationalTask
-from huggingface_hub.inference._providers.replicate import ReplicateTask, ReplicateTextToSpeechTask
+from huggingface_hub.inference._providers.replicate import (
+    ReplicateImageToImageTask,
+    ReplicateTask,
+    ReplicateTextToSpeechTask,
+)
 from huggingface_hub.inference._providers.sambanova import SambanovaConversationalTask, SambanovaFeatureExtractionTask
 from huggingface_hub.inference._providers.together import TogetherTextToImageTask
 
@@ -1057,6 +1061,44 @@ def test_get_response_single_output(self, mocker):
         mock.return_value.get.assert_called_once_with("https://example.com/image.jpg")
         assert response == mock.return_value.get.return_value.content
 
+    def test_image_to_image_payload(self):
+        helper = ReplicateImageToImageTask()
+        dummy_image = b"dummy image data"
+        encoded_image = base64.b64encode(dummy_image).decode("utf-8")
+        image_uri = f"data:image/jpeg;base64,{encoded_image}"
+
+        # No model version
+        payload = helper._prepare_payload_as_dict(
+            dummy_image,
+            {"num_inference_steps": 20},
+            InferenceProviderMapping(
+                provider="replicate",
+                hf_model_id="google/gemini-pro-vision",
+                providerId="google/gemini-pro-vision",
+                task="image-to-image",
+                status="live",
+            ),
+        )
+        assert payload == {
+            "input": {"input_image": image_uri, "num_inference_steps": 20},
+        }
+
+        payload = helper._prepare_payload_as_dict(
+            dummy_image,
+            {"num_inference_steps": 20},
+            InferenceProviderMapping(
+                provider="replicate",
+                hf_model_id="google/gemini-pro-vision",
+                providerId="google/gemini-pro-vision:123456",
+                task="image-to-image",
+                status="live",
+            ),
+        )
+        assert payload == {
+            "input": {"input_image": image_uri, "num_inference_steps": 20},
+            "version": "123456",
+        }
+
 
 class TestSambanovaProvider:
     def test_prepare_url_conversational(self):