Add work in progress experimental learned extraction nodes

__init__.py

@@ -28,6 +28,10 @@ from .nodes.dora_extract_wd import (
     DoRAExtractFixed, DoRAExtractRatio, DoRAExtractQuantile,
     DoRAExtractKnee, DoRAExtractFrobenius
 )
+from .nodes.dora_learned_wd import (
+    DoRALearnedExtractFixed, DoRALearnedExtractRatio, DoRALearnedExtractQuantile,
+    DoRALearnedExtractKnee, DoRALearnedExtractFrobenius
+)
 from .nodes.lora_resize import (
     LoRAResizeFixed, LoRAResizeRatio,
     LoRAResizeFrobenius, LoRAResizeCumulative,
@@ -68,6 +72,9 @@ class ModelUtilsExtension(ComfyExtension):
             # DoRA Extraction
             DoRAExtractFixed, DoRAExtractRatio, DoRAExtractQuantile,
             DoRAExtractKnee, DoRAExtractFrobenius,
+            # Learned DoRA Extraction
+            DoRALearnedExtractFixed, DoRALearnedExtractRatio, DoRALearnedExtractQuantile,
+            DoRALearnedExtractKnee, DoRALearnedExtractFrobenius,
             # LoRA Resize
             LoRAResizeFixed, LoRAResizeRatio,
             LoRAResizeFrobenius, LoRAResizeCumulative,

nodes/dora_learned_wd.py

@@ -0,0 +1,790 @@
+"""
+Learned DoRA Extraction via Optimization.
+
+Extracts Weight-Decomposed Low-Rank Adapters using gradient descent optimization
+to minimize the reconstruction MSE loss, matching the true non-linear DoRA formulation.
+Uses analytical SVD purely as the initialization seed.
+"""
+import fnmatch
+import os
+import re
+import math
+import torch
+import torch.linalg as linalg
+import folder_paths
+import comfy.utils
+from tqdm import tqdm
+from comfy_api.latest import io
+from .device_utils import (
+    estimate_model_size, prepare_for_large_operation,
+    cleanup_after_operation
+)
+
+from unifiedefficientloader import MemoryEfficientSafeOpen, transfer_to_gpu_pinned, IncrementalSafetensorsWriter
+
+# Import the existing SV ranking and chunking logic so we don't have to duplicate it
+from .dora_extract_wd import (
+    _compute_rank, _svd_extract_linear_lowrank, _svd_extract_linear,
+    _svd_extract_conv, _detect_fused_layer, _compile_patterns,
+    _matches_any_pattern, _build_lora_output_path, _get_model_inputs,
+    _format_lora_key, _get_common_inputs
+)
+
+
+def _adaptive_lr_update_cosine(curr_lr: float, improved: bool, worse_loss_counter: int, iteration: int, tensor_shape: tuple, min_lr: float, early_stop_stall: int, lr_factor: float = 0.95, lr_cooldown: int = 1) -> tuple:
+    M, N = tensor_shape
+    shape_ratio = abs(M - N) / max(M, N) if max(M, N) > 0 else 0
+
+    t = min(worse_loss_counter / max(early_stop_stall, 1), 1.0)
+    u_factor = (1 + math.cos(2 * math.pi * t)) / 2
+
+    if improved:
+        base_boost = 1.25
+        distance = base_boost - 1.0
+        scaled_distance = distance * (1.0 + 0.5 * shape_ratio)
+        boost_mult = 1.0 + scaled_distance * (1.0 - u_factor)
+        return min(curr_lr * boost_mult, 100.0), True
+    else:
+        cooldown = max(lr_cooldown, 1)
+        if iteration % cooldown != 0:
+            return curr_lr, False
+
+        min_decay = lr_factor - 0.03 * shape_ratio
+        max_decay = 0.995
+        decay_mult = min_decay + (max_decay - min_decay) * u_factor
+        return max(curr_lr * decay_mult, min_lr), True
+
+
+def _compute_shape_aware_plateau_params(M: int, N: int, lr_patience: int, lr_factor: float, lr_cooldown: int, lr_shape_influence: float = 1.0) -> tuple:
+    if lr_shape_influence > 0:
+        aspect_ratio = max(M, N) / min(M, N) if min(M, N) > 0 else 1.0
+        ar_factor = math.sqrt(aspect_ratio)
+        blend = lr_shape_influence
+
+        effective_patience = lr_patience
+        raw_factor = lr_factor
+        aggressive_factor = raw_factor**ar_factor
+        effective_factor = raw_factor + (aggressive_factor - raw_factor) * blend
+        effective_cooldown = lr_cooldown
+    else:
+        effective_patience = lr_patience
+        effective_factor = lr_factor
+        effective_cooldown = lr_cooldown
+
