Update with configuration available in SwiftDiffusionCLI

swift/StableDiffusion/pipeline/NumPyRandomSource.swift

@@ -10,7 +10,7 @@ import CoreML
 ///  [NumPy's older randomkit.c](https://github.com/numpy/numpy/blob/v1.0/numpy/random/mtrand/randomkit.c)
 ///
 @available(iOS 16.2, macOS 13.1, *)
-struct NumPyRandomSource: RandomNumberGenerator {
+struct NumPyRandomSource: RandomNumberGenerator, RandomSource {
 
     struct State {
         var key = [UInt32](repeating: 0, count: 624)

swift/StableDiffusion/pipeline/RandomSource.swift

@@ -0,0 +1,6 @@
+import CoreML
+
+@available(iOS 16.2, macOS 13.1, *)
+public protocol RandomSource {
+    mutating func normalShapedArray(_ shape: [Int], mean: Double, stdev: Double) -> MLShapedArray<Double>
+}

swift/StableDiffusion/pipeline/StableDiffusionPipeline.Configuration.swift

@@ -37,6 +37,8 @@ extension StableDiffusionPipeline {
         public var disableSafety: Bool = false
         /// The type of Scheduler to use.
         public var schedulerType: StableDiffusionScheduler = .pndmScheduler
+        /// The type of RNG to use
+        public var rngType: StableDiffusionRNG = .numpyRNG
         
         /// Given the configuration, what mode will be used for generation
         public var mode: Mode {

swift/StableDiffusion/pipeline/StableDiffusionPipeline.swift

@@ -14,6 +14,14 @@ public enum StableDiffusionScheduler {
     case dpmSolverMultistepScheduler
 }
 
+/// RNG compatible with StableDiffusionPipeline
+public enum StableDiffusionRNG {
+    /// RNG that matches numpy implementation
+    case numpyRNG
+    /// RNG that matches PyTorch CPU implementation.
+    case torchRNG
+}
+
 /// A pipeline used to generate image samples from text input using stable diffusion
 ///
 /// This implementation matches:
@@ -157,7 +165,7 @@ public struct StableDiffusionPipeline: ResourceManaging {
                 throw Error.startingImageProvidedWithoutEncoder
             }
             
-            let noiseTuples = generateImage2ImageLatentSamples(config.imageCount, stdev: 1, seed: config.seed)
+            let noiseTuples = generateImage2ImageLatentSamples(config.imageCount, rng: config.rngType, stdev: 1, seed: config.seed)
             latents = try noiseTuples.map({
                 try encoder.encode(
                     image: startingImage,
@@ -168,7 +176,7 @@ public struct StableDiffusionPipeline: ResourceManaging {
         } else {
             timestepStrength = nil
             // Generate random latent samples from specified seed
-            latents = generateLatentSamples(config.imageCount, stdev: stdev, seed: config.seed)
+            latents = generateLatentSamples(config.imageCount, rng: config.rngType, stdev: stdev, seed: config.seed)
         }
 
         // De-noising loop
@@ -224,11 +232,19 @@ public struct StableDiffusionPipeline: ResourceManaging {
         return try decodeToImages(latents, disableSafety: config.disableSafety)
     }
 
-    func generateLatentSamples(_ count: Int, stdev: Float, seed: UInt32) -> [MLShapedArray<Float32>] {
+    private func randomSource(from rng: StableDiffusionRNG, seed: UInt32) -> RandomSource {
+        switch rng {
+        case .numpyRNG:
+            return NumPyRandomSource(seed: seed)
+        case .torchRNG:
+            return TorchRandomSource(seed: seed)
+        }
+    }
+
+    func generateLatentSamples(_ count: Int, rng: StableDiffusionRNG, stdev: Float, seed: UInt32) -> [MLShapedArray<Float32>] {
         var sampleShape = unet.latentSampleShape
         sampleShape[0] = 1
-
-        var random = NumPyRandomSource(seed: seed)
+        var random = randomSource(from: rng, seed: seed)
         let samples = (0..<count).map { _ in
             MLShapedArray<Float32>(
                 converting: random.normalShapedArray(sampleShape, mean: 0.0, stdev: Double(stdev)))
@@ -245,11 +261,11 @@ public struct StableDiffusionPipeline: ResourceManaging {
     ///   - diagonalAndLatentNoiseIsSame: Diffusions library does not seem to use the same noise for the `DiagonalGaussianDistribution` operation,
     ///     but I have seen implementations of pipelines where it is the same.
     /// - Returns: An array of tuples of noise values with length of batch size.
-    func generateImage2ImageLatentSamples(_ count: Int, stdev: Float, seed: UInt32, diagonalAndLatentNoiseIsSame: Bool = false) -> [(diagonal: MLShapedArray<Float32>, latentNoise: MLShapedArray<Float32>)] {
+    func generateImage2ImageLatentSamples(_ count: Int, rng: StableDiffusionRNG, stdev: Float, seed: UInt32, diagonalAndLatentNoiseIsSame: Bool = false) -> [(diagonal: MLShapedArray<Float32>, latentNoise: MLShapedArray<Float32>)] {
         var sampleShape = unet.latentSampleShape
         sampleShape[0] = 1
 
-        var random = NumPyRandomSource(seed: UInt32(truncatingIfNeeded: seed))
+        var random = randomSource(from: rng, seed: seed)
         let samples = (0..<count).map { _ in
             if diagonalAndLatentNoiseIsSame {
                 let noise = MLShapedArray<Float32>(

swift/StableDiffusion/pipeline/TorchRandomSource.swift

@@ -13,7 +13,7 @@ import CoreML
 ///  https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/core/TransformationHelper.h
 ///
 @available(iOS 16.2, macOS 13.1, *)
-struct TorchRandomSource: RandomNumberGenerator {
+struct TorchRandomSource: RandomNumberGenerator, RandomSource {
 
   struct State {
     var key = [UInt32](repeating: 0, count: 624)

swift/StableDiffusionCLI/main.swift

@@ -69,6 +69,9 @@ struct StableDiffusionSample: ParsableCommand {
     @Option(help: "Scheduler to use, one of {pndm, dpmpp}")
     var scheduler: SchedulerOption = .pndm
 
+    @Option(help: "Random number generator to use, one of {numpy, torch}")
+    var rng: RNGOption = .numpy
+
     @Flag(help: "Disable safety checking")
     var disableSafety: Bool = false
 
@@ -250,6 +253,17 @@ enum SchedulerOption: String, ExpressibleByArgument {
     }
 }
 
+@available(iOS 16.2, macOS 13.1, *)
+enum RNGOption: String, ExpressibleByArgument {
+    case numpy, torch
+    var stableDiffusionRNG: StableDiffusionRNG {
+        switch self {
+        case .numpy: return .numpyRNG
+        case .torch: return .torchRNG
+        }
+    }
+}
+
 if #available(iOS 16.2, macOS 13.1, *) {
     StableDiffusionSample.main()
 } else {