[Inference Providers] LoRAs with Replicate

src/huggingface_hub/inference/_providers/__init__.py

@@ -23,7 +23,7 @@
 from .nebius import NebiusConversationalTask, NebiusTextGenerationTask, NebiusTextToImageTask
 from .novita import NovitaConversationalTask, NovitaTextGenerationTask, NovitaTextToVideoTask
 from .openai import OpenAIConversationalTask
-from .replicate import ReplicateTask, ReplicateTextToSpeechTask
+from .replicate import ReplicateTask, ReplicateTextToImageTask, ReplicateTextToSpeechTask
 from .sambanova import SambanovaConversationalTask, SambanovaFeatureExtractionTask
 from .together import TogetherConversationalTask, TogetherTextGenerationTask, TogetherTextToImageTask
 
@@ -115,7 +115,7 @@
         "conversational": OpenAIConversationalTask(),
     },
     "replicate": {
-        "text-to-image": ReplicateTask("text-to-image"),
+        "text-to-image": ReplicateTextToImageTask(),
         "text-to-speech": ReplicateTextToSpeechTask(),
         "text-to-video": ReplicateTask("text-to-video"),
     },

src/huggingface_hub/inference/_providers/replicate.py

@@ -47,6 +47,19 @@ def get_response(self, response: Union[bytes, Dict], request_params: Optional[Re
         return get_session().get(output_url).content
 
 
+class ReplicateTextToImageTask(ReplicateTask):
+    def __init__(self):
+        super().__init__("text-to-image")
+
+    def _prepare_payload_as_dict(
+        self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
+    ) -> Optional[Dict]:
+        payload: Dict = super()._prepare_payload_as_dict(inputs, parameters, provider_mapping_info)  # type: ignore[assignment]
+        if provider_mapping_info.adapter_weights_path is not None:
+            payload["input"]["lora_weights"] = f"https://huggingface.co/{provider_mapping_info.hf_model_id}"
+        return payload
+
+
 class ReplicateTextToSpeechTask(ReplicateTask):
     def __init__(self):
         super().__init__("text-to-speech")