diff --git a/src/nnsight/modeling/img2img_diffusion.py b/src/nnsight/modeling/img2img_diffusion.py
new file mode 100644
index 00000000..cec11707
--- /dev/null
+++ b/src/nnsight/modeling/img2img_diffusion.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
+
+import torch
+from diffusers import AutoPipelineForImage2Image
+from transformers import BatchEncoding
+from typing_extensions import Self
+from ..intervention.contexts import InterventionTracer
+
+from .. import util
+from .mixins import RemoteableMixin
+
+
+class Diffuser(util.WrapperModule):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__()
+
+        self.pipeline = AutoPipelineForImage2Image.from_pretrained(*args, **kwargs)
+        
+        for key, value in self.pipeline.__dict__.items():
+            if isinstance(value, torch.nn.Module):
+                setattr(self, key, value)
+
+        self.tokenizer = self.pipeline.tokenizer
+
+
+class Img2ImgDiffusionModel(RemoteableMixin):
+    
+    __methods__ = {"generate": "_generate"}
+
+    def __init__(self, *args, **kwargs) -> None:
+
+        self._model: Diffuser = None
+
+        super().__init__(*args, **kwargs)
+        
+    def _load_meta(self, repo_id:str, **kwargs):
+        
+        
+        model = Diffuser(
+            repo_id,
+            device_map=None,
+            low_cpu_mem_usage=False,
+            **kwargs,
+        )
+
+        return model
+        
+
+    def _load(self, repo_id: str, device_map=None, **kwargs) -> Diffuser:
+
+        model = Diffuser(repo_id, device_map=device_map, **kwargs)
+
+        return model
+
+    def _prepare_input(
+        self,
+        inputs: Union[str, List[str]],
+    ) -> Any:
+
+        if isinstance(inputs, str):
+            inputs = [inputs]
+
+        return ((inputs,), {}), len(inputs)
+
+    def _batch(
+        self,
+        batched_inputs: Optional[Dict[str, Any]],
+        prepared_inputs: BatchEncoding,
+    ) -> torch.Tensor:
+
+        if batched_inputs is None:
+
+            return ((prepared_inputs, ), {})
+
+        return (batched_inputs + prepared_inputs, )
+
+    def _execute(self, prepared_inputs: Any, *args, **kwargs):
+
+        return self._model.unet(
+            prepared_inputs,
+            *args,
+            **kwargs,
+        )
+
+    def _generate(
+        self, prepared_inputs: Any, *args, seed: int = None, **kwargs
+    ):
+
+        if self._scanning():
+
+            kwargs["num_inference_steps"] = 1
+
+        generator = torch.Generator()
+
+        if seed is not None:
+
+            if isinstance(prepared_inputs, list):
+                generator = [torch.Generator().manual_seed(seed) for _ in range(len(prepared_inputs))]
+            else:
+                generator = generator.manual_seed(seed)
+            
+        output = self._model.pipeline(
+            prepared_inputs, *args, generator=generator, **kwargs
+        )
+
+        output = self._model(output)
+
+        return output
+
+
+if TYPE_CHECKING:
+
+    class Img2ImgDiffusionModel(Img2ImgDiffusionModel, AutoPipelineForImage2Image):
+
+        def generate(self, *args, **kwargs) -> InterventionTracer:
+            return self._model.pipeline(*args, **kwargs)
+