+    return effective_patience, effective_factor, effective_cooldown
+
+
+def _optimize_dora(
+    tensor_base: torch.Tensor,
+    tensor_ft: torch.Tensor,
+    init_down: torch.Tensor,
+    init_up: torch.Tensor,
+    optimize_iters: int,
+    learning_rate: float,
+    optimizer_type: str = "prodigy",
+    lr_schedule: str = "plateau",
+    lr_patience: int = 2,
+    lr_factor: float = 0.9,
+    lr_cooldown: int = 2,
+    early_stop_loss: float = 1e-6,
+    early_stop_stall: int = 2000,
+    early_stop_lr: float = 9.01e-9,
+    layer_name: str = "Layer",
+) -> tuple:
+    """
+    Run gradient descent to optimize lora_down and lora_up against the actual DoRA formula.
+
+    Args:
+        tensor_base: Base model weight
+        tensor_ft: Finetuned model weight
+        init_down: SVD-initialized lora_down matrix
+        init_up: SVD-initialized lora_up matrix
+        optimize_iters: Number of iterations for AdamW
+        learning_rate: Learning rate for AdamW
+        optimizer_type: Optimizer choice (adamw, radam, prodigy)
+        lr_schedule: LR Schedule choice (exponential, plateau, adaptive)
+        early_stop_loss: Target loss to stop optimizing
+        early_stop_stall: Number of non-improving iterations before stopping
+
+    Returns:
+        (optimized_down, optimized_up)
+    """
+    if optimize_iters <= 0:
+        return init_down, init_up
+
+    is_conv = tensor_base.ndim == 4
+    if is_conv:
+        out_ch, in_ch, kh, kw = tensor_base.shape
+        flat_base = tensor_base.reshape(out_ch, -1)
+        flat_ft = tensor_ft.reshape(out_ch, -1)
+
+        # Flatten init matrices for matmul: (out, rank) @ (rank, in*k*k)
+        rank = init_down.shape[0]
+        flat_up = init_up.reshape(out_ch, rank)
+        flat_down = init_down.reshape(rank, -1)
+    else:
+        flat_base = tensor_base
+        flat_ft = tensor_ft
+        flat_up = init_up
+        flat_down = init_down
+
+    # Magnitude of the finetuned model (fixed DoRA scale)
+    m = torch.linalg.norm(flat_ft, dim=1, keepdim=True).detach()
+    eps = 1e-8
+
+    # Set up learnable parameters
+    with torch.inference_mode(False), torch.enable_grad():
+        # Clone inference tensors to normal tensors for autograd
+        flat_base_opt = flat_base.clone()
+        flat_ft_opt = flat_ft.clone()
+        m_opt = m.clone()
+
+        # Free original tensors to save memory
+        del flat_base, flat_ft, m
+
+        B = torch.nn.Parameter(flat_up.clone())
+        A = torch.nn.Parameter(flat_down.clone())
+
+        opt_type = optimizer_type.lower()
+        if opt_type == "radam":
+            optimizer = torch.optim.RAdam([B, A], lr=learning_rate)
+        elif opt_type == "prodigy":
+            try:
+                from prodigyplus.prodigy_plus_schedulefree import ProdigyPlusScheduleFree
+                optimizer = ProdigyPlusScheduleFree([B, A], lr=learning_rate, use_schedulefree=False)
+            except ImportError:
+                print("ProdigyPlusScheduleFree optimizer not found. Falling back to AdamW. To install: pip install prodigyplus")
+                optimizer = torch.optim.AdamW([B, A], lr=learning_rate)
+        else:
+            optimizer = torch.optim.AdamW([B, A], lr=learning_rate)
+
+        # Learning rate scheduling and early stopping state
+        best_loss = float('inf')
+        worse_loss_counter = 0
+        plateau_counter = 0
+        cooldown_counter = 0
+        current_lr = learning_rate
+        schedule_type = lr_schedule.lower()
+
+        M_dim, N_dim = flat_base_opt.shape[0], flat_base_opt.shape[1]
+        eff_patience, eff_factor, eff_cooldown = _compute_shape_aware_plateau_params(M_dim, N_dim, lr_patience, lr_factor, lr_cooldown)
+
+        # Optimization loop
+        opt_pbar = tqdm(range(optimize_iters), desc=f"  └─ Optimizing {layer_name}", leave=False, dynamic_ncols=True, mininterval=0.5)
+        for i in opt_pbar:
+            optimizer.zero_grad()
+
