Advanced Techniques & Workflows

def latent_interpolation(latent_a, latent_b, steps=10):
    interpolated = []
    for i in range(steps):
        alpha = i / (steps - 1)
        interpolated_latent = (1 - alpha) * latent_a + alpha * latent_b
        interpolated.append(interpolated_latent)
    return interpolated

def slerp(latent_a, latent_b, alpha):
    # Normalize vectors
    a_norm = latent_a / torch.norm(latent_a, dim=1, keepdim=True)
    b_norm = latent_b / torch.norm(latent_b, dim=1, keepdim=True)
    
    # Calculate angle
    dot = (a_norm * b_norm).sum(1)
    theta = torch.acos(torch.clamp(dot, -1, 1))
    
    # SLERP formula
    sin_theta = torch.sin(theta)
    wa = torch.sin((1 - alpha) * theta) / sin_theta
    wb = torch.sin(alpha * theta) / sin_theta
    
    return wa.unsqueeze(1) * latent_a + wb.unsqueeze(1) * latent_b

# ComfyUI workflow for latent exploration
[Latent A] → [Latent Interpolate] → [KSampler] → [Preview]
                ↑
          [Latent B]
          
# Advanced: Multi-dimensional navigation
[Center Latent] → [Add Noise Direction 1] → [Blend]
                → [Add Noise Direction 2] ↗

def masked_latent_composite(latents, masks):
    """Combine multiple latents using masks"""
    result = torch.zeros_like(latents[0])
    for latent, mask in zip(latents, masks):
        # Resize mask to latent dimensions
        mask_resized = F.interpolate(mask, size=latent.shape[-2:])
        result += latent * mask_resized
    return result

# Regional prompt structure
regions = [
    {
        "prompt": "detailed robot",
        "mask": left_half_mask,
        "weight": 1.2
    },
    {
        "prompt": "lush forest",
        "mask": right_half_mask,
        "weight": 1.0
    }
]

# Bounding box control
gligen_inputs = {
    "boxes": [[0.1, 0.1, 0.4, 0.4]],  # [x, y, w, h]
    "phrases": ["red car"],
    "strengths": [0.8]
}

# Switch prompts during generation
prompt_schedule = {
    0: "a cat sitting",
    10: "[a cat sitting|a dog sitting]",  # Alternate
    20: "a dog sitting"
}

# AND operation
"a cat AND a dog"  # Both must appear

# Weight distribution
"(cat:1.2) and (dog:0.8)"  # Cat emphasized

def cfg_rescale(noise_pred_conditional, noise_pred_unconditional, 
                guidance_scale, rescale_factor=0.7):
    """Rescale CFG to prevent oversaturation"""
    # Standard CFG
    noise_pred = noise_pred_unconditional + guidance_scale * \
                 (noise_pred_conditional - noise_pred_unconditional)
    
    # Rescale
    std_pos = noise_pred_conditional.std()
    std_cfg = noise_pred.std()
    
    rescale_factor = rescale_factor * (std_pos / std_cfg)
    return noise_pred * rescale_factor

def dynamic_thresholding(x, percentile=99.5):
    """Apply dynamic thresholding to prevent artifacts"""
    s = torch.quantile(
        x.abs().reshape(x.shape[0], -1), 
        percentile / 100, 
        dim=1
    )
    s = torch.maximum(s, torch.ones_like(s))
    return torch.clamp(x / s.view(-1, 1, 1, 1), -1, 1) * s.view(-1, 1, 1, 1)

def karras_schedule(n, sigma_min=0.002, sigma_max=80, rho=7):
    """Generate Karras noise schedule"""
    ramp = torch.linspace(0, 1, n)
    min_inv_rho = sigma_min ** (1 / rho)
    max_inv_rho = sigma_max ** (1 / rho)
    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas

def edm_sampler(x, model, sigmas, s_churn=0, s_noise=1):
    """EDM sampling with stochasticity control"""
    for i, (sigma, sigma_next) in enumerate(zip(sigmas[:-1], sigmas[1:])):
        # Add noise for stochasticity
        if s_churn > 0:
            gamma = min(s_churn / n, np.sqrt(2) - 1)
            eps = torch.randn_like(x) * s_noise
            sigma_hat = sigma * (1 + gamma)
            x = x + eps * (sigma_hat - sigma)
        
        # Denoise
        x = denoise_step(x, model, sigma_hat, sigma_next)
    return x

# Restart sampling workflow
steps = 50
restart_points = [20, 35]

for i in range(steps):
    if i in restart_points:
        # Add controlled noise and continue
        latents = latents + torch.randn_like(latents) * 0.1
    
    latents = sampler_step(latents, i)

# 4x upscale pipeline
stages = [
    {"size": 512, "denoise": 1.0},    # Base generation
    {"size": 768, "denoise": 0.6},    # First upscale
    {"size": 1024, "denoise": 0.4},   # Second upscale
    {"size": 2048, "denoise": 0.3},   # Final upscale
]

for stage in stages:
    if stage["size"] > 512:
        latent = upscale_latent(latent, stage["size"])
    
    latent = generate(latent, denoise=stage["denoise"])

