alternative_sampling.py

import torch
from model import SmoothDiffusionUNet
from noise_scheduler import FrequencyAwareNoise
from config import Config
from torchvision.utils import save_image, make_grid
import numpy as np

def deterministic_sample(model, noise_scheduler, device, n_samples=4):
    """Deterministic sampling - just do a few big denoising steps"""
    config = Config()
    model.eval()
    
    with torch.no_grad():
        # Start with noise but not too extreme
        x = torch.randn(n_samples, 3, config.image_size, config.image_size, device=device) * 0.5
        
        print(f"Starting simplified sampling for {n_samples} samples...")
        
        # Use fewer, bigger steps - more like denoising than full diffusion
        timesteps = [400, 300, 200, 150, 100, 70, 50, 30, 20, 10, 5, 1]
        
        for i, t_val in enumerate(timesteps):
            print(f"Step {i+1}/{len(timesteps)}, t={t_val}")
            
            t_tensor = torch.full((n_samples,), t_val, device=device, dtype=torch.long)
            
            # Get model prediction
            predicted_noise = model(x, t_tensor)
            
            # Simple denoising step
            alpha_bar_t = noise_scheduler.alpha_bars[t_val].item()
            
            # Predict clean image
            pred_x0 = (x - np.sqrt(1 - alpha_bar_t) * predicted_noise) / np.sqrt(alpha_bar_t)
            pred_x0 = torch.clamp(pred_x0, -1, 1)
            
            # Move towards clean prediction
            if i < len(timesteps) - 1:
                # Not final step - blend
                next_t = timesteps[i + 1]
                alpha_bar_next = noise_scheduler.alpha_bars[next_t].item()
                
                # Add some noise for next step
                noise_scale = np.sqrt(1 - alpha_bar_next)
                noise = torch.randn_like(x) * 0.1  # Much less noise
                
                x = np.sqrt(alpha_bar_next) * pred_x0 + noise_scale * noise
            else:
                # Final step
                x = pred_x0
            
            x = torch.clamp(x, -1.5, 1.5)  # Prevent drift
            
            if i % 3 == 0:
                print(f"  Current range: [{x.min():.3f}, {x.max():.3f}], std: {x.std():.3f}")
        
        # Final clamp
        x = torch.clamp(x, -1, 1)
        
        print(f"Final samples:")
        print(f"  Range: [{x.min():.3f}, {x.max():.3f}]")
        print(f"  Mean: {x.mean():.3f}, Std: {x.std():.3f}")
        
        # Convert to display range
        x_display = torch.clamp((x + 1) / 2, 0, 1)
        
        # Create and save grid
        grid = make_grid(x_display, nrow=2, normalize=False, pad_value=1.0)
        save_image(grid, "simplified_samples.png")
        print(f"Samples saved to simplified_samples.png")
        
        return x, grid

def progressive_sample(model, noise_scheduler, device, n_samples=4):
    """Progressive denoising - start from less noise"""
    config = Config()
    model.eval()
    
    with torch.no_grad():
        # Start from moderately noisy image instead of pure noise
        x = torch.randn(n_samples, 3, config.image_size, config.image_size, device=device) * 0.3
        
        print(f"Starting progressive denoising for {n_samples} samples...")
        
        # Start from a moderate timestep instead of maximum noise
        start_t = 200
        
        for step, t in enumerate(reversed(range(0, start_t))):
            if step % 50 == 0:
                print(f"Denoising step {step}/{start_t}, t={t}")
            
            t_tensor = torch.full((n_samples,), t, device=device, dtype=torch.long)
            
            # Get prediction
            predicted_noise = model(x, t_tensor)
            
            # Standard DDPM step but with more stability
            alpha_t = noise_scheduler.alphas[t].item()
            alpha_bar_t = noise_scheduler.alpha_bars[t].item()
            beta_t = noise_scheduler.betas[t].item()
            
            if t > 0:
                alpha_bar_prev = noise_scheduler.alpha_bars[t-1].item()
                
                # Predict x0
                pred_x0 = (x - np.sqrt(1 - alpha_bar_t) * predicted_noise) / np.sqrt(alpha_bar_t)
                pred_x0 = torch.clamp(pred_x0, -1, 1)
                
                # Posterior mean
                coeff1 = np.sqrt(alpha_t) * (1 - alpha_bar_prev) / (1 - alpha_bar_t)
                coeff2 = np.sqrt(alpha_bar_prev) * beta_t / (1 - alpha_bar_t)
                mean = coeff1 * x + coeff2 * pred_x0
                
                # Reduced noise for stability
                if t > 1:
                    posterior_variance = beta_t * (1 - alpha_bar_prev) / (1 - alpha_bar_t)
                    noise = torch.randn_like(x)
                    # Reduce noise by half for more stability
                    x = mean + np.sqrt(posterior_variance) * noise * 0.5
                else:
                    x = mean
            else:
                x = (x - np.sqrt(1 - alpha_bar_t) * predicted_noise) / np.sqrt(alpha_bar_t)
            
            # Gentle clamping
            x = torch.clamp(x, -1.2, 1.2)
        
        x = torch.clamp(x, -1, 1)
        
        print(f"Progressive samples:")
        print(f"  Range: [{x.min():.3f}, {x.max():.3f}]")
        print(f"  Mean: {x.mean():.3f}, Std: {x.std():.3f}")
        
        x_display = torch.clamp((x + 1) / 2, 0, 1)
        grid = make_grid(x_display, nrow=2, normalize=False, pad_value=1.0)
        save_image(grid, "progressive_samples.png")
        print(f"Samples saved to progressive_samples.png")
        
        return x, grid

def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Load model
    checkpoint = torch.load('model_final.pth', map_location=device)
    config = Config()
    
    model = SmoothDiffusionUNet(config).to(device)
    noise_scheduler = FrequencyAwareNoise(config)
    model.load_state_dict(checkpoint)
    
    print("=== TRYING DETERMINISTIC SAMPLING ===")
    deterministic_sample(model, noise_scheduler, device, n_samples=4)
    
    print("\n=== TRYING PROGRESSIVE SAMPLING ===")
    progressive_sample(model, noise_scheduler, device, n_samples=4)

if __name__ == "__main__":
    main()