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tests/paper_validation.py

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+"""
+Paper Consistency Validation for Cosmos Models
+Reference: arXiv 2511.00062 - World Simulation with Video Foundation Models for Physical AI
+
+This module implements minimal reproducible tests to verify consistency with paper claims.
+"""
+import sys
+import os
+import json
+import time
+from pathlib import Path
+from typing import List, Dict, Any
+from datetime import datetime
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+# Paper Reference Points for Validation
+PAPER_CLAIMS = {
+    "predict_unified_generation": {
+        "section": "Section 3.1 - Unified World Generation",
+        "claim": "Cosmos-Predict2.5 unifies Text2World, Image2World, and Video2World into a single model",
+        "validation": "Verify model can handle all three input modalities"
+    },
+    "predict_temporal_consistency": {
+        "section": "Section 4.2 - Temporal Coherence",
+        "claim": "Generated videos maintain reasonable spatiotemporal continuity in short-term prediction",
+        "validation": "Measure frame-to-frame pixel difference to verify smooth transitions"
+    },
+    "predict_reproducibility": {
+        "section": "Section 5 - Reproducibility",
+        "claim": "Fixed random seeds produce deterministic outputs",
+        "validation": "Run same prompt with same seed twice, verify identical outputs"
+    },
+    "transfer_structure_preservation": {
+        "section": "Section 3.2 - World-to-World Translation",
+        "claim": "Cosmos-Transfer2.5 preserves structural consistency during domain transfer",
+        "validation": "Compare edge maps between input and output to verify structure preservation"
+    },
+    "transfer_domain_adaptation": {
+        "section": "Section 4.3 - Domain Transfer",
+        "claim": "Model can perform world-to-world translation (e.g., day->night, clear->rain)",
+        "validation": "Verify output differs from input while maintaining structure"
+    }
+}
+
+
+def validate_predict_temporal_consistency(
+    num_samples: int = 3,
+    seed_base: int = 42
+) -> Dict[str, Any]:
+    """
+    Validate temporal consistency of Predict2.5 outputs
+
+    Paper claim (Section 4.2): Generated videos maintain spatiotemporal continuity
+
+    Validation method:
+    - Generate N videos with different seeds
+    - Compute mean frame-to-frame pixel difference
+    - Check that differences are smooth (not explosive)
+    """
+    print("\n" + "=" * 60)
+    print("VALIDATION: Predict2.5 Temporal Consistency")
+    print("Paper Reference: Section 4.2 - Temporal Coherence")
+    print("=" * 60)
+
+    from cosmos.utils_video import compute_temporal_smoothness, load_video_frames
+    from cosmos.infer_predict import predict_text2world
+    import tempfile
+
+    results = {
+        "test_name": "predict_temporal_consistency",
+        "paper_section": "Section 4.2",
+        "paper_claim": "Generated videos maintain reasonable spatiotemporal continuity",
+        "num_samples": num_samples,
+        "samples": [],
+        "overall_pass": False,
+        "timestamp": datetime.now().isoformat()
+    }
+
+    prompt = "A ball rolling slowly across a flat surface, simple motion, smooth"
+
+    for i in range(num_samples):
+        seed = seed_base + i
+        output_path = tempfile.mktemp(suffix=".mp4")
+
+        print(f"\nSample {i+1}/{num_samples} (seed={seed})")
+
+        try:
+            result = predict_text2world(
+                prompt=prompt,
+                num_frames=25,  # ~1.5s at 16fps
+                height=480,
+                width=720,
+                num_inference_steps=15,  # Faster for validation
+                seed=seed,
+                output_path=output_path
+            )
+
+            # Load frames and compute smoothness
+            frames = load_video_frames(output_path)
+            smoothness = compute_temporal_smoothness(frames)
+
+            sample_result = {
+                "seed": seed,
+                "num_frames": result['num_frames'],
+                "smoothness": smoothness,
+                "video_path": output_path,
+                "pass": smoothness['mean_diff'] < 50  # Threshold for "smooth"
+            }
+
+            print(f"  Mean frame diff: {smoothness['mean_diff']:.2f}")
+            print(f"  Max frame diff: {smoothness['max_diff']:.2f}")
+            print(f"  Pass: {sample_result['pass']}")
+
+            results['samples'].append(sample_result)
+
+        except Exception as e:
+            print(f"  ERROR: {e}")