# ComfyUI workflow
[Base Generation] → [Face Detector] → [Face Enhancement]
        ↓                                    ↓
   [Background]     →    [Composite]    ←  [Enhanced Face]
        ↓
   [Final Output]

# Multi-model style mixing
models = ["photorealistic.ckpt", "artistic.ckpt", "anime.ckpt"]
weights = [0.5, 0.3, 0.2]

# Generate with each model
latents = []
for model in models:
    latent = generate_with_model(prompt, model)
    latents.append(latent)

# Weighted combination
final_latent = sum(l * w for l, w in zip(latents, weights))

# Enable flash attention 2 for speed
config = {
    "use_flash_attention_2": True,
    "attention_slice_size": "auto",
    "attention_processor": "flash_attn",
    "enable_math": False,  # Disable for pure Flash Attention
    "enable_mem_efficient": True
}

# With torch.compile for additional speedup
model = torch.compile(model, mode="reduce-overhead")

def token_merging(tokens, merge_ratio=0.5):
    """Merge similar tokens to reduce computation"""
    # Calculate similarity matrix
    similarity = torch.matmul(tokens, tokens.transpose(-1, -2))
    
    # Find most similar pairs
    _, indices = similarity.topk(int(len(tokens) * merge_ratio))
    
    # Merge tokens
    merged = average_similar_tokens(tokens, indices)
    return merged

# Trade compute for memory
model.enable_gradient_checkpointing()

# Custom checkpointing
def checkpoint_forward(module, x):
    return torch.utils.checkpoint.checkpoint(
        module, x, use_reentrant=False
    )

# Generate in tiles for large images
def tiled_generation(size, tile_size=512, overlap=64):
    tiles = []
    for y in range(0, size[0], tile_size - overlap):
        for x in range(0, size[1], tile_size - overlap):
            tile = generate_tile(x, y, tile_size)
            tiles.append((x, y, tile))
    
    return blend_tiles(tiles, size, overlap)

def optimize_prompt(base_prompt, target_features):
    """Iteratively optimize prompt for target features"""
    current_prompt = base_prompt
    
    for iteration in range(10):
        # Generate with current prompt
        features = extract_features(generate(current_prompt))
        
        # Calculate feature difference
        diff = target_features - features
        
        # Update prompt based on difference
        current_prompt = update_prompt(current_prompt, diff)
        
        if convergence_reached(diff):
            break
    
    return current_prompt

def expand_prompt(simple_prompt):
    """Expand simple prompt with quality modifiers"""
    quality_tags = [
        "highly detailed", "4k", "professional",
        "award winning", "trending on artstation"
    ]
    
    style_hints = analyze_intended_style(simple_prompt)
    
    expanded = f"{simple_prompt}, {', '.join(quality_tags)}"
    if style_hints:
        expanded += f", {style_hints}"
    
    return expanded

def ancestral_sample_with_temp(noise_pred, sigma, temperature=1.0):
    """Ancestral sampling with temperature control"""
    # Add noise with temperature scaling
    noise = torch.randn_like(noise_pred) * temperature
    
    # Ancestral update
    sigma_down = (sigma**2 - sigma_next**2) ** 0.5
    x_next = x + sigma_down * (noise_pred + noise)
    
    return x_next

def importance_sampling(latents, condition, num_samples=5):
    """Generate multiple samples and select best"""
    samples = []
    scores = []
    
    for _ in range(num_samples):
        sample = generate_sample(latents, condition)
        score = evaluate_quality(sample, condition)
        samples.append(sample)
        scores.append(score)
    
    # Return best sample
    best_idx = torch.argmax(torch.tensor(scores))
    return samples[best_idx]

# Apply control at different resolutions
control_scales = [
    {"resolution": 256, "strength": 0.3},
    {"resolution": 512, "strength": 0.6},
    {"resolution": 1024, "strength": 1.0},
]

for scale in control_scales:
    control_resized = resize(control_image, scale["resolution"])
    apply_control(control_resized, scale["strength"])

def temporal_controlnet(frames, control_strength_decay=0.95):
    """Apply control with temporal decay"""
    previous_control = None
    controlled_frames = []
    
    for i, frame in enumerate(frames):
        if previous_control is not None:
            # Blend with previous frame's control
            current_control = blend_controls(
                extract_control(frame),
                previous_control,
                alpha=control_strength_decay
            )
        else:
            current_control = extract_control(frame)
        
        controlled_frame = generate_with_control(
            frame, current_control
        )
        controlled_frames.append(controlled_frame)
        previous_control = current_control
    
    return controlled_frames

# Consistency distillation (LCM/TCD style)
def consistency_distillation(teacher_model, student_model, x, t):
    """Train student for consistency"""
    # Teacher prediction
    with torch.no_grad():
        teacher_v = teacher_model(x, t)
        x_pred = predict_x0(x, teacher_v, t)
    
    # Student should match at adjacent timesteps
    t_next = get_adjacent_timestep(t)
    student_v = student_model(x, t_next)
    
