Switched back to slerp for latents interpolation

app.py

@@ -45,6 +45,17 @@ def InitializeOutpainting():
         pipeline = StableDiffusionInpaintPipeline.from_pretrained(modelNames[modelIndex])
                                                                 #safety_checker=lambda images, **kwargs: (images, False))
 
+# Based on: https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/4
+# Further optimized to trade a divide operation for a multiply
+def slerp(start, end, alpha):
+    start_norm = torch.norm(start, dim=1, keepdim=True)
+    end_norm = torch.norm(end, dim=1, keepdim=True)
+    omega = torch.acos((start*end/(start_norm*end_norm)).sum(1))
+    sinOmega = torch.sin(omega)
+    first = torch.sin((1.0-alpha)*omega)/sinOmega
+    second = torch.sin(alpha*omega)/sinOmega
+    return first.unsqueeze(1)*start + second.unsqueeze(1)*end
+
 def diffuse(latentWalk, staticLatents, generatorSeed, inputImage, mask, pauseInference, prompt, negativePrompt, guidanceScale, numInferenceSteps):
     global lastImage, lastSeed, generator, oldLatentWalk, activeLatents
 
@@ -55,7 +66,7 @@ def diffuse(latentWalk, staticLatents, generatorSeed, inputImage, mask, pauseInf
         GenerateNewLatentsForInference()
 
     if oldLatentWalk != latentWalk:
-        activeLatents = torch.lerp(oldLatents, latents, latentWalk)
+        activeLatents = slerp(oldLatents, latents, latentWalk)
         oldLatentWalk = latentWalk
     
     if lastSeed != generatorSeed: