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bmaltais 2025-05-18 12:55:40 -04:00
parent 4f1405c1c7
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tools/extract_lora_from_models-nw.py
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@ -1,20 +1,46 @@
import sys
import os
# 1. Add sd-scripts directory to sys.path
script_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(script_dir)
sd_scripts_dir_path = os.path.join(project_root, "sd-scripts")
if sd_scripts_dir_path not in sys.path:
sys.path.insert(0, sd_scripts_dir_path)
# Now you can import from the library package and the networks package
try:
from library import sai_model_spec, model_util, sdxl_model_util
from library.utils import setup_logging
from networks import lora # <--- CORRECTED LORA IMPORT
except ImportError as e:
print(f"Error importing from sd-scripts. Please check your sd-scripts folder structure.")
print(f"Attempted to load from: {sd_scripts_dir_path}")
print(f"Original error: {e}")
print("Current sys.path relevant entries:")
for p in sys.path:
if "sd-scripts" in p or "kohya_ss" in p: # Print relevant paths for debugging
print(p)
# Ensure 'networks' directory exists in 'sd-scripts' and contains 'lora.py'
# Also ensure 'sd-scripts/networks/__init__.py' exists.
raise
# --- The rest of your script ---
import argparse
import json
import os
# import os # Already imported
import time
import torch
from safetensors.torch import load_file, save_file
from tqdm import tqdm
from library import sai_model_spec, model_util, sdxl_model_util
import lora # Assuming this is your existing lora script/library
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
MIN_SV = 1e-6
# ... (Keep all your existing helper functions: index_sv_cumulative, index_sv_fro, etc.)
def index_sv_cumulative(S, target):
original_sum = float(torch.sum(S))
cumulative_sums = torch.cumsum(S, dim=0) / original_sum
@ -38,72 +64,161 @@ def index_sv_ratio(S, target):
return index
def index_sv_knee_improved(S, MIN_SV_KNEE=1e-8): # MIN_SV_KNEE can be same as global MIN_SV or specific
"""
Determine rank using the knee point detection method with normalization.
Normalizes singular values and their indices to the [0,1] range
to make the knee detection scale-invariant.
"""
n = len(S)
if n < 3: return 1
if n < 3: # Need at least 3 points to detect a knee
return 1
# S is expected to be sorted in descending order.
s_max, s_min = S[0], S[-1]
if s_max - s_min < MIN_SV_KNEE: return 1
# Handle flat or nearly flat singular value spectrum
if s_max - s_min < MIN_SV_KNEE:
# If all singular values are almost the same, a knee is not well-defined.
# Returning 1 is a conservative choice for low rank.
# Alternatively, n // 2 or n - 1 could be chosen depending on desired behavior.
return 1
# Normalize singular values to [0, 1]
# s_normalized[0] will be 1, s_normalized[n-1] will be 0
s_normalized = (S - s_min) / (s_max - s_min)
# Normalize indices to [0, 1]
# x_normalized[0] will be 0, x_normalized[n-1] will be 1
x_normalized = torch.linspace(0, 1, n, device=S.device, dtype=S.dtype)
# The line in normalized space connects (x_norm[0], s_norm[0]) to (x_norm[n-1], s_norm[n-1])
# This is (0, 1) to (1, 0).
# The equation of the line passing through (0,1) and (1,0) is x + y - 1 = 0.
# So, A=1, B=1, C=-1 for the line equation Ax + By + C = 0.
# Calculate the perpendicular distance from each point (x_normalized[i], s_normalized[i]) to this line.
# Distance = |A*x_i + B*y_i + C| / sqrt(A^2 + B^2)
# Distance = |1*x_normalized + 1*s_normalized - 1| / sqrt(1^2 + 1^2)
# Distance = |x_normalized + s_normalized - 1| / sqrt(2)
# The sqrt(2) denominator is constant and doesn't affect argmax, so it can be omitted for finding the index.
distances = (x_normalized + s_normalized - 1).abs()
# Find the 0-based index of the point with the maximum distance.
knee_index_0based = torch.argmax(distances).item()
# Convert 0-based index to 1-based rank.
rank = knee_index_0based + 1
# Clamp rank similar to original: must be > 0 and <= n-1 (typical for rank reduction)
# If knee_index_0based is n-1 (last point), rank becomes n. min(n, n-1) results in n-1.
rank = max(1, min(rank, n - 1))
return rank
def index_sv_cumulative_knee(S, min_sv_threshold=1e-8):
"""
Determine rank using the knee point detection method on the normalized cumulative sum of singular values.
This method identifies a point where adding more singular values contributes diminishingly to the total sum.