+            results['samples'].append({
+                "seed": seed,
+                "error": str(e),
+                "pass": False
+            })
+
+    # Overall assessment
+    passed = sum(1 for s in results['samples'] if s.get('pass', False))
+    results['passed_samples'] = passed
+    results['overall_pass'] = passed >= num_samples // 2 + 1  # Majority pass
+
+    print(f"\nOverall: {passed}/{num_samples} samples passed")
+    print(f"Validation {'PASSED' if results['overall_pass'] else 'FAILED'}")
+
+    return results
+
+
+def validate_predict_reproducibility(seed: int = 42) -> Dict[str, Any]:
+    """
+    Validate reproducibility of Predict2.5 outputs
+
+    Paper claim (Section 5): Fixed seeds produce deterministic outputs
+
+    Validation method:
+    - Run same prompt with same seed twice
+    - Compare output frame by frame
+    - Verify outputs are identical (or near-identical)
+    """
+    print("\n" + "=" * 60)
+    print("VALIDATION: Predict2.5 Reproducibility")
+    print("Paper Reference: Section 5 - Reproducibility")
+    print("=" * 60)
+
+    from cosmos.utils_video import load_video_frames, compute_ssim
+    from cosmos.infer_predict import predict_text2world
+    import tempfile
+    import numpy as np
+
+    results = {
+        "test_name": "predict_reproducibility",
+        "paper_section": "Section 5",
+        "paper_claim": "Fixed random seeds produce deterministic outputs",
+        "seed": seed,
+        "overall_pass": False,
+        "timestamp": datetime.now().isoformat()
+    }
+
+    prompt = "A simple test scene with a single red sphere"
+
+    # Run 1
+    print("\nRun 1...")
+    output1 = tempfile.mktemp(suffix="_run1.mp4")
+    result1 = predict_text2world(
+        prompt=prompt,
+        num_frames=17,
+        height=480,
+        width=720,
+        num_inference_steps=10,
+        seed=seed,
+        output_path=output1
+    )
+
+    # Run 2 (same parameters)
+    print("\nRun 2...")
+    output2 = tempfile.mktemp(suffix="_run2.mp4")
+    result2 = predict_text2world(
+        prompt=prompt,
+        num_frames=17,
+        height=480,
+        width=720,
+        num_inference_steps=10,
+        seed=seed,
+        output_path=output2
+    )
+
+    # Compare outputs
+    print("\nComparing outputs...")
+    frames1 = load_video_frames(output1)
+    frames2 = load_video_frames(output2)
+
+    if len(frames1) != len(frames2):
+        print(f"  Frame count mismatch: {len(frames1)} vs {len(frames2)}")
+        results['error'] = "Frame count mismatch"
+        return results
+
+    # Compute SSIM for each frame pair
+    ssim_scores = []
+    for i, (f1, f2) in enumerate(zip(frames1, frames2)):
+        ssim = compute_ssim(f1, f2)
+        ssim_scores.append(ssim)
+
+    mean_ssim = np.mean(ssim_scores)
+    min_ssim = np.min(ssim_scores)
+
+    results['mean_ssim'] = float(mean_ssim)
+    results['min_ssim'] = float(min_ssim)
+    results['overall_pass'] = mean_ssim > 0.95  # Very high similarity expected
+
+    print(f"  Mean SSIM: {mean_ssim:.4f}")
+    print(f"  Min SSIM: {min_ssim:.4f}")
+    print(f"  Validation {'PASSED' if results['overall_pass'] else 'FAILED'}")
+
+    return results
+
+
+def validate_transfer_structure_preservation(
+    control_type: str = "edge"
+) -> Dict[str, Any]:
+    """
+    Validate structure preservation in Transfer2.5 outputs
+
+    Paper claim (Section 3.2): Transfer preserves structural consistency
+
+    Validation method:
+    - Create test video with known structure
+    - Apply style transfer
+    - Compare edge maps of input and output
+    - Verify edges are preserved (high correlation)
+    """
+    print("\n" + "=" * 60)
+    print("VALIDATION: Transfer2.5 Structure Preservation")
+    print("Paper Reference: Section 3.2 - World-to-World Translation")
+    print("=" * 60)
+
+    from cosmos.utils_video import (
+        create_test_video, load_video_frames, extract_edges, compute_ssim
+    )
+    from cosmos.infer_transfer import transfer_video
+    import tempfile
+    import numpy as np
+
+    results = {
+        "test_name": "transfer_structure_preservation",
+        "paper_section": "Section 3.2",
+        "paper_claim": "Cosmos-Transfer2.5 preserves structural consistency during domain transfer",
+        "control_type": control_type,
+        "overall_pass": False,
+        "timestamp": datetime.now().isoformat()
+    }
+
+    # Create test video with clear structure
+    test_video = create_test_video(num_frames=17, width=320, height=240)
+    output_path = tempfile.mktemp(suffix="_transfer.mp4")
+
+    try:
+        print("\nRunning transfer...")
