Formatting, linting, and hopefully a CI build.

Sources/StableDiffusionCore/ModelState.swift

@@ -0,0 +1,52 @@
+import CoreML
+import Foundation
+import StableDiffusion
+import StableDiffusionProtos
+
+public actor ModelState {
+    private let url: URL
+    private var pipeline: StableDiffusionPipeline?
+    private var tokenizer: BPETokenizer?
+
+    public init(url: URL) throws {
+        self.url = url
+    }
+
+    public func load() throws {
+        let config = MLModelConfiguration()
+        config.computeUnits = .all
+        pipeline = try StableDiffusionPipeline(
+            resourcesAt: url,
+            controlNet: [],
+            configuration: config,
+            disableSafety: true,
+            reduceMemory: false
+        )
+        let mergesUrl = url.appending(component: "merges.txt")
+        let vocabUrl = url.appending(component: "vocab.json")
+        tokenizer = try BPETokenizer(mergesAt: mergesUrl, vocabularyAt: vocabUrl)
+    }
+
+    public func generate(_ request: SdGenerateImagesRequest) throws -> SdGenerateImagesResponse {
+        guard let pipeline else {
+            throw SdCoreError.modelNotLoaded
+        }
+
+        var pipelineConfig = StableDiffusionPipeline.Configuration(prompt: request.prompt)
+        pipelineConfig.negativePrompt = request.negativePrompt
+        pipelineConfig.seed = UInt32.random(in: 0 ..< UInt32.max)
+
+        var response = SdGenerateImagesResponse()
+        for _ in 0 ..< request.imageCount {
+            let images = try pipeline.generateImages(configuration: pipelineConfig)
+
+            for cgImage in images {
+                guard let cgImage else { continue }
+                var image = SdImage()
+                image.content = try cgImage.toPngData()
+                response.images.append(image)
+            }
+        }
+        return response
+    }
+}