"""
n = len(S)
if n < 3: return 1
if n < 3: # Need at least 3 points to detect a knee
return 1
s_sum = torch.sum(S)
if s_sum < min_sv_threshold: return 1
# If all singular values are zero or very small, return rank 1.
if s_sum < min_sv_threshold:
return 1
# Calculate cumulative sum of singular values, normalized by the total sum.
# y_values[0] = S[0]/s_sum, ..., y_values[n-1] = 1.0
y_values = torch.cumsum(S, dim=0) / s_sum
y_min, y_max = y_values[0], y_values[n-1]
if y_max - y_min < min_sv_threshold: return 1
# Normalize these y_values (cumulative sums) to the range [0,1] for knee detection.
y_min, y_max = y_values[0], y_values[n-1] # y_max is typically 1.0
# If the normalized cumulative sum curve is very flat (e.g., S[0] captures almost all energy),
# it implies the first few components are dominant.
if y_max - y_min < min_sv_threshold: # Using min_sv_threshold here as a sensitivity for flatness
# This condition means (S[0] + ... + S[n-1]) - S[0] is small relative to sum(S) if n>1
# Effectively, S[1:] components are negligible.
return 1
# y_norm[0] = 0, y_norm[n-1] = 1 (represents the normalized cumulative sum from start to end)
y_norm = (y_values - y_min) / (y_max - y_min)
# x_values are indices, normalized to [0, 1]
# x_norm[0] = 0, ..., x_norm[n-1] = 1
x_norm = torch.linspace(0, 1, n, device=S.device, dtype=S.dtype)
distances = (y_norm - x_norm).abs()
# The "knee" is the point on the curve (x_norm[i], y_norm[i]) that is farthest
# from the line connecting the start and end of this normalized curve.
# In this normalized space, the line connects (0,0) to (1,1).
# The equation of this line is Y = X, or X - Y = 0.
# The distance from a point (x_i, y_i) to the line X - Y = 0 is |x_i - y_i| / sqrt(1^2 + (-1)^2).
# We can maximize |x_i - y_i| (or |y_i - x_i|) as sqrt(2) is a constant factor.
distances = (y_norm - x_norm).abs() # y_norm is expected to be >= x_norm for a concave cumulative curve.
# Find the 0-based index of the point with the maximum distance.
knee_index_0based = torch.argmax(distances).item()
# Convert 0-based index to 1-based rank.
rank = knee_index_0based + 1
# Clamp rank to be between 1 and n-1 (as n elements give n-1 possible ranks for truncation).
# A rank of n means no truncation. n-1 is the highest sensible rank for reduction.
rank = max(1, min(rank, n - 1))
return rank
def index_sv_rel_decrease(S, tau=0.1):
if len(S) < 2: return 1
ratios = S[1:] / S[:-1]
for k in range(len(ratios)):
if ratios[k] < tau:
return max(1, k + 1)
return min(len(S), len(S) - 1 if len(S) > 1 else 1)
def save_to_file(file_name, model_sd, dtype, metadata=None): # Changed model to model_sd for clarity
if dtype is not None:
for key in list(model_sd.keys()):
if isinstance(model_sd[key], torch.Tensor):
model_sd[key] = model_sd[key].to(dtype)
"""Determine rank based on relative decrease threshold."""
if len(S) < 2:
# For matrices with fewer than 2 singular values, a relative decrease
# isn't meaningful. Returning 1 is a sensible default.
return 1
# Filter out non-tensor metadata if it accidentally gets into model_sd
final_sd = {k: v for k, v in model_sd.items() if isinstance(v, torch.Tensor)}
# Compute ratios of consecutive singular values
# S is sorted descending, so S[:-1] >= S[1:]
# ratios will be <= 1.0
ratios = S[1:] / S[:-1] # Example: S=[10,1,0.5], ratios=[0.1, 0.5]
# Find the smallest k such that S[k+1]/S[k] < tau.
# The rank would then be k+1, as we include S[k].
for k in range(len(ratios)): # k ranges from 0 to len(S)-2
if ratios[k] < tau:
# We found a significant drop after the k-th singular value.
# So, we keep k+1 singular values (indices 0 to k).
# The rank is k+1. Since k >= 0, k+1 >= 1.
return k + 1
# If no drop below tau was found, it means all relative decreases were >= tau.
# In this case, this method suggests using all available singular values.
# The actual rank will be capped later by args.dim/conv_dim and matrix dimensions.
return len(S)
def save_to_file(file_name, model_to_save, state_dict_content, dtype, metadata=None): # Renamed params for clarity
if dtype is not None:
for key in list(state_dict_content.keys()):
if isinstance(state_dict_content[key], torch.Tensor):
state_dict_content[key] = state_dict_content[key].to(dtype)
# save_file from safetensors expects a state_dict as the first argument if metadata is also passed.
# torch.save would also expect the state_dict.
# The 'model' variable being passed to save_file should be the state_dict itself.
if os.path.splitext(file_name)[1] == ".safetensors":
save_file(final_sd, file_name, metadata=metadata) # Pass metadata here
save_file(model_to_save, file_name, metadata=metadata) # Pass metadata correctly
else:
# For .pt, metadata is typically not saved in this manner.
# If you need to save metadata with .pt, you might save a dict like {'state_dict': final_sd, 'metadata': metadata}
torch.save(final_sd, file_name)
if metadata:
logger.warning(".pt format does not standardly support metadata like safetensors. Metadata not saved in file.")