    # Consistency loss
    loss = F.mse_loss(student_v, teacher_v)
    return loss

# GAN-based acceleration
def add_training(generator, discriminator, real_images):
    """Adversarial Diffusion Distillation"""
    # Generate with few steps
    fake_images = generator(noise, steps=4)
    
    # Discriminator loss
    d_real = discriminator(real_images)
    d_fake = discriminator(fake_images.detach())
    d_loss = gan_loss(d_real, d_fake)
    
    # Generator loss with perceptual component
    g_adv = discriminator(fake_images)
    g_loss = gan_loss(g_adv, real=True) + \
             perceptual_loss(fake_images, real_images)
    
    return g_loss, d_loss

def flow_matching_loss(model, x0, x1, t):
    """Rectified flow training"""
    # Interpolate between noise and data
    xt = t * x1 + (1 - t) * x0
    
    # Target velocity
    target_v = x1 - x0
    
    # Model prediction
    pred_v = model(xt, t)
    
    # Matching loss
    return F.mse_loss(pred_v, target_v)

# Sampling with flow
def sample_flow(model, x0, steps=50):
    """ODE sampling for rectified flows"""
    dt = 1.0 / steps
    xt = x0
    
    for i in range(steps):
        t = i * dt
        v = model(xt, t)
        xt = xt + v * dt
    
    return xt

# Compress latents with neural codec
encoded = neural_codec.encode(latent)  # Ultra-compressed
transmitted = send_over_network(encoded)
decoded = neural_codec.decode(transmitted)
final_image = vae.decode(decoded)

class BatchPipeline:
    def __init__(self, base_workflow):
        self.workflow = base_workflow
        self.results = []
    
    def process_batch(self, inputs, variations):
        for input_data in inputs:
            for variation in variations:
                # Apply variation
                modified_workflow = self.apply_variation(
                    self.workflow, variation
                )
                
                # Generate
                result = execute_workflow(
                    modified_workflow, input_data
                )
                
                self.results.append({
                    "input": input_data,
                    "variation": variation,
                    "output": result
                })
    
    def apply_variation(self, workflow, variation):
        # Modify workflow parameters
        return modified_workflow

def ab_test_parameters(base_config, test_params, num_samples=10):
    """Test different parameter combinations"""
    results = {}
    
    for param_name, param_values in test_params.items():
        param_results = []
        
        for value in param_values:
            # Update config
            test_config = base_config.copy()
            test_config[param_name] = value
            
            # Generate samples
            samples = generate_samples(test_config, num_samples)
            
            # Evaluate quality
            quality_score = evaluate_batch(samples)
            param_results.append((value, quality_score))
        
        results[param_name] = param_results
    
    return results

class RealTimeGenerator:
    """Optimized for <100ms generation"""
    
    def __init__(self, model_path):
        # Load optimized model
        self.model = load_lcm_model(model_path)
        self.model = torch.compile(self.model)
        
        # Pre-allocate tensors
        self.noise = torch.randn(1, 4, 64, 64).cuda()
        self.text_cache = {}
    
    @torch.inference_mode()
    def generate(self, prompt, seed=None):
        # Cache text encoding
        if prompt not in self.text_cache:
            self.text_cache[prompt] = encode_prompt(prompt)
        
        # Fast generation
        if seed:
            torch.manual_seed(seed)
        
        # 4-step LCM generation
        latents = self.noise.clone()
        for t in [999, 749, 499, 249]:
            latents = self.model(
                latents, t, 
                self.text_cache[prompt],
                guidance_scale=1.5
            )
        
        return decode_latents(latents)

class GenerationProfiler:
    def __init__(self):
        self.metrics = defaultdict(list)
    
    def profile_generation(self, workflow):
        with torch.profiler.profile() as prof:
            output = execute_workflow(workflow)
        
        # Extract metrics
        self.metrics["total_time"].append(prof.total_time)
        self.metrics["memory_used"].append(torch.cuda.max_memory_allocated())
        self.metrics["quality_score"].append(assess_quality(output))
        
        return output
    
    def get_report(self):
        return {
            metric: {
                "mean": np.mean(values),
                "std": np.std(values),
                "min": np.min(values),
                "max": np.max(values)
            }
            for metric, values in self.metrics.items()
        }

def differential_diffusion(x, mask, strength_map):
    """Apply different denoising strengths by region"""
    # Decompose into regions
    regions = segment_by_mask(x, mask)
    
    # Apply different schedules
    for region, strength in zip(regions, strength_map):
        region_schedule = modify_schedule(base_schedule, strength)
        regions[i] = denoise_region(region, region_schedule)
    
    return combine_regions(regions)

def self_attention_guidance(model, x, t, scale=0.5):
    """Enhance details using self-attention maps"""
    # Get attention maps
    _, attns = model(x, t, return_attention=True)
    
    # Blur attention for guidance
    blurred = gaussian_blur(attns, sigma=1.0)
    
    # Guided prediction
    pred = model(x, t)
    guided = pred + scale * (attns - blurred)
    
    return guided