+            # 1. Reconstruct directional weight: W_dir = W_base + B @ A
+            w_approx = flat_base_opt + (B @ A)
+
+            # 2. Normalize directional weight
+            approx_norm = torch.linalg.norm(w_approx, dim=1, keepdim=True) + eps
+
+            # 3. Scale by target magnitude
+            w_reconstructed = m_opt * (w_approx / approx_norm)
+
+            # 4. Compute MSE loss against actual finetuned weight
+            loss = torch.nn.functional.mse_loss(w_reconstructed, flat_ft_opt)
+            loss_val = loss.item()
+
+            if i == 0:
+                initial_loss = loss_val
+                best_loss = loss_val
+
+            loss.backward()
+            optimizer.step()
+
+            # --- Early Stopping & Schedulers ---
+            improved = False
+            if loss_val < best_loss * 0.9999: # 0.01% improvement threshold
+                best_loss = loss_val
+                worse_loss_counter = 0
+                plateau_counter = 0
+                improved = True
+            else:
+                worse_loss_counter += 1
+                plateau_counter += 1
+
+            if loss_val <= early_stop_loss:
+                break
+
+            if worse_loss_counter > early_stop_stall:
+                break
+
+            if current_lr <= early_stop_lr:
+                break
+
+            # LR Schedulers
+            if schedule_type == "adaptive":
+                current_lr, updated = _adaptive_lr_update_cosine(
+                    current_lr, improved, worse_loss_counter, i, (M_dim, N_dim), early_stop_lr, early_stop_stall, lr_factor, lr_cooldown
+                )
+                if updated:
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = current_lr
+            elif schedule_type == "plateau":
+                if cooldown_counter > 0:
+                    cooldown_counter -= 1
+                elif plateau_counter >= max(eff_patience, 1): # Decay if patience threshold reached
+                    current_lr = max(current_lr * eff_factor, early_stop_lr)
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = current_lr
+                    plateau_counter = 0 # reset counter after decay
+                    cooldown_counter = eff_cooldown
+            elif schedule_type == "exponential":
+                current_lr = max(current_lr * 0.99, early_stop_lr)
+                for param_group in optimizer.param_groups:
+                    param_group['lr'] = current_lr
+
+            if i % 10 == 0 or i == optimize_iters - 1:
+                if schedule_type == "plateau":
+                    opt_pbar.set_postfix({"loss": f"{loss_val:.4e}", "lr": f"{current_lr:.2e}", "plat": f"{plateau_counter}/{eff_patience}"}, refresh=False)
+                else:
+                    opt_pbar.set_postfix({"loss": f"{loss_val:.4e}", "lr": f"{current_lr:.2e}", "stall": f"{worse_loss_counter}"}, refresh=False)
+
+        final_loss = loss.item()
+        opt_pbar.close()
+        print(f"  └─ {layer_name} | Loss: {initial_loss:.6f} -> {final_loss:.6f} (stopped at iter {i+1})")
+
+        final_up = B.detach()
+        final_down = A.detach()
+
+    # Reshape back to original dimensions
+    if is_conv:
+        final_up = final_up.reshape(out_ch, rank, 1, 1)
+        final_down = final_down.reshape(rank, in_ch, kh, kw)
+
+    return final_down, final_up
+
+
+def _extract_chunked_learned_layer(
+    tensor_base: torch.Tensor,
+    tensor_ft: torch.Tensor,
+    num_chunks: int,
+    mode: str,
+    mode_param: float,
+    device: str,
+    max_rank: int = None,
+    optimize_iters: int = 500,
+    learning_rate: float = 1.0,
+    optimizer_type: str = "prodigy",
+    lr_schedule: str = "plateau",
+    lr_patience: int = 2,
+    lr_factor: float = 0.9,
+    lr_cooldown: int = 2,
+    early_stop_loss: float = 1e-6,
+    early_stop_stall: int = 2000,
+    early_stop_lr: float = 9.01e-9,
+    layer_name: str = "Layer",
+) -> tuple:
+    """Extract Learned DoRA from fused layer by chunking."""
+    out_dim, in_dim = tensor_base.shape
+    chunk_size = out_dim // num_chunks
+
+    all_lora_up = []
+    all_lora_down = []
+
+    for i in range(num_chunks):
+        chunk_base = tensor_base[i * chunk_size:(i + 1) * chunk_size, :]
+        chunk_ft = tensor_ft[i * chunk_size:(i + 1) * chunk_size, :]
+
+        norm_base = torch.linalg.norm(chunk_base, dim=1, keepdim=True)
+        norm_ft = torch.linalg.norm(chunk_ft, dim=1, keepdim=True)
+        eps = 1e-8
+
+        w_dir = chunk_ft * (norm_base / (norm_ft + eps))
+        weight_diff = w_dir - chunk_base
+
+        try:
+            result, mode_str = _svd_extract_linear(weight_diff, mode, mode_param, device, max_rank)
+            if mode_str == "full":
+                return None, None, 0
+            lora_down, lora_up, _ = result
+
+            # Apply learned optimization
+            lora_down, lora_up = _optimize_dora(
+                chunk_base, chunk_ft, lora_down, lora_up, optimize_iters, learning_rate,
+                optimizer_type, lr_schedule, lr_patience, lr_factor, lr_cooldown,
+                early_stop_loss, early_stop_stall, early_stop_lr,
+                layer_name=f"{layer_name} (Chunk {i+1}/{num_chunks})"
+            )
+
+            all_lora_down.append(lora_down)
+            all_lora_up.append(lora_up)
+        except Exception:
+            return None, None, 0
+
+    combined_lora_up = torch.cat(all_lora_up, dim=0)
+    combined_lora_down = all_lora_down[0]
+    rank = combined_lora_down.shape[0]
+
+    return combined_lora_up, combined_lora_down, rank
+
+
+def extract_dora_learned_from_files(
+    model_a_path: str,
+    model_b_path: str,
+    mode: str,
+    linear_param: float,
+    conv_param: float,
+    device: str,
+    save_dtype: str,
+    output_path: str,
+    optimize_iters: int = 500,
+    learning_rate: float = 1.0,
+    optimizer_type: str = "prodigy",
+    lr_schedule: str = "plateau",
+    lr_patience: int = 2,
+    lr_factor: float = 0.9,
+    lr_cooldown: int = 2,
+    early_stop_loss: float = 1e-6,
+    early_stop_stall: int = 2000,
+    early_stop_lr: float = 9.01e-9,
+    linear_max_rank: int = None,
+    conv_max_rank: int = None,
+    clamp_quantile: float = 0.99,
+    min_diff: float = 0.0,
+    skip_patterns_str: str = "",
+    mismatch_mode: str = "skip",
+    chunk_large_layers: bool = True,
+    svd_niter: int = 2,
+    lazy_load: bool = True,
+    force_clear_cache: bool = True,
+    glob_skip_patterns: bool = False,
+) -> None:
+    """
+    Extract Learned DoRA from difference between two models.
+    """
+    save_torch_dtype = {
+        "fp32": torch.float32,
+        "fp16": torch.float16,
+        "bf16": torch.bfloat16,
+    }.get(save_dtype, torch.float16)
+
+    skip_patterns = _compile_patterns(skip_patterns_str, glob_mode=glob_skip_patterns)
+
+    total_size_gb = estimate_model_size(model_a_path) + estimate_model_size(model_b_path)
+    print(f"[Learned DoRA Extract] Preparing memory for {total_size_gb:.2f}GB operation...")