+        result = transfer_video(
+            input_video=test_video,
+            prompt="Transform to nighttime scene with city lights",
+            control_type=control_type,
+            num_inference_steps=10,
+            seed=42,
+            output_path=output_path
+        )
+
+        # Compare edge maps
+        print("\nComparing structure (edge maps)...")
+        input_frames = load_video_frames(test_video)
+        output_frames = load_video_frames(output_path)
+
+        edge_similarities = []
+        for i, (inp, out) in enumerate(zip(input_frames[:5], output_frames[:5])):
+            inp_edges = extract_edges(inp)
+            out_edges = extract_edges(out)
+            ssim = compute_ssim(inp_edges, out_edges)
+            edge_similarities.append(ssim)
+
+        mean_edge_ssim = np.mean(edge_similarities)
+        results['mean_edge_ssim'] = float(mean_edge_ssim)
+        results['edge_similarities'] = [float(s) for s in edge_similarities]
+
+        # Structure is preserved if edge SSIM > 0.3 (some similarity expected)
+        results['overall_pass'] = mean_edge_ssim > 0.3
+
+        print(f"  Mean edge SSIM: {mean_edge_ssim:.4f}")
+        print(f"  Validation {'PASSED' if results['overall_pass'] else 'FAILED'}")
+
+    except Exception as e:
+        print(f"  ERROR: {e}")
+        results['error'] = str(e)
+
+    finally:
+        # Cleanup
+        if os.path.exists(test_video):
+            os.remove(test_video)
+
+    return results
+
+
+def validate_transfer_domain_change() -> Dict[str, Any]:
+    """
+    Validate that Transfer2.5 actually changes the domain
+
+    Paper claim (Section 4.3): Model performs world-to-world translation
+
+    Validation method:
+    - Apply day->night transfer
+    - Verify output differs from input (different domain)
+    - While still maintaining some structure
+    """
+    print("\n" + "=" * 60)
+    print("VALIDATION: Transfer2.5 Domain Change")
+    print("Paper Reference: Section 4.3 - Domain Transfer")
+    print("=" * 60)
+
+    from cosmos.utils_video import (
+        create_test_video, load_video_frames, compute_ssim
+    )
+    from cosmos.infer_transfer import transfer_style
+    import tempfile
+    import numpy as np
+
+    results = {
+        "test_name": "transfer_domain_change",
+        "paper_section": "Section 4.3",
+        "paper_claim": "Model can perform world-to-world translation (e.g., day->night)",
+        "overall_pass": False,
+        "timestamp": datetime.now().isoformat()
+    }
+
+    test_video = create_test_video(num_frames=17, width=320, height=240)
+    output_path = tempfile.mktemp(suffix="_domain.mp4")
+
+    try:
+        print("\nApplying day -> night transfer...")
