/** * Comprehensive TDD Test Suite for Imagen4 Generator * * Following London School TDD with emphasis on performance assertions, * image generation pipeline coordination, and quality validation. * * RED-GREEN-REFACTOR CYCLE: * Focus on AI-powered image generation, performance optimization, * batch processing coordination, and quality assessment. */ import { describe, it, expect, beforeEach, afterEach, jest, } from "@jest/globals"; import { EventEmitter } from "events"; import { Imagen4Generator } from "../imagen4-generator.js"; import { MockFactory, TestDataGenerator, MockBuilder, ContractTester, PerformanceTester, PropertyGenerator, } from "./test-utilities.js"; // Mock external dependencies following London School principles jest.mock("../../../utils/logger.js"); jest.mock("../../../ai/image-ai-engine.js"); jest.mock("../../../rendering/gpu-accelerator.js"); describe("Imagen4Generator - London School TDD with Performance Focus", () => { let imagen4Generator: Imagen4Generator; let mockConfig: any; let mockLogger: jest.Mocked; let mockImageAIEngine: jest.Mocked; let mockGPUAccelerator: jest.Mocked; let mockRenderingPipeline: jest.Mocked; let mockQualityAssessor: jest.Mocked; let mockStorageManager: jest.Mocked; let mockPerformanceMonitor: jest.Mocked; let mockBatchProcessor: jest.Mocked; let mockBuilder: MockBuilder; beforeEach(() => { // Setup comprehensive mock configuration for image generation mockConfig = { ai: { model: "imagen4-ultra-v2", maxResolution: { width: 4096, height: 4096 }, supportedFormats: ["png", "jpg", "webp", "tiff", "bmp"], qualityLevels: ["draft", "standard", "high", "ultra"], features: { textToImage: true, imageToImage: true, inpainting: true, outpainting: true, styleTransfer: true, superResolution: true, }, performance: { gpuMemoryOptimization: true, batchOptimization: true, caching: true, streaming: false, }, }, generation: { defaultSteps: 50, maxSteps: 100, guidanceScale: 7.5, seed: -1, // Random seed sampler: "ddim", negativePrompt: "blurry, low quality, distorted", aspectRatios: { square: "1:1", portrait: "3:4", landscape: "4:3", widescreen: "16:9", cinema: "21:9", }, }, performance: { targetLatency: 5000, // 5 seconds maxConcurrentGenerations: 4, gpuMemoryLimit: 12288, // 12GB batchSize: 4, cacheEnabled: true, preloadModels: true, optimization: { precisionMode: "fp16", tensorrtOptimization: true, dynamicBatching: true, memoryPooling: true, }, }, quality: { assessmentEnabled: true, minQualityScore: 0.7, metrics: [ "sharpness", "color_accuracy", "composition", "prompt_adherence", ], enhancement: { autoCorrection: true, noiseReduction: true, colorBalancing: true, contrastOptimization: true, }, }, storage: { basePath: "/tmp/imagen4", compression: "lossless", metadata: true, versioning: true, cleanup: { enabled: true, retentionDays: 30, maxStorageGB: 100, }, }, batch: { enabled: true, maxBatchSize: 16, parallelBatches: 2, queueSize: 100, priorityLevels: 5, scheduling: "priority_first", }, }; mockBuilder = new MockBuilder(); // Setup Logger mock mockLogger = mockBuilder .mockFunction("info", jest.fn()) .mockFunction("debug", jest.fn()) .mockFunction("warn", jest.fn()) .mockFunction("error", jest.fn()) .build() as any; // Setup ImageAIEngine mock mockImageAIEngine = { initialize: jest.fn().mockResolvedValue(undefined), generateImage: jest.fn().mockImplementation(async (prompt, options) => ({ imageData: `generated_image_${Date.now()}.png`, metadata: { prompt: prompt, steps: options?.steps || 50, guidance: options?.guidance || 7.5, seed: options?.seed || 12345, resolution: options?.resolution || { width: 1024, height: 1024 }, }, performance: { generationTime: 3500, memoryUsed: 8192, gpuUtilization: 85, }, })), enhancePrompt: jest .fn() .mockImplementation( async (prompt) => `${prompt}, highly detailed, professional photography, 8k resolution`, ), analyzeStyle: jest.fn().mockResolvedValue({ dominantStyle: "photorealistic", confidence: 0.92, suggestions: ["increase_detail", "adjust_lighting"], }), optimizeGeneration: jest.fn().mockResolvedValue({ optimalSteps: 42, optimalGuidance: 8.2, recommendedSampler: "euler_a", }), preloadModels: jest.fn().mockResolvedValue(undefined), unloadModels: jest.fn().mockResolvedValue(undefined), }; // Setup GPUAccelerator mock mockGPUAccelerator = { initialize: jest.fn().mockResolvedValue(undefined), getAvailableMemory: jest.fn().mockReturnValue(10240), // 10GB available getTotalMemory: jest.fn().mockReturnValue(12288), // 12GB total getCurrentUtilization: jest.fn().mockReturnValue(45), // 45% utilization optimizeBatch: jest.fn().mockImplementation(async (requests) => ({ optimalBatchSize: Math.min(requests.length, 4), memoryEstimate: requests.length * 2048, expectedLatency: requests.length * 1.2 * 1000, })), allocateMemory: jest.fn().mockResolvedValue("memory_pool_123"), releaseMemory: jest.fn().mockResolvedValue(undefined), warmupKernels: jest.fn().mockResolvedValue(undefined), }; // Setup RenderingPipeline mock mockRenderingPipeline = { initialize: jest.fn().mockResolvedValue(undefined), processImage: jest .fn() .mockImplementation(async (imageData, pipeline) => ({ processedImage: `processed_${imageData}`, appliedEffects: pipeline.effects || [], processingTime: 850, qualityScore: 0.88, })), applyEnhancements: jest.fn().mockResolvedValue({ enhancedImage: "enhanced_image_data", improvements: ["noise_reduction", "sharpness", "color_balance"], }), generateVariations: jest .fn() .mockImplementation(async (baseImage, count) => Array.from({ length: count }, (_, i) => ({ id: `variation_${i}`, imageData: `${baseImage}_var_${i}`, similarity: 0.75 + Math.random() * 0.2, })), ), }; // Setup QualityAssessor mock mockQualityAssessor = { assessQuality: jest .fn() .mockImplementation(async (imageData, criteria) => ({ overallScore: 0.85, metrics: { sharpness: 0.9, colorAccuracy: 0.82, composition: 0.88, promptAdherence: 0.8, }, issues: [], recommendations: ["minor_color_adjustment"], passed: true, })), compareImages: jest.fn().mockResolvedValue({ similarity: 0.78, differences: ["color_tone", "detail_level"], preference: "image_a", }), validatePromptAdherence: jest.fn().mockResolvedValue({ score: 0.85, detectedElements: ["person", "landscape", "sunset"], missingElements: [], extraElements: ["bird"], }), }; // Setup StorageManager mock mockStorageManager = { initialize: jest.fn().mockResolvedValue(undefined), storeImage: jest.fn().mockImplementation(async (imageData, metadata) => ({ id: `img_${Date.now()}`, path: `/storage/images/${metadata.name || "image"}.png`, size: 2048576, format: "png", stored: new Date(), })), retrieveImage: jest.fn().mockResolvedValue({ imageData: "retrieved_image_data", metadata: { created: new Date(), size: 1024576 }, }), deleteImage: jest.fn().mockResolvedValue(true), getStorageUsage: jest.fn().mockReturnValue({ used: 50 * 1024 * 1024 * 1024, // 50GB total: 100 * 1024 * 1024 * 1024, // 100GB percentage: 50, }), cleanup: jest.fn().mockResolvedValue({ deletedFiles: 25, freedSpace: 10 * 1024 * 1024 * 1024, // 10GB }), }; // Setup PerformanceMonitor mock mockPerformanceMonitor = { start: jest.fn().mockResolvedValue(undefined), recordGeneration: jest.fn(), recordBatch: jest.fn(), getMetrics: jest.fn().mockResolvedValue({ ...MockFactory.createPerformanceMetrics(), imageGeneration: { averageLatency: 3200, throughput: 18.5, // images per minute gpuUtilization: 78, memoryEfficiency: 0.85, cacheHitRate: 0.65, }, }), getGenerationMetrics: jest.fn().mockReturnValue({ totalGenerations: 1250, successRate: 0.94, averageQualityScore: 0.82, topPromptPatterns: ["landscape", "portrait", "abstract"], }), startTimer: jest.fn().mockReturnValue("timer_123"), endTimer: jest.fn().mockReturnValue(2350), // milliseconds }; // Setup BatchProcessor mock mockBatchProcessor = { initialize: jest.fn().mockResolvedValue(undefined), enqueueBatch: jest.fn().mockImplementation(async (requests) => ({ batchId: `batch_${Date.now()}`, size: requests.length, estimatedCompletion: new Date(Date.now() + requests.length * 2000), priority: 1, })), processBatch: jest.fn().mockImplementation(async (batchId) => ({ batchId, results: Array.from({ length: 4 }, (_, i) => ({ id: `result_${i}`, success: true, imageData: `batch_image_${i}`, generationTime: 2000 + Math.random() * 1000, })), totalTime: 8500, successRate: 1.0, })), getBatchStatus: jest.fn().mockResolvedValue({ batchId: "batch_123", status: "processing", progress: 0.6, completed: 6, total: 10, }), cancelBatch: jest.fn().mockResolvedValue(true), }; // Mock constructor dependencies jest.mocked(require("../../../utils/logger.js")).Logger = jest .fn() .mockImplementation(() => mockLogger); // Create Imagen4Generator instance imagen4Generator = new Imagen4Generator(mockConfig); // Inject mocks (imagen4Generator as any).imageAIEngine = mockImageAIEngine; (imagen4Generator as any).gpuAccelerator = mockGPUAccelerator; (imagen4Generator as any).renderingPipeline = mockRenderingPipeline; (imagen4Generator as any).qualityAssessor = mockQualityAssessor; (imagen4Generator as any).storageManager = mockStorageManager; (imagen4Generator as any).performanceMonitor = mockPerformanceMonitor; (imagen4Generator as any).batchProcessor = mockBatchProcessor; }); afterEach(() => { jest.clearAllMocks(); mockBuilder.clear(); }); // ==================== INITIALIZATION AND PERFORMANCE SETUP ==================== describe("Initialization and Performance Optimization", () => { it("should coordinate initialization with GPU optimization and model preloading", async () => { // ARRANGE const initializeSpy = jest.spyOn(imagen4Generator, "initialize"); const performanceTimer = mockPerformanceMonitor.startTimer(); // ACT await imagen4Generator.initialize(); // ASSERT - Verify initialization coordination with performance focus expect(initializeSpy).toHaveBeenCalledTimes(1); expect(mockImageAIEngine.initialize).toHaveBeenCalled(); expect(mockGPUAccelerator.initialize).toHaveBeenCalled(); expect(mockImageAIEngine.preloadModels).toHaveBeenCalled(); expect(mockGPUAccelerator.warmupKernels).toHaveBeenCalled(); expect(mockPerformanceMonitor.start).toHaveBeenCalled(); expect(mockLogger.info).toHaveBeenCalledWith( "Initializing Imagen4 Generator with performance optimization", ); // Verify performance timing expect(mockPerformanceMonitor.startTimer).toHaveBeenCalled(); expect(mockPerformanceMonitor.endTimer).toHaveBeenCalledWith( performanceTimer, ); }); it("should validate GPU memory requirements before initialization", async () => { // ARRANGE mockGPUAccelerator.getAvailableMemory.mockReturnValueOnce(4096); // Insufficient memory // ACT const result = await imagen4Generator.initialize(); // ASSERT expect(mockGPUAccelerator.getAvailableMemory).toHaveBeenCalled(); expect(mockLogger.warn).toHaveBeenCalledWith( "GPU memory may be insufficient for optimal performance", expect.objectContaining({ available: 4096, recommended: expect.any(Number), }), ); }); it("should handle initialization failure with proper cleanup", async () => { // ARRANGE const initError = new Error("GPU initialization failed"); mockGPUAccelerator.initialize.mockRejectedValueOnce(initError); // ACT & ASSERT await expect(imagen4Generator.initialize()).rejects.toThrow( "GPU initialization failed", ); expect(mockImageAIEngine.unloadModels).toHaveBeenCalled(); expect(mockLogger.error).toHaveBeenCalledWith( "Failed to initialize Imagen4 Generator", initError, ); }); }); // ==================== SINGLE IMAGE GENERATION WITH PERFORMANCE FOCUS ==================== describe("Single Image Generation Performance", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should generate image within performance targets", async () => { // ARRANGE const request = { prompt: "A serene mountain landscape at sunset", resolution: { width: 1024, height: 1024 }, quality: "high", style: "photorealistic", steps: 50, guidance: 7.5, }; const performanceTest = PerformanceTester.createPerformanceTest( "single_image_generation", () => imagen4Generator.generateImage(request), mockConfig.performance.targetLatency, // 5000ms 1, ); // ACT & ASSERT await performanceTest(); expect(mockPerformanceMonitor.recordGeneration).toHaveBeenCalled(); }); it("should coordinate prompt enhancement with AI optimization", async () => { // ARRANGE const request = { prompt: "beautiful landscape", resolution: { width: 1024, height: 1024 }, enhancePrompt: true, }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockImageAIEngine.enhancePrompt).toHaveBeenCalledWith( "beautiful landscape", ); expect(mockImageAIEngine.generateImage).toHaveBeenCalledWith( "beautiful landscape, highly detailed, professional photography, 8k resolution", expect.any(Object), ); expect(mockPerformanceMonitor.recordGeneration).toHaveBeenCalled(); }); it("should optimize generation parameters for best performance", async () => { // ARRANGE const request = { prompt: "abstract art composition", autoOptimize: true, quality: "ultra", }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockImageAIEngine.optimizeGeneration).toHaveBeenCalled(); expect(mockGPUAccelerator.allocateMemory).toHaveBeenCalled(); expect(result.data.metadata.performance.generationTime).toBeLessThan( mockConfig.performance.targetLatency, ); }); it("should handle memory constraints with dynamic optimization", async () => { // ARRANGE mockGPUAccelerator.getAvailableMemory.mockReturnValueOnce(2048); // Low memory const request = { prompt: "high detail fantasy scene", resolution: { width: 2048, height: 2048 }, // High memory requirement quality: "ultra", }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockGPUAccelerator.optimizeBatch).toHaveBeenCalled(); expect(mockLogger.info).toHaveBeenCalledWith( "Optimizing generation for memory constraints", expect.any(Object), ); }); }); // ==================== BATCH PROCESSING PERFORMANCE ==================== describe("Batch Processing Performance Optimization", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should coordinate optimal batch sizing with GPU memory limits", async () => { // ARRANGE const requests = Array.from({ length: 12 }, (_, i) => ({ prompt: `Test prompt ${i}`, resolution: { width: 1024, height: 1024 }, quality: "standard", })); // ACT const result = await imagen4Generator.generateBatch(requests); // ASSERT expect(result.success).toBe(true); expect(mockGPUAccelerator.optimizeBatch).toHaveBeenCalledWith(requests); expect(mockBatchProcessor.enqueueBatch).toHaveBeenCalled(); expect(mockPerformanceMonitor.recordBatch).toHaveBeenCalled(); }); it("should meet batch processing performance targets", async () => { // ARRANGE const batchRequests = Array.from({ length: 8 }, (_, i) => ({ prompt: `Batch image ${i}`, resolution: { width: 512, height: 512 }, quality: "standard", })); const batchPerformanceTest = PerformanceTester.createPerformanceTest( "batch_processing", () => imagen4Generator.generateBatch(batchRequests), 15000, // 15 seconds for batch of 8 1, ); // ACT & ASSERT await batchPerformanceTest(); }); it("should implement dynamic batch partitioning for large requests", async () => { // ARRANGE const largeBatch = Array.from({ length: 32 }, (_, i) => ({ prompt: `Large batch image ${i}`, resolution: { width: 1024, height: 1024 }, })); // ACT const result = await imagen4Generator.generateBatch(largeBatch); // ASSERT expect(result.success).toBe(true); expect(mockBatchProcessor.enqueueBatch).toHaveBeenCalledTimes(2); // Should split into multiple batches expect(mockLogger.info).toHaveBeenCalledWith( "Partitioning large batch for optimal processing", expect.objectContaining({ originalSize: 32, partitions: 2 }), ); }); it("should handle batch priority scheduling for performance optimization", async () => { // ARRANGE const highPriorityBatch = Array.from({ length: 4 }, (_, i) => ({ prompt: `High priority ${i}`, priority: 5, // Highest priority resolution: { width: 1024, height: 1024 }, })); const lowPriorityBatch = Array.from({ length: 4 }, (_, i) => ({ prompt: `Low priority ${i}`, priority: 1, // Lowest priority resolution: { width: 512, height: 512 }, })); // ACT const highPriorityResult = await imagen4Generator.generateBatch(highPriorityBatch); const lowPriorityResult = await imagen4Generator.generateBatch(lowPriorityBatch); // ASSERT expect(highPriorityResult.success).toBe(true); expect(lowPriorityResult.success).toBe(true); expect(mockBatchProcessor.enqueueBatch).toHaveBeenCalledTimes(2); }); }); // ==================== QUALITY ASSESSMENT WITH PERFORMANCE METRICS ==================== describe("Quality Assessment Performance Integration", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should coordinate quality assessment without significant performance impact", async () => { // ARRANGE const request = { prompt: "Quality test image", resolution: { width: 1024, height: 1024 }, qualityAssessment: true, }; const timer = mockPerformanceMonitor.startTimer(); // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockQualityAssessor.assessQuality).toHaveBeenCalled(); expect(result.data.quality.overallScore).toBeGreaterThan( mockConfig.quality.minQualityScore, ); // Quality assessment shouldn't add more than 20% overhead const totalTime = mockPerformanceMonitor.endTimer(timer); expect(totalTime).toBeLessThan( mockConfig.performance.targetLatency * 1.2, ); }); it("should implement performance-optimized quality enhancement", async () => { // ARRANGE const request = { prompt: "Enhancement test", resolution: { width: 1024, height: 1024 }, autoEnhance: true, }; mockQualityAssessor.assessQuality.mockResolvedValueOnce({ overallScore: 0.65, // Below threshold metrics: { sharpness: 0.6, colorAccuracy: 0.7 }, issues: ["low_sharpness"], recommendations: ["apply_sharpening"], passed: false, }); // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockRenderingPipeline.applyEnhancements).toHaveBeenCalled(); expect(mockQualityAssessor.assessQuality).toHaveBeenCalledTimes(2); // Before and after enhancement }); it("should provide detailed performance metrics for quality processes", async () => { // ARRANGE const requests = Array.from({ length: 5 }, (_, i) => ({ prompt: `Quality metrics test ${i}`, resolution: { width: 1024, height: 1024 }, qualityAssessment: true, })); // ACT await imagen4Generator.generateBatch(requests); const metrics = await imagen4Generator.getPerformanceMetrics(); // ASSERT expect(metrics.success).toBe(true); expect(metrics.data.imageGeneration).toBeDefined(); expect(metrics.data.imageGeneration.averageLatency).toBeLessThan( mockConfig.performance.targetLatency, ); expect(metrics.data.imageGeneration.throughput).toBeGreaterThan(10); // Images per minute expect(metrics.data.imageGeneration.gpuUtilization).toBeLessThan(95); // Not maxed out }); }); // ==================== MEMORY AND RESOURCE PERFORMANCE ==================== describe("Memory Management and Resource Performance", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should coordinate memory pooling for optimal GPU utilization", async () => { // ARRANGE const sequentialRequests = Array.from({ length: 10 }, (_, i) => ({ prompt: `Sequential test ${i}`, resolution: { width: 1024, height: 1024 }, })); // ACT const results = []; for (const request of sequentialRequests) { results.push(await imagen4Generator.generateImage(request)); } // ASSERT expect(results.every((r) => r.success)).toBe(true); expect(mockGPUAccelerator.allocateMemory).toHaveBeenCalledTimes(10); expect(mockGPUAccelerator.releaseMemory).toHaveBeenCalledTimes(10); // Verify memory pooling efficiency const memoryMetrics = await imagen4Generator.getMemoryMetrics(); expect(memoryMetrics.success).toBe(true); expect(memoryMetrics.data.poolEfficiency).toBeGreaterThan(0.8); }); it("should implement aggressive memory cleanup under pressure", async () => { // ARRANGE mockGPUAccelerator.getAvailableMemory.mockReturnValue(1024); // Very low memory mockStorageManager.getStorageUsage.mockReturnValue({ used: 90 * 1024 * 1024 * 1024, // 90GB used total: 100 * 1024 * 1024 * 1024, // 100GB total percentage: 90, }); const request = { prompt: "Memory pressure test", resolution: { width: 2048, height: 2048 }, // High memory usage }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(mockStorageManager.cleanup).toHaveBeenCalled(); expect(mockGPUAccelerator.releaseMemory).toHaveBeenCalled(); expect(mockLogger.warn).toHaveBeenCalledWith( "Memory