+    prepare_for_large_operation(total_size_gb * 1.5, torch.device(device))
+
+    handler_a = MemoryEfficientSafeOpen(model_a_path, low_memory=lazy_load)
+    handler_b = MemoryEfficientSafeOpen(model_b_path, low_memory=lazy_load)
+
+    try:
+        keys_a = set(handler_a.keys())
+        keys_b = set(handler_b.keys())
+        weight_keys = [k for k in keys_a if k.endswith(".weight")]
+        pbar = comfy.utils.ProgressBar(len(weight_keys))
+        stats = {"extracted": 0, "full": 0, "skipped": 0, "chunked": 0}
+
+        def _process_layer(key):
+            lora_name = _format_lora_key(key)
+
+            if _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns):
+                return "skipped", None
+
+            use_pinned = device == 'cuda'
+
+            if key not in keys_b:
+                if mismatch_mode == "skip":
+                    return "skipped", None
+                if mismatch_mode == "error":
+                    raise ValueError(f"Key {key} not found in model B")
+
+                # Cannot extract true DoRA without base. Fallback to diff.
+                cpu_a = handler_a.get_tensor(key)
+                tensor_a = transfer_to_gpu_pinned(cpu_a, device, torch.float32) if use_pinned else cpu_a.to(device=device, dtype=torch.float32)
+                del cpu_a
+                weight_diff = tensor_a
+                tensor_base = torch.zeros_like(tensor_a)
+                tensor_ft = tensor_a
+                dora_scale = None
+            else:
+                cpu_a = handler_a.get_tensor(key)
+                cpu_b = handler_b.get_tensor(key)
+
+                if use_pinned:
+                    tensor_a = transfer_to_gpu_pinned(cpu_a, device, torch.float32)
+                    tensor_b = transfer_to_gpu_pinned(cpu_b, device, torch.float32)
+                else:
+                    tensor_a = cpu_a.to(device=device, dtype=torch.float32)
+                    tensor_b = cpu_b.to(device=device, dtype=torch.float32)
+                del cpu_a, cpu_b
+
+                if tensor_a.shape != tensor_b.shape:
+                    if mismatch_mode == "skip":
+                        del tensor_a, tensor_b
+                        return "skipped", None
+                    if mismatch_mode == "error":
+                        raise ValueError(f"Shape mismatch for {key}: {tensor_a.shape} vs {tensor_b.shape}")
+
+                    weight_diff = tensor_a
+                    tensor_base = torch.zeros_like(tensor_a)
+                    tensor_ft = tensor_a
+                    dora_scale = None
+                    del tensor_b
+                else:
+                    tensor_base = tensor_b
+                    tensor_ft = tensor_a
+
+                    is_conv_dim = tensor_a.ndim == 4
+                    if is_conv_dim:
+                        out_ch = tensor_a.shape[0]
+                        flat_a = tensor_a.reshape(out_ch, -1)
+                        flat_b = tensor_b.reshape(out_ch, -1)
+                        norm_a = torch.linalg.norm(flat_a, dim=1, keepdim=True)
+                        norm_b = torch.linalg.norm(flat_b, dim=1, keepdim=True)
+                    else:
+                        norm_a = torch.linalg.norm(tensor_a, dim=1, keepdim=True)
+                        norm_b = torch.linalg.norm(tensor_b, dim=1, keepdim=True)
+
+                    eps = 1e-8
+                    norm_ratio = norm_b / (norm_a + eps)
+                    if is_conv_dim:
+                        norm_ratio = norm_ratio.view(-1, 1, 1, 1)
+                        dora_scale = norm_a.view(-1, 1, 1, 1)
+                    else:
+                        dora_scale = norm_a
+
+                    w_dir = tensor_a * norm_ratio
+                    weight_diff = w_dir - tensor_b
+                    del w_dir
+
+            if min_diff > 0 and weight_diff.abs().max() < min_diff:
+                del weight_diff, tensor_base, tensor_ft
+                return "skipped", None
+
+            if weight_diff.ndim < 2:
+                del weight_diff, tensor_base, tensor_ft
+                return "skipped", None
+
+            is_conv = weight_diff.ndim == 4
+            layer_results = {}
+
+            try:
+                # 1. Run Analytical SVD (Initialization)
+                if is_conv:
+                    result, mode_str = _svd_extract_conv(
+                        weight_diff, mode, conv_param, device, conv_max_rank, clamp_quantile
+                    )
+                else:
+                    result, mode_str = _svd_extract_linear(
+                        weight_diff, mode, linear_param, device, linear_max_rank, clamp_quantile, svd_niter
+                    )
+
+                # 2. Run Optimization (Learned Rounding)
+                if mode_str != "full" and dora_scale is not None:
+                    lora_down, lora_up, _ = result
