diff --git a/riffusion/streamlit/util.py b/riffusion/streamlit/util.py
index c1fce73..7f4c865 100644
--- a/riffusion/streamlit/util.py
+++ b/riffusion/streamlit/util.py
@@ -2,6 +2,7 @@
 Streamlit utilities (mostly cached wrappers around riffusion code).
 """
 import io
+import threading
 import typing as T
 
 import pydub
@@ -106,6 +107,14 @@ def get_scheduler(scheduler: str, config: T.Any) -> T.Any:
         raise ValueError(f"Unknown scheduler {scheduler}")
 
 
+@st.experimental_singleton
+def pipeline_lock() -> threading.Lock:
+    """
+    Singleton lock used to prevent concurrent access to any model pipeline.
+    """
+    return threading.Lock()
+
+
 @st.experimental_singleton
 def load_stable_diffusion_img2img_pipeline(
     checkpoint: str = "riffusion/riffusion-model-v1",
@@ -149,22 +158,23 @@ def run_txt2img(
     """
     Run the text to image pipeline with caching.
     """
-    pipeline = load_stable_diffusion_pipeline(device=device, scheduler=scheduler)
+    with pipeline_lock():
+        pipeline = load_stable_diffusion_pipeline(device=device, scheduler=scheduler)
 
-    generator_device = "cpu" if device.lower().startswith("mps") else device
-    generator = torch.Generator(device=generator_device).manual_seed(seed)
+        generator_device = "cpu" if device.lower().startswith("mps") else device
+        generator = torch.Generator(device=generator_device).manual_seed(seed)
 
-    output = pipeline(
-        prompt=prompt,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance,
-        negative_prompt=negative_prompt or None,
-        generator=generator,
-        width=width,
-        height=height,
-    )
+        output = pipeline(
+            prompt=prompt,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance,
+            negative_prompt=negative_prompt or None,
+            generator=generator,
+            width=width,
+            height=height,
+        )
 
-    return output["images"][0]
+        return output["images"][0]
 
 
 @st.experimental_singleton
@@ -269,31 +279,32 @@ def run_img2img(
     scheduler: str = SCHEDULER_OPTIONS[0],
     progress_callback: T.Optional[T.Callable[[float], T.Any]] = None,
 ) -> Image.Image:
-    pipeline = load_stable_diffusion_img2img_pipeline(device=device, scheduler=scheduler)
+    with pipeline_lock():
+        pipeline = load_stable_diffusion_img2img_pipeline(device=device, scheduler=scheduler)
 
-    generator_device = "cpu" if device.lower().startswith("mps") else device
-    generator = torch.Generator(device=generator_device).manual_seed(seed)
+        generator_device = "cpu" if device.lower().startswith("mps") else device
+        generator = torch.Generator(device=generator_device).manual_seed(seed)
 
-    num_expected_steps = max(int(num_inference_steps * denoising_strength), 1)
+        num_expected_steps = max(int(num_inference_steps * denoising_strength), 1)
 
-    def callback(step: int, tensor: torch.Tensor, foo: T.Any) -> None:
-        if progress_callback is not None:
-            progress_callback(step / num_expected_steps)
+        def callback(step: int, tensor: torch.Tensor, foo: T.Any) -> None:
+            if progress_callback is not None:
+                progress_callback(step / num_expected_steps)
 
-    result = pipeline(
-        prompt=prompt,
-        image=init_image,
-        strength=denoising_strength,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        negative_prompt=negative_prompt or None,
-        num_images_per_prompt=1,
-        generator=generator,
-        callback=callback,
-        callback_steps=1,
-    )
+        result = pipeline(
+            prompt=prompt,
+            image=init_image,
+            strength=denoising_strength,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt or None,
+            num_images_per_prompt=1,
+            generator=generator,
+            callback=callback,
+            callback_steps=1,
+        )
 
-    return result.images[0]
+        return result.images[0]
 
 
 class StreamlitCounter: