ml-stable-diffusion/swift/StableDiffusion/pipeline/StableDiffusionPipeline+Resources.swift

// For licensing see accompanying LICENSE.md file.
// Copyright (C) 2022 Apple Inc. All Rights Reserved.

import Foundation
import CoreML

public extension StableDiffusionPipeline {

    /// Create stable diffusion pipeline using model resources at a
    /// specified URL
    ///
    /// - Parameters:
    ///    - baseURL: URL pointing to directory holding all model
    ///               and tokenization resources
    ///   - configuration: The configuration to load model resources with
    ///   - disableSafety: Load time disable of safety to save memory
    /// - Returns:
    ///  Pipeline ready for image generation if all  necessary resources loaded
    init(resourcesAt baseURL: URL,
         configuration config: MLModelConfiguration = .init(),
         disableSafety: Bool = false) throws {

        /// Expect URL of each resource
        let textEncoderURL = baseURL.appending(path: "TextEncoder.mlmodelc")
        let unetURL = baseURL.appending(path: "Unet.mlmodelc")
        let unetChunk1URL = baseURL.appending(path: "UnetChunk1.mlmodelc")
        let unetChunk2URL = baseURL.appending(path: "UnetChunk2.mlmodelc")
        let decoderURL = baseURL.appending(path: "VAEDecoder.mlmodelc")
        let safetyCheckerURL = baseURL.appending(path: "SafetyChecker.mlmodelc")
        let vocabURL = baseURL.appending(path: "vocab.json")
        let mergesURL = baseURL.appending(path: "merges.txt")

        // Text tokenizer and encoder
        let tokenizer = try BPETokenizer(mergesAt: mergesURL, vocabularyAt: vocabURL)
        let textEncoderModel = try MLModel(contentsOf: textEncoderURL, configuration: config)
        let textEncoder = TextEncoder(tokenizer: tokenizer, model:textEncoderModel )

        // Unet model
        let unet: Unet
        if FileManager.default.fileExists(atPath: unetChunk1URL.path) &&
            FileManager.default.fileExists(atPath: unetChunk2URL.path) {
            let chunk1 = try MLModel(contentsOf: unetChunk1URL, configuration: config)
            let chunk2 = try MLModel(contentsOf: unetChunk2URL, configuration: config)
            unet = Unet(chunks: [chunk1, chunk2])
        } else {
            let unetModel =  try MLModel(contentsOf: unetURL, configuration: config)
            unet = Unet(model: unetModel)
        }

        // Image Decoder
        let decoderModel = try MLModel(contentsOf: decoderURL, configuration: config)
        let decoder = Decoder(model: decoderModel)

        // Optional safety checker
        var safetyChecker: SafetyChecker? = nil
        if !disableSafety &&
            FileManager.default.fileExists(atPath: safetyCheckerURL.path) {
            let checkerModel = try MLModel(contentsOf: safetyCheckerURL, configuration: config)
            safetyChecker = SafetyChecker(model: checkerModel)
        }

        // Construct pipelien
        self.init(textEncoder: textEncoder,
                  unet: unet,
                  decoder: decoder,
                  safetyChecker: safetyChecker)
    }
}