+                    lora_down, lora_up = _optimize_dora(
+                        tensor_base, tensor_ft, lora_down, lora_up, optimize_iters, learning_rate,
+                        optimizer_type, lr_schedule, lr_patience, lr_factor, lr_cooldown,
+                        early_stop_loss, early_stop_stall, early_stop_lr,
+                        layer_name=lora_name
+                    )
+                    result = (lora_down, lora_up, _)
+
+            except Exception as e:
+                if chunk_large_layers and not is_conv:
+                    num_chunks = _detect_fused_layer(key, weight_diff.shape)
+                    if num_chunks > 1:
+                        print(f"[Learned DoRA] Chunked: {key} ({num_chunks} chunks)")
+                        lora_up, lora_down, rank = _extract_chunked_learned_layer(
+                            tensor_base, tensor_ft, num_chunks, mode, linear_param, device, linear_max_rank, optimize_iters, learning_rate,
+                            optimizer_type, lr_schedule, lr_patience, lr_factor, lr_cooldown,
+                            early_stop_loss, early_stop_stall, early_stop_lr,
+                            layer_name=lora_name
+                        )
+                        if lora_up is not None:
+                            layer_results[f"{lora_name}.lora_up.weight"] = lora_up.to(save_torch_dtype).cpu().contiguous()
+                            layer_results[f"{lora_name}.lora_down.weight"] = lora_down.to(save_torch_dtype).cpu().contiguous()
+                            layer_results[f"{lora_name}.alpha"] = torch.tensor(rank, dtype=save_torch_dtype)
+                            if dora_scale is not None:
+                                layer_results[f"{lora_name}.dora_scale"] = dora_scale.to(save_torch_dtype).cpu().contiguous()
+                            del weight_diff, tensor_base, tensor_ft
+                            return "chunked", layer_results
+
+                print(f"[Learned DoRA] Failed: {key}: {e}")
+                del weight_diff, tensor_base, tensor_ft
+                return "skipped", None
+
+            if mode_str == "full":
+                layer_results[f"{lora_name}.diff"] = weight_diff.to(save_torch_dtype).cpu().contiguous()
+                status = "full"
+            else:
+                lora_down, lora_up, _ = result
+                rank = lora_down.shape[0]
+                layer_results[f"{lora_name}.lora_down.weight"] = lora_down.to(save_torch_dtype).cpu().contiguous()
+                layer_results[f"{lora_name}.lora_up.weight"] = lora_up.to(save_torch_dtype).cpu().contiguous()
+                layer_results[f"{lora_name}.alpha"] = torch.tensor(rank, dtype=save_torch_dtype)
+                if dora_scale is not None:
+                    layer_results[f"{lora_name}.dora_scale"] = dora_scale.to(save_torch_dtype).cpu().contiguous()
+                status = "extracted"
+
+            del weight_diff, tensor_base, tensor_ft
+            return status, layer_results
+
+        writer = IncrementalSafetensorsWriter(output_path)
+        writer.__enter__()
+        try:
+            for key in tqdm(weight_keys, desc="Extracting Learned DoRA", unit="layers"):
+                status, layer_sd = _process_layer(key)
+                stats[status] += 1
+                if layer_sd:
+                    writer.write_dict(layer_sd)
+
+                if force_clear_cache:
+                    import gc
+                    gc.collect()
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+
+                pbar.update(1)
+        finally:
+            writer.__exit__(None, None, None)
+
+        print(f"[Learned DoRA] Done: {stats['extracted']} extracted, {stats['chunked']} chunked, "
+              f"{stats['full']} full, {stats['skipped']} skipped")
+
+    finally:
+        handler_a.__exit__(None, None, None)
+        handler_b.__exit__(None, None, None)
+        cleanup_after_operation()
+
+
+# =============================================================================
+# Node Definitions
+# =============================================================================
+
+def _get_learned_inputs():
+    return [
+        io.Int.Input("optimize_iters", default=500, min=0, max=10000,
+                    tooltip="Number of gradient descent iterations (0 = skip optimization)"),
+        io.Float.Input("learning_rate", default=1.0, min=0.0001, max=100.0, step=0.001,
+                      tooltip="Base learning rate. AdamW prefers ~0.01, Prodigy prefers ~1.0"),
+        io.Combo.Input("optimizer", options=["prodigy", "adamw", "radam"], default="prodigy",
+                      tooltip="Optimization algorithm"),
+        io.Combo.Input("lr_schedule", options=["plateau", "adaptive", "exponential", "constant"], default="plateau",
+                      tooltip="Learning rate scheduling strategy"),
