// // PipelineLoader.swift // Diffusion // // Created by Pedro Cuenca on December 2022. // See LICENSE at https://github.com/huggingface/swift-coreml-diffusers/LICENSE // import CoreML import Combine import Path import ZIPFoundation import StableDiffusion class PipelineLoader { static let models = Path.applicationSupport / "hf-diffusion-models" let model: ModelInfo let computeUnits: ComputeUnits let maxSeed: UInt32 private var downloadSubscriber: Cancellable? init(model: ModelInfo, computeUnits: ComputeUnits? = nil, maxSeed: UInt32 = UInt32.max) { self.model = model self.computeUnits = computeUnits ?? model.defaultComputeUnits self.maxSeed = maxSeed state = .undetermined setInitialState() } enum PipelinePreparationPhase { case undetermined case waitingToDownload case downloading(Double) case downloaded case uncompressing case readyOnDisk case loaded case failed(Error) } var state: PipelinePreparationPhase { didSet { statePublisher.value = state } } private(set) lazy var statePublisher: CurrentValueSubject = CurrentValueSubject(state) private(set) var downloader: Downloader? = nil func setInitialState() { if ready { state = .readyOnDisk return } if downloaded { state = .downloaded return } state = .waitingToDownload } } extension PipelineLoader { static func removeAll() { try? models.delete() } } extension PipelineLoader { func cancel() { downloader?.cancel() } } extension PipelineLoader { var url: URL { return model.modelURL(for: variant) } var filename: String { return url.lastPathComponent } var downloadedPath: Path { PipelineLoader.models / filename } var downloadedURL: URL { downloadedPath.url } var uncompressPath: Path { downloadedPath.parent } var packagesFilename: String { downloadedPath.basename(dropExtension: true) } var compiledPath: Path { downloadedPath.parent/packagesFilename } var downloaded: Bool { return downloadedPath.exists } var ready: Bool { return compiledPath.exists } var variant: AttentionVariant { switch computeUnits { case .cpuOnly : return .original // Not supported yet case .cpuAndGPU : return .original case .cpuAndNeuralEngine: return .splitEinsum case .all : return .splitEinsum @unknown default: fatalError("Unknown MLComputeUnits") } } // TODO: maybe receive Progress to add another progress as child func prepare() async throws -> Pipeline { do { try PipelineLoader.models.mkdir(.p) try await download() try await unzip() let pipeline = try await load(url: compiledPath.url) return Pipeline(pipeline, maxSeed: maxSeed) } catch { state = .failed(error) throw error } } @discardableResult func download() async throws -> URL { if ready || downloaded { return downloadedURL } let downloader = Downloader(from: url, to: downloadedURL) self.downloader = downloader downloadSubscriber = downloader.downloadState.sink { state in if case .downloading(let progress) = state { self.state = .downloading(progress) } } try downloader.waitUntilDone() return downloadedURL } func unzip() async throws { guard downloaded else { return } state = .uncompressing do { try FileManager().unzipItem(at: downloadedURL, to: uncompressPath.url) } catch { // Cleanup if error occurs while unzipping try uncompressPath.delete() throw error } try downloadedPath.delete() state = .readyOnDisk } func load(url: URL) async throws -> StableDiffusionPipeline { let beginDate = Date() let configuration = MLModelConfiguration() configuration.computeUnits = computeUnits let pipeline = try StableDiffusionPipeline(resourcesAt: url, configuration: configuration, disableSafety: false, reduceMemory: model.reduceMemory) print("Pipeline loaded in \(Date().timeIntervalSince(beginDate))") state = .loaded return pipeline } }