pressure detected, performing cleanup", expect.any(Object), ); }); it("should provide real-time resource utilization metrics", async () => { // ARRANGE const request = { prompt: "Resource monitoring test", resolution: { width: 1024, height: 1024 }, }; // ACT await imagen4Generator.generateImage(request); const resourceMetrics = await imagen4Generator.getResourceMetrics(); // ASSERT expect(resourceMetrics.success).toBe(true); expect(resourceMetrics.data).toEqual( expect.objectContaining({ gpu: expect.objectContaining({ utilization: expect.any(Number), memoryUsed: expect.any(Number), memoryTotal: expect.any(Number), }), storage: expect.objectContaining({ used: expect.any(Number), available: expect.any(Number), percentage: expect.any(Number), }), performance: expect.objectContaining({ averageLatency: expect.any(Number), throughput: expect.any(Number), cacheHitRate: expect.any(Number), }), }), ); }); }); // ==================== ADVANCED FEATURES PERFORMANCE ==================== describe("Advanced Features Performance Optimization", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should coordinate style transfer with minimal performance overhead", async () => { // ARRANGE const request = { prompt: "Portrait of a person", styleReference: "van_gogh_style", resolution: { width: 1024, height: 1024 }, styleStrength: 0.8, }; const timer = mockPerformanceMonitor.startTimer(); // ACT const result = await imagen4Generator.generateImageWithStyle(request); // ASSERT expect(result.success).toBe(true); expect(mockImageAIEngine.analyzeStyle).toHaveBeenCalled(); const processingTime = mockPerformanceMonitor.endTimer(timer); expect(processingTime).toBeLessThan( mockConfig.performance.targetLatency * 1.5, ); // 50% overhead max }); it("should implement performance-optimized image variations", async () => { // ARRANGE const baseRequest = { prompt: "Base image for variations", resolution: { width: 1024, height: 1024 }, }; const baseResult = await imagen4Generator.generateImage(baseRequest); const variationCount = 6; // ACT const variationsResult = await imagen4Generator.generateVariations( baseResult.data.id, variationCount, ); // ASSERT expect(variationsResult.success).toBe(true); expect(variationsResult.data.variations).toHaveLength(variationCount); expect(mockRenderingPipeline.generateVariations).toHaveBeenCalledWith( expect.any(String), variationCount, ); // Variations should be faster than generating from scratch expect(variationsResult.data.totalProcessingTime).toBeLessThan( mockConfig.performance.targetLatency * variationCount * 0.7, ); }); it("should coordinate inpainting with performance optimization", async () => { // ARRANGE const inpaintRequest = { baseImage: "base_image_data", maskImage: "mask_image_data", prompt: "Fill the masked area with flowers", resolution: { width: 1024, height: 1024 }, inpaintStrength: 0.9, }; // ACT const result = await imagen4Generator.inpaintImage(inpaintRequest); // ASSERT expect(result.success).toBe(true); expect(mockImageAIEngine.generateImage).toHaveBeenCalledWith( inpaintRequest.prompt, expect.objectContaining({ mode: "inpaint", baseImage: inpaintRequest.baseImage, mask: inpaintRequest.maskImage, }), ); expect(result.data.performance.generationTime).toBeLessThan( mockConfig.performance.targetLatency, ); }); }); // ==================== CACHING AND OPTIMIZATION PERFORMANCE ==================== describe("Caching and Performance Optimization Strategies", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should implement intelligent prompt caching for performance", async () => { // ARRANGE const identicalRequests = Array.from({ length: 3 }, () => ({ prompt: "Cached prompt test", resolution: { width: 1024, height: 1024 }, steps: 50, seed: 42, // Same seed for identical results })); // ACT const results = []; for (const request of identicalRequests) { results.push(await imagen4Generator.generateImage(request)); } // ASSERT expect(results.every((r) => r.success)).toBe(true); // First generation should be normal speed, subsequent should be from cache expect(results[0].data.metadata.cached).toBe(false); expect(results[1].data.metadata.cached).toBe(true); expect(results[2].data.metadata.cached).toBe(true); // Cached results should be significantly faster expect(results[1].data.metadata.performance.generationTime).toBeLessThan( results[0].data.metadata.performance.generationTime * 0.1, ); }); it("should coordinate model warming for consistent performance", async () => { // ARRANGE await imagen4Generator.warmupModels([ "photorealistic", "artistic", "cartoon", ]); const requests = [ { prompt: "Photorealistic portrait", style: "photorealistic" }, { prompt: "Artistic landscape", style: "artistic" }, { prompt: "Cartoon character", style: "cartoon" }, ]; // ACT const results = await Promise.all( requests.map((req) => imagen4Generator.generateImage(req)), ); // ASSERT expect(results.every((r) => r.success)).toBe(true); expect(mockImageAIEngine.preloadModels).toHaveBeenCalled(); // All generations should be fast due to model warming results.forEach((result) => { expect(result.data.metadata.performance.generationTime).toBeLessThan( mockConfig.performance.targetLatency * 0.8, ); }); }); it("should implement performance-based quality degradation", async () => { // ARRANGE mockGPUAccelerator.getCurrentUtilization.mockReturnValue(95); // High GPU load const request = { prompt: "Performance degradation test", resolution: { width: 2048, height: 2048 }, quality: "ultra", adaptiveQuality: true, }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(true); expect(result.data.metadata.qualityAdjusted).toBe(true); expect(result.data.metadata.adjustmentReason).toBe( "performance_optimization", ); expect(mockLogger.info).toHaveBeenCalledWith( "Adjusting quality for performance optimization", expect.any(Object), ); }); }); // ==================== CONTRACT AND ERROR PERFORMANCE ==================== describe("Contract Validation and Error Handling Performance", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should maintain service response contracts under all performance conditions", async () => { // ARRANGE & ACT const singleResult = await imagen4Generator.generateImage({ prompt: "Contract test", resolution: { width: 1024, height: 1024 }, }); const batchResult = await imagen4Generator.generateBatch([ { prompt: "Batch contract test 1" }, { prompt: "Batch contract test 2" }, ]); const metricsResult = await imagen4Generator.getPerformanceMetrics(); // ASSERT ContractTester.validateServiceResponse(singleResult); ContractTester.validateServiceResponse(batchResult); ContractTester.validateServiceResponse(metricsResult); }); it("should handle performance bottlenecks gracefully", async () => { // ARRANGE mockGPUAccelerator.allocateMemory.mockRejectedValue( new Error("Out of GPU memory"), ); const request = { prompt: "Memory error test", resolution: { width: 4096, height: 4096 }, // Very high memory requirement }; // ACT const result = await imagen4Generator.generateImage(request); // ASSERT expect(result.success).toBe(false); expect(result.error?.code).toBe("GPU_MEMORY_INSUFFICIENT"); expect(mockLogger.error).toHaveBeenCalledWith( "GPU memory allocation failed", expect.any(Error), ); }); it("should meet performance SLA requirements consistently", async () => { // ARRANGE - Performance SLA test const performanceRequirements = { singleImageMaxLatency: 5000, // 5 seconds batchThroughput: 10, // images per minute memoryEfficiency: 0.8, // 80% efficiency successRate: 0.95, // 95% success rate }; const testRequests = Array.from({ length: 20 }, (_, i) => ({ prompt: `SLA test ${i}`, resolution: { width: 1024, height: 1024 }, })); // ACT const startTime = Date.now(); const results = await Promise.all( testRequests.map((req) => imagen4Generator.generateImage(req)), ); const totalTime = Date.now() - startTime; // ASSERT const successfulResults = results.filter((r) => r.success); const successRate = successfulResults.length / results.length; const averageLatency = totalTime / results.length; const throughput = (successfulResults.length / totalTime) * 60000; // per minute expect(successRate).toBeGreaterThanOrEqual( performanceRequirements.successRate, ); expect(averageLatency).toBeLessThanOrEqual( performanceRequirements.singleImageMaxLatency, ); expect(throughput).toBeGreaterThanOrEqual( performanceRequirements.batchThroughput, ); expect(mockLogger.info).toHaveBeenCalledWith( "Performance SLA validation completed", expect.objectContaining({ successRate, averageLatency, throughput, }), ); }); }); // ==================== PROPERTY-BASED PERFORMANCE TESTING ==================== describe("Property-Based Performance Testing", () => { beforeEach(async () => { await imagen4Generator.initialize(); }); it("should maintain performance across diverse generation parameters", async () => { // ARRANGE - Property-based performance test const performanceTestCases = PropertyGenerator.generateTestCases(() => { const resolutions = [ { width: 512, height: 512 }, { width: 1024, height: 1024 }, { width: 1920, height: 1080 }, { width: 2048, height: 2048 }, ]; const qualities = ["draft", "standard", "high"]; const steps = [20, 30, 50, 75]; return { prompt: TestDataGenerator.randomString(30), resolution: resolutions[Math.floor(Math.random() * resolutions.length)], quality: qualities[Math.floor(Math.random() * qualities.length)], steps: steps[Math.floor(Math.random() * steps.length)], }; }, 10); // ACT & ASSERT for (const testCase of performanceTestCases) { const startTime = Date.now(); const result = await imagen4Generator.generateImage(testCase); const latency = Date.now() - startTime; expect(result.success).toBe(true); // Performance should scale predictably with parameters const expectedMaxLatency = calculateExpectedLatency(testCase); expect(latency).toBeLessThanOrEqual(expectedMaxLatency); } }); // Helper function for expected latency calculation function calculateExpectedLatency(params: any): number { const baseLatency = 2000; // 2 seconds base const resolutionFactor = (params.resolution.width * params.resolution.height) / (1024 * 1024); const qualityFactor = { draft: 0.5, standard: 1.0, high: 1.5 }[params.quality] || 1.0; const stepsFactor = params.steps / 50; return baseLatency * resolutionFactor * qualityFactor * stepsFactor; } }); }); /** * RED-GREEN-REFACTOR CYCLE DOCUMENTATION FOR IMAGEN4 GENERATOR: * * This comprehensive test suite demonstrates London School TDD with performance focus: * * 1. PERFORMANCE-FIRST TESTING: * - All operations tested against strict performance targets * - GPU memory optimization and utilization tracking * - Batch processing efficiency and throughput optimization * - Caching strategies for improved response times * * 2. RESOURCE MANAGEMENT VALIDATION: * - Memory allocation and cleanup coordination * - GPU utilization optimization under various load conditions * - Storage management with automated cleanup strategies * - Performance degradation handling with adaptive quality * * 3. QUALITY-PERFORMANCE BALANCE: * - Quality assessment integrated without performance impact * - Automatic enhancement with performance constraints * - Performance-based quality adaptation mechanisms * - SLA compliance validation across diverse scenarios * * 4. LONDON SCHOOL PRINCIPLES WITH PERFORMANCE FOCUS: * - RED: Define performance requirements through failing tests * - GREEN: Implement coordination logic to meet performance targets * - REFACTOR: Optimize performance while maintaining functional contracts * * Key Performance Collaboration Patterns Tested: * - GPUAccelerator ↔ MemoryManager (efficient memory allocation and pooling) * - BatchProcessor ↔ PerformanceMonitor (optimal batch sizing and throughput) * - CacheManager ↔ AIEngine (intelligent caching for repeated requests) * - QualityAssessor ↔ PerformanceOptimizer (quality assessment with minimal overhead) * - ResourceMonitor ↔ AdaptiveQuality (dynamic quality adjustment under load) * * This design ensures high-performance image generation while maintaining * quality standards and resource efficiency across various operating conditions. */