diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py
index 70892ecde9..b8140b11e9 100644
--- a/invokeai/app/invocations/generate.py
+++ b/invokeai/app/invocations/generate.py
@@ -4,6 +4,8 @@ from datetime import datetime, timezone
 from typing import Any, Literal, Optional, Union
 
 import numpy as np
+
+from torch import Tensor
 from PIL import Image
 from pydantic import Field
 from skimage.exposure.histogram_matching import match_histograms
@@ -12,7 +14,9 @@ from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
-from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
+from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator, Generator
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.util.util import image_to_dataURL
 
 SAMPLER_NAME_VALUES = Literal[
     tuple(InvokeAIGenerator.schedulers())
@@ -41,18 +45,32 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Any = None, step: int = 0
-    ) -> None:
+        self, context: InvocationContext, sample: Tensor, step: int
+    ) -> None:  
+        # TODO: only output a preview image when requested
+        image = Generator.sample_to_lowres_estimated_image(sample)
+
+        (width, height) = image.size
+        width *= 8
+        height *= 8
+
+        dataURL = image_to_dataURL(image, image_format="JPEG")
+
         context.services.events.emit_generator_progress(
             context.graph_execution_state_id,
             self.id,
+            {
+                "width": width,
+                "height": height,
+                "dataURL": dataURL
+            },
             step,
-            float(step) / float(self.steps),
+            self.steps,
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, step)
+        def step_callback(state: PipelineIntermediateState):
+            self.dispatch_progress(context, state.latents, state.step)
 
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py
index e2ab4e61e3..c8eb7671d0 100644
--- a/invokeai/app/services/events.py
+++ b/invokeai/app/services/events.py
@@ -1,7 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any, Dict
+from typing import Any, Dict, TypedDict
 
+ProgressImage = TypedDict(
+    "ProgressImage", {"dataURL": str, "width": int, "height": int}
+)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -23,8 +26,9 @@ class EventServiceBase:
         self,
         graph_execution_state_id: str,
         invocation_id: str,
+        progress_image: ProgressImage | None,
         step: int,
-        percent: float,
+        total_steps: int,
     ) -> None:
         """Emitted when there is generation progress"""
         self.__emit_session_event(
@@ -32,8 +36,9 @@ class EventServiceBase:
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
                 invocation_id=invocation_id,
+                progress_image=progress_image,
                 step=step,
-                percent=percent,
+                total_steps=total_steps,
             ),
         )