Lock to prevent concurrent access to model pipeline in playground

This should avoid the most basic conflict with multiple users on
the app at one time. Seems to work with initial testing.

Topic: pipeline_lock

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)
-
-    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,
-    )
+    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)
+
+        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)
-
-    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)
-
-    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,
-    )
-
-    return result.images[0]
+    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)
+
+        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)
+
+        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]
 
 
 class StreamlitCounter: