diff --git a/06_gpu_and_ml/comfyui/comfyapp.py b/06_gpu_and_ml/comfyui/comfyapp.py
index 41ddc98da..bc3e0bd9f 100644
--- a/06_gpu_and_ml/comfyui/comfyapp.py
+++ b/06_gpu_and_ml/comfyui/comfyapp.py
@@ -197,24 +197,52 @@ def launch_comfy_background(self):
         subprocess.run(cmd, shell=True, check=True)
 
     @modal.method()
-    def infer(self, workflow_path: str = "/root/workflow_api.json"):
-        # runs the comfy run --workflow command as a subprocess
-        cmd = f"comfy run --workflow {workflow_path} --wait --timeout 1200"
-        subprocess.run(cmd, shell=True, check=True)
+    def infer(
+        self, client_id: str, workflow_path: str = "/root/workflow_api.json"
+    ):
+        import json
+        import time
+        import urllib
+
+        # reference locally running ComfyUI server
+        server_address = "127.0.0.1:8188"
 
         # completed workflows write output images to this directory
         output_dir = "/root/comfy/ComfyUI/output"
-        # looks up the name of the output image file based on the workflow
-        workflow = json.loads(Path(workflow_path).read_text())
-        file_prefix = [
-            node.get("inputs")
-            for node in workflow.values()
-            if node.get("class_type") == "SaveImage"
-        ][0]["filename_prefix"]
+
+        # adapted from https://github.com/comfyanonymous/ComfyUI/blob/master/script_examples/websockets_api_example.py
+        def queue_prompt(prompt):
+            # queues a workflow run (the workflow JSON is passed in as the "prompt" variable)
+            p = {"prompt": prompt, "client_id": client_id}
+            data = json.dumps(p).encode("utf-8")
+            req = urllib.request.Request(
+                "http://{}/prompt".format(server_address), data=data
+            )
+            return json.loads(urllib.request.urlopen(req).read())
+
+        def get_history(prompt_id):
+            # fetches the history of a queued prompt
+            with urllib.request.urlopen(
+                "http://{}/history/{}".format(server_address, prompt_id)
+            ) as response:
+                return json.loads(response.read())
+
+        # queue the workflow
+        with open(workflow_path, "r") as f:
+            workflow = json.load(f)
+        prompt_id = queue_prompt(workflow)["prompt_id"]
+        print(f"Running workflow with request id {client_id}...")
+
+        # poll the history endpoint every second until the workflow is complete
+        while True:
+            history = get_history(prompt_id)
+            if history:
+                break
+            time.sleep(1)
 
         # returns the image as bytes
         for f in Path(output_dir).iterdir():
-            if f.name.startswith(file_prefix):
+            if f.name.startswith(client_id):
                 return f.read_bytes()
 
     @modal.web_endpoint(method="POST")
@@ -237,7 +265,7 @@ def api(self, item: Dict):
         json.dump(workflow_data, Path(new_workflow_file).open("w"))
 
         # run inference on the currently running container
-        img_bytes = self.infer.local(new_workflow_file)
+        img_bytes = self.infer.local(client_id, new_workflow_file)
 
         return Response(img_bytes, media_type="image/jpeg")