+        result = transfer_style(
+            input_video=test_video,
+            source_style="daytime",
+            target_style="nighttime with city lights",
+            control_type="blur",
+            num_inference_steps=10,
+            seed=42,
+            output_path=output_path
+        )
+
+        # Compare input and output
+        input_frames = load_video_frames(test_video)
+        output_frames = load_video_frames(output_path)
+
+        # Compute pixel-level similarity (should be different but not completely)
+        similarities = []
+        for inp, out in zip(input_frames[:5], output_frames[:5]):
+            ssim = compute_ssim(inp, out)
+            similarities.append(ssim)
+
+        mean_ssim = np.mean(similarities)
+        results['mean_ssim'] = float(mean_ssim)
+
+        # Domain change successful if:
+        # - Output is different from input (SSIM < 0.9)
+        # - But not completely random (SSIM > 0.1)
+        results['overall_pass'] = 0.1 < mean_ssim < 0.9
+
+        print(f"  Mean SSIM (input vs output): {mean_ssim:.4f}")
+        print(f"  Domain change detected: {results['overall_pass']}")
+        print(f"  Validation {'PASSED' if results['overall_pass'] else 'FAILED'}")
+
+    except Exception as e:
+        print(f"  ERROR: {e}")
+        results['error'] = str(e)
+
+    finally:
+        if os.path.exists(test_video):
+            os.remove(test_video)
+
+    return results
+
+
+def run_all_validations(skip_transfer: bool = False) -> Dict[str, Any]:
+    """
+    Run all paper consistency validations
+
+    Args:
+        skip_transfer: Skip Transfer2.5 tests if VRAM is limited
+
+    Returns:
+        Combined validation results
+    """
+    print("\n" + "=" * 70)
+    print("PAPER CONSISTENCY VALIDATION")
+    print("Reference: arXiv 2511.00062")
+    print("=" * 70)
+
+    all_results = {
+        "paper": "arXiv 2511.00062 - World Simulation with Video Foundation Models for Physical AI",
+        "timestamp": datetime.now().isoformat(),
+        "validations": {}
+    }
+
+    # Predict2.5 Validations
+    print("\n[PREDICT2.5 VALIDATIONS]")
+
+    all_results['validations']['predict_temporal'] = validate_predict_temporal_consistency(
+        num_samples=2  # Reduced for speed
+    )
+
+    all_results['validations']['predict_reproducibility'] = validate_predict_reproducibility()
+
+    # Transfer2.5 Validations
+    if not skip_transfer:
+        print("\n[TRANSFER2.5 VALIDATIONS]")
+
+        # Clear Predict model first
+        from cosmos.loaders import clear_model_cache
+        clear_model_cache()
+
+        all_results['validations']['transfer_structure'] = validate_transfer_structure_preservation()
+        all_results['validations']['transfer_domain'] = validate_transfer_domain_change()
+    else:
+        print("\n[TRANSFER2.5 VALIDATIONS - SKIPPED]")
+        all_results['validations']['transfer_skipped'] = True
+
+    # Summary
+    print("\n" + "=" * 70)
+    print("VALIDATION SUMMARY")
+    print("=" * 70)
+
+    passed_count = 0
+    total_count = 0
+
+    for name, result in all_results['validations'].items():
+        if isinstance(result, dict) and 'overall_pass' in result:
+            total_count += 1
+            if result['overall_pass']:
+                passed_count += 1
+            status = "PASSED" if result['overall_pass'] else "FAILED"
+            print(f"  {name}: {status}")
+
+    all_results['summary'] = {
+        "passed": passed_count,
+        "total": total_count,
+        "overall_pass": passed_count == total_count
+    }
+
+    print(f"\nOverall: {passed_count}/{total_count} validations passed")
+
+    return all_results
+
+
+def save_validation_report(results: Dict[str, Any], output_path: str):
+    """Save validation results to JSON file"""
+    with open(output_path, 'w') as f:
+        json.dump(results, f, indent=2)
+    print(f"\nValidation report saved to: {output_path}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Run paper consistency validations")
+    parser.add_argument("--skip-transfer", action="store_true", help="Skip Transfer2.5 tests")
+    parser.add_argument("--output", default="validation_results.json", help="Output file")
+
+    args = parser.parse_args()
+
+    results = run_all_validations(skip_transfer=args.skip_transfer)
+    save_validation_report(results, args.output)