diff --git a/__init__.py b/__init__.py
index 77164d9..253e606 100644
--- a/__init__.py
+++ b/__init__.py
@@ -13,8 +13,9 @@ from .elevenlabs_tts import NODE_CLASS_MAPPINGS as ELEVENLABS_TTS_MAPPINGS, NODE
 from .flux2_replicate import NODE_CLASS_MAPPINGS as FLUX2_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as FLUX2_DISPLAY
 from .openai_tts import NODE_CLASS_MAPPINGS as OPENAI_TTS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as OPENAI_TTS_DISPLAY
 from .openai_node import NODE_CLASS_MAPPINGS as OPENAI_LLM_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as OPENAI_LLM_DISPLAY
+from .sora import NODE_CLASS_MAPPINGS as SORA_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as SORA_DISPLAY
 from .cleanup import NODE_CLASS_MAPPINGS as CLEANUP_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as CLEANUP_DISPLAY
 
-NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **GEMINI_MAPPINGS, **GEMINI_DIAR_MAPPINGS, **GPT_IMAGE_MAPPINGS, **IMAGEN_IMAGE_MAPPINGS, **IMAGEN_EDIT_MAPPINGS, **VEO_MAPPINGS, **VEO_GEMINI_MAPPINGS, **GEMINI_SEGMENT_MAPPINGS, **NANO_BANANA_MAPPINGS, **GEMINI_TTS_MAPPINGS, **ELEVENLABS_TTS_MAPPINGS, **FLUX2_MAPPINGS, **OPENAI_TTS_MAPPINGS, **OPENAI_LLM_MAPPINGS, **CLEANUP_MAPPINGS}
-NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **GEMINI_DISPLAY, **GEMINI_DIAR_DISPLAY, **GPT_IMAGE_DISPLAY, **IMAGEN_IMAGE_DISPLAY, **IMAGEN_EDIT_DISPLAY, **VEO_DISPLAY, **VEO_GEMINI_DISPLAY, **GEMINI_SEGMENT_DISPLAY, **NANO_BANANA_DISPLAY, **GEMINI_TTS_DISPLAY, **ELEVENLABS_TTS_DISPLAY, **FLUX2_DISPLAY, **OPENAI_TTS_DISPLAY, **OPENAI_LLM_DISPLAY, **CLEANUP_DISPLAY}
+NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **GEMINI_MAPPINGS, **GEMINI_DIAR_MAPPINGS, **GPT_IMAGE_MAPPINGS, **IMAGEN_IMAGE_MAPPINGS, **IMAGEN_EDIT_MAPPINGS, **VEO_MAPPINGS, **VEO_GEMINI_MAPPINGS, **GEMINI_SEGMENT_MAPPINGS, **NANO_BANANA_MAPPINGS, **GEMINI_TTS_MAPPINGS, **ELEVENLABS_TTS_MAPPINGS, **FLUX2_MAPPINGS, **OPENAI_TTS_MAPPINGS, **OPENAI_LLM_MAPPINGS, **SORA_MAPPINGS, **CLEANUP_MAPPINGS}
+NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **GEMINI_DISPLAY, **GEMINI_DIAR_DISPLAY, **GPT_IMAGE_DISPLAY, **IMAGEN_IMAGE_DISPLAY, **IMAGEN_EDIT_DISPLAY, **VEO_DISPLAY, **VEO_GEMINI_DISPLAY, **GEMINI_SEGMENT_DISPLAY, **NANO_BANANA_DISPLAY, **GEMINI_TTS_DISPLAY, **ELEVENLABS_TTS_DISPLAY, **FLUX2_DISPLAY, **OPENAI_TTS_DISPLAY, **OPENAI_LLM_DISPLAY, **SORA_DISPLAY, **CLEANUP_DISPLAY}
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
\ No newline at end of file
diff --git a/sora.py b/sora.py
new file mode 100644
index 0000000..fe16068
--- /dev/null
+++ b/sora.py
@@ -0,0 +1,87 @@
+import time
+import io
+import torch
+import numpy as np
+import av
+from PIL import Image
+from openai import OpenAI
+
+class Sora:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True, "default": "A calico cat playing a piano on stage"}),
+                "openai_api_key": ("STRING", {"multiline": False, "default": ""}),
+                "model": (["sora-2", "sora-2-pro"], {"default": "sora-2"}),
+                "size": (["720x1280", "1280x720", "1024x1792", "1792x1024"], {"default": "1280x720"}),
+                "duration": (["4", "8", "12"], {"default": "4"}),
+                "seed": ("INT", {"default": 69, "min": 1, "max": 2147483646, "step": 1}),
+            },
+            "optional": {"input_image": ("IMAGE",)}
+        }
+
+    RETURN_TYPES = ("IMAGE", "AUDIO")
+    RETURN_NAMES = ("frames", "audio")
+    FUNCTION = "gen_video"
+    CATEGORY = "OpenAI/Video"
+    OUTPUT_IS_LIST = (True, False)
+
+    def gen_video(self, prompt, openai_api_key, model, size, duration, seed, input_image=None):
+        client = OpenAI(api_key=openai_api_key)
+        api_args = {"prompt": prompt, "model": model, "size": size, "seconds": duration}
+        
+        img_buf = None
+        if input_image is not None:
+            img_buf = io.BytesIO()
+            # Convert Tensor (Batch, H, W, C) -> Numpy (H, W, C) -> PIL
+            arr = (input_image.cpu().numpy()[0] * 255).astype(np.uint8)
+            Image.fromarray(arr).save(img_buf, format="JPEG", quality=95)
+            img_buf.seek(0)
+            api_args["input_reference"] = ("ref.jpg", img_buf, "image/jpeg")
+
+        try:
+            job = client.videos.create(**api_args)
+            print(f"Job started: {job.id}")
+            
+            while (status := client.videos.retrieve(job.id)).status not in ["completed", "failed"]:
+                time.sleep(4)
+            
+            if status.status == "failed":
+                raise Exception(f"API Error: {status.error.message}")
+
+            video_bytes = io.BytesIO(client.videos.download_content(video_id=job.id).read())
+            
+            container = av.open(video_bytes)
+            frames = []
+            for frame in container.decode(video=0):
+                img = frame.to_rgb().to_ndarray().astype(np.float32) / 255.0
+                frames.append(torch.from_numpy(img).unsqueeze(0))
+            container.close()
+            
+            video_bytes.seek(0) 
+            container = av.open(video_bytes)
+            audio = None
+            
+            if container.streams.audio:
+                audio_data = [f.to_ndarray() for f in container.decode(audio=0)]
+                if audio_data:
+                    waveform = torch.from_numpy(np.concatenate(audio_data, axis=1)).float()
+                    # Normalize Audio
+                    if audio_data[0].dtype == np.int16: waveform /= 32768.0
+                    elif audio_data[0].dtype == np.int32: waveform /= 2147483648.0
+                    
+                    audio = {
+                        "waveform": waveform.unsqueeze(0), 
+                        "sample_rate": container.streams.audio[0].rate
+                    }
+            container.close()
+
+            return ([torch.cat(frames, dim=0)], audio)
+
+        finally:
+            if img_buf:
+                img_buf.close()
+
+NODE_CLASS_MAPPINGS = {"Sora": Sora}
+NODE_DISPLAY_NAME_MAPPINGS = {"Sora": "Sora 2 (OpenAI)"}
\ No newline at end of file