From c6947fffd9b6c906447ce36673e15848411e4b1b Mon Sep 17 00:00:00 2001
From: Giovanny Moreno <gio@orchidmedia.io>
Date: Thu, 18 Jun 2026 11:13:47 -0500
Subject: [PATCH] feat: make GPT-Image-2 nodes async for parallel execution
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ComfyUI's executor (execution.py) detects async node functions via
inspect.iscoroutinefunction() and wraps them in asyncio.create_task()
instead of awaiting sequentially. This allows multiple API nodes at the
same graph depth to fire their HTTP calls simultaneously.

Changes:
- Add `import asyncio` to nodes.py
- KIE_GPTImage2_TextToImage.generate: def → async def
- KIE_GPTImage2_ImageToImage.generate: def → async def
- Both now use asyncio.to_thread() to run the blocking HTTP job in a
  thread pool, allowing true concurrent I/O (GIL released for network)

Result: N independent GPT-Image-2 nodes in a workflow run in parallel
instead of sequentially. For 5 ad generation nodes this yields ~5x
wall-clock speedup. KIE API supports 100+ concurrent tasks and 20
requests/10s, so the server side is not a bottleneck.

No behaviour change for single-node workflows.
---
 nodes.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/nodes.py b/nodes.py
index a15a390..e76561e 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,3 +1,4 @@
+import asyncio
 import json
 import os
 import time
@@ -383,7 +384,7 @@ def INPUT_TYPES(cls):
     FUNCTION = "generate"
     CATEGORY = "kie/api"
 
-    def generate(
+    async def generate(
         self,
         prompt: str,
         aspect_ratio: str = "auto",
@@ -395,7 +396,8 @@ def generate(
         max_retries: int = 2,
         retry_backoff_s: float = 3.0,
     ):
-        image_tensor = run_gpt_image2_text_to_image(
+        image_tensor = await asyncio.to_thread(
+            run_gpt_image2_text_to_image,
             prompt=prompt,
             aspect_ratio=aspect_ratio,
             resolution=resolution,
@@ -451,7 +453,7 @@ def INPUT_TYPES(cls):
     FUNCTION = "generate"
     CATEGORY = "kie/api"
 
-    def generate(
+    async def generate(
         self,
         prompt: str,
         images: torch.Tensor,
@@ -464,7 +466,8 @@ def generate(
         max_retries: int = 2,
         retry_backoff_s: float = 3.0,
     ):
-        image_tensor = run_gpt_image2_image_to_image(
+        image_tensor = await asyncio.to_thread(
+            run_gpt_image2_image_to_image,
             prompt=prompt,
             images=images,
             aspect_ratio=aspect_ratio,