+        io.Int.Input("lr_patience", default=2, min=0, max=1000, tooltip="Steps to wait before decaying LR (Plateau)"),
+        io.Float.Input("lr_factor", default=0.9, min=0.01, max=1.0, step=0.01, tooltip="Factor to decay LR by (Plateau)"),
+        io.Int.Input("lr_cooldown", default=2, min=0, max=1000, tooltip="Steps to wait after decay before checking again (Plateau)"),
+        io.Float.Input("early_stop_loss", default=1e-6, min=0.0, max=1.0, step=1e-9,
+                      tooltip="Stop early if MSE loss drops below this value"),
+        io.Int.Input("early_stop_stall", default=2000, min=0, max=10000,
+                      tooltip="Stop early if loss doesn't improve for this many iterations"),
+        io.Float.Input("early_stop_lr", default=9.01e-9, min=0.0, max=1.0, step=1e-10,
+                      tooltip="Stop early if learning rate drops below this value"),
+    ]
+
+
+class DoRALearnedExtractFixed(io.ComfyNode):
+    """Extract Learned DoRA with fixed rank."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DoRALearnedExtractFixed",
+            display_name="DoRA Learned Extract (Fixed Rank) (WIP)",
+            category="ModelUtils/DoRA",
+            description="Extract DoRA via MSE Loss minimization with fixed rank.",
+            is_experimental=True,
+            inputs=[
+                *_get_model_inputs(),
+                io.Int.Input("linear_dim", default=64, min=1, max=16384, tooltip="Rank for linear/attention layers"),
+                io.Int.Input("conv_dim", default=32, min=1, max=16384, tooltip="Rank for conv layers"),
+                *_get_learned_inputs(),
+                io.Int.Input("svd_niter", default=2, min=0, max=10, tooltip="SVD iterations for initialization"),
+                *_get_common_inputs(),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, model_a, model_b, linear_dim, conv_dim, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, svd_niter, chunk_large_layers,
+                clamp_quantile, min_diff, mismatch_mode, output_filename,
+                save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
+
+        model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
+        model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
+        output_path = _build_lora_output_path(output_filename)
+
+        extract_dora_learned_from_files(
+            model_a_path, model_b_path, "fixed", linear_dim, conv_dim,
+            device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_dim, conv_dim,
+            clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers, svd_niter,
+            lazy_load, force_clear_cache, glob_skip_patterns
+        )
+
+        return io.NodeOutput(output_path)
+
+
+class DoRALearnedExtractRatio(io.ComfyNode):
+    """Extract Learned DoRA with singular value ratio threshold."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DoRALearnedExtractRatio",
+            display_name="DoRA Learned Extract (Ratio) (WIP)",
+            category="ModelUtils/DoRA",
+            description="Learned extraction keeping SVs > max(S) / ratio.",
+            is_experimental=True,
+            inputs=[
+                *_get_model_inputs(),
+                io.Float.Input("linear_ratio", default=2.0, min=1.0, max=100.0, step=0.1),
+                io.Float.Input("conv_ratio", default=2.0, min=1.0, max=100.0, step=0.1),
+                io.Int.Input("linear_max_rank", default=128, min=1, max=16384),
+                io.Int.Input("conv_max_rank", default=128, min=1, max=16384),
+                *_get_learned_inputs(),
+                *_get_common_inputs(),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, model_a, model_b, linear_ratio, conv_ratio, linear_max_rank, conv_max_rank,
+                optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
+                output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
+
+        model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
+        model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
+        output_path = _build_lora_output_path(output_filename)
+
+        extract_dora_learned_from_files(
+            model_a_path, model_b_path, "ratio", linear_ratio, conv_ratio,
+            device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
+            clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
+            lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
+        )
+
+        return io.NodeOutput(output_path)
+
+
+class DoRALearnedExtractQuantile(io.ComfyNode):
+    """Extract Learned DoRA with cumulative singular value quantile."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DoRALearnedExtractQuantile",
+            display_name="DoRA Learned Extract (Quantile) (WIP)",
+            category="ModelUtils/DoRA",
+            description="Learned extraction keeping enough SVs to reach target percentage.",
+            is_experimental=True,
+            inputs=[
+                *_get_model_inputs(),
+                io.Float.Input("linear_quantile", default=0.9, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("conv_quantile", default=0.9, min=0.0, max=1.0, step=0.01),
+                io.Int.Input("linear_max_rank", default=128, min=1, max=16384),
+                io.Int.Input("conv_max_rank", default=128, min=1, max=16384),
+                *_get_learned_inputs(),
+                *_get_common_inputs(),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, model_a, model_b, linear_quantile, conv_quantile, linear_max_rank, conv_max_rank,
+                optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
+                output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
+
+        model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
+        model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
+        output_path = _build_lora_output_path(output_filename)
+
+        extract_dora_learned_from_files(
+            model_a_path, model_b_path, "quantile", linear_quantile, conv_quantile,
+            device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
+            clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
+            lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
+        )
+
+        return io.NodeOutput(output_path)
+
+
+class DoRALearnedExtractKnee(io.ComfyNode):
+    """Extract Learned DoRA with automatic knee detection."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DoRALearnedExtractKnee",
+            display_name="DoRA Learned Extract (Knee Detection) (WIP)",
+            category="ModelUtils/DoRA",
+            description="Learned extraction automatically finding optimal rank using knee detection.",
+            is_experimental=True,
+            inputs=[
+                *_get_model_inputs(),
+                io.Combo.Input("knee_method", options=["sv_knee", "sv_cumulative_knee"], default="sv_knee"),
+                io.Int.Input("linear_max_rank", default=128, min=1, max=16384),
+                io.Int.Input("conv_max_rank", default=128, min=1, max=16384),
+                *_get_learned_inputs(),
+                *_get_common_inputs(),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, model_a, model_b, knee_method, linear_max_rank, conv_max_rank,
+                optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
+                output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
+
+        model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
+        model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
+        output_path = _build_lora_output_path(output_filename)
+
+        extract_dora_learned_from_files(
+            model_a_path, model_b_path, knee_method, 0, 0,
+            device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
+            clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
+            lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
+        )
+
+        return io.NodeOutput(output_path)
+
+
+class DoRALearnedExtractFrobenius(io.ComfyNode):
+    """Extract Learned DoRA preserving target fraction of Frobenius norm."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DoRALearnedExtractFrobenius",
+            display_name="DoRA Learned Extract (Frobenius) (WIP)",
+            category="ModelUtils/DoRA",
+            description="Learned extraction preserving target fraction of Frobenius norm.",
+            is_experimental=True,
+            inputs=[
+                *_get_model_inputs(),
+                io.Float.Input("linear_target", default=0.9, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("conv_target", default=0.9, min=0.0, max=1.0, step=0.01),
+                io.Int.Input("linear_max_rank", default=128, min=1, max=16384),
+                io.Int.Input("conv_max_rank", default=128, min=1, max=16384),
+                *_get_learned_inputs(),
+                *_get_common_inputs(),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, model_a, model_b, linear_target, conv_target, linear_max_rank, conv_max_rank,
+                optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
+                output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
+
+        model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
+        model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
+        output_path = _build_lora_output_path(output_filename)
+
+        extract_dora_learned_from_files(
+            model_a_path, model_b_path, "sv_fro", linear_target, conv_target,
+            device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
+            clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
+            lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
+        )
+
+        return io.NodeOutput(output_path)