torch.save(model_to_save, file_name)
def svd_decomposition(
model_org_path=None, # Renamed for clarity
model_tuned_path=None, # Renamed for clarity
def svd(
model_org=None,
model_tuned=None,
save_to=None,
algo="lora", # New: lora or loha
network_dim=4, # For LoRA: rank. For LoHA: "factor" or "hada_dim"
network_alpha=None, # For LoRA: alpha (often same as rank). For LoHA: "rank_initial" or "hada_alpha"
conv_dim=None, # For LoRA: conv_rank. For LoHA: "conv_factor"
conv_alpha=None, # For LoRA: conv_alpha. For LoHA: "conv_rank_initial"
dim=4,
v2=None,
sdxl=None,
conv_dim=None,
v_parameterization=None,
device=None,
save_precision=None,
@ -118,9 +233,12 @@ def svd_decomposition(
verbose=False,
):
def str_to_dtype(p):
if p == "float": return torch.float
if p == "fp16": return torch.float16
if p == "bf16": return torch.bfloat16
if p == "float":
return torch.float
if p == "fp16":
return torch.float16
if p == "bf16":
return torch.bfloat16
return None
assert not (v2 and sdxl), "v2 and sdxl cannot be specified at the same time"
@ -128,395 +246,300 @@ def svd_decomposition(
load_dtype = str_to_dtype(load_precision) if load_precision else None
save_dtype = str_to_dtype(save_precision) if save_precision else torch.float
work_device = "cpu" # Perform SVD and weight manipulation on CPU then move
compute_device = device if device else "cpu"
# Handle default alpha values based on dim values if not provided
if network_alpha is None: network_alpha = network_dim
if conv_dim is None: conv_dim = network_dim # default conv_dim to network_dim
if conv_alpha is None: conv_alpha = conv_dim # default conv_alpha to conv_dim
work_device = "cpu"
# Load models
if not sdxl:
logger.info(f"Loading original SD model: {model_org_path}")
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_org_path, load_dtype)
logger.info(f"Loading original SD model: {model_org}")
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_org)
text_encoders_o = [text_encoder_o]
logger.info(f"Loading tuned SD model: {model_tuned_path}")
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_tuned_path, load_dtype)
if load_dtype:
text_encoder_o.to(load_dtype)
unet_o.to(load_dtype)
logger.info(f"Loading tuned SD model: {model_tuned}")
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_tuned)
text_encoders_t = [text_encoder_t]
if load_dtype:
text_encoder_t.to(load_dtype)
unet_t.to(load_dtype)
model_version = model_util.get_model_version_str_for_sd1_sd2(v2, v_parameterization)
else: # SDXL
else:
device_org = load_original_model_to or "cpu"
device_tuned = load_tuned_model_to or "cpu"
logger.info(f"Loading original SDXL model: {model_org_path}")
logger.info(f"Loading original SDXL model: {model_org}")
text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_org_path, device_org, load_dtype
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_org, device_org
)
text_encoders_o = [text_encoder_o1, text_encoder_o2]
if load_dtype:
text_encoder_o1.to(load_dtype)
text_encoder_o2.to(load_dtype)
unet_o.to(load_dtype)
logger.info(f"Loading tuned SDXL model: {model_tuned_path}")
logger.info(f"Loading tuned SDXL model: {model_tuned}")
text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_tuned_path, device_tuned, load_dtype
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_tuned, device_tuned
)
text_encoders_t = [text_encoder_t1, text_encoder_t2]
if load_dtype:
text_encoder_t1.to(load_dtype)
text_encoder_t2.to(load_dtype)
unet_t.to(load_dtype)
model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0
# Create temporary LoRA network to identify modules and get original weights
# Use a minimal fixed dimension for this stage as we only need module structure and original weights
temp_lora_kwargs = {"conv_dim": 1, "conv_alpha": 1.0} # Minimal conv settings for network creation
lora_network_o = lora.create_network(1.0, 1, 1.0, None, text_encoders_o, unet_o, **temp_lora_kwargs)
lora_network_t = lora.create_network(1.0, 1, 1.0, None, text_encoders_t, unet_t, **temp_lora_kwargs)
assert len(lora_network_o.text_encoder_loras) == len(lora_network_t.text_encoder_loras)
diffs = {}
text_encoder_differs = False
# Text Encoders
for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.text_encoder_loras, lora_network_t.text_encoder_loras)):
module_key_name = lora_o.lora_name # e.g. "lora_te1_text_model_encoder_layers_0_mlp_fc1"
org_weight = lora_o.org_module.weight.to(device=work_device, dtype=torch.float)
tuned_weight = lora_t.org_module.weight.to(device=work_device, dtype=torch.float)
diff = tuned_weight - org_weight
if torch.max(torch.abs(diff)) > min_diff:
text_encoder_differs = True
logger.info(f"Text encoder {i+1} module {module_key_name} differs: max diff {torch.max(torch.abs(diff))}")
diffs[module_key_name] = diff
else:
logger.info(f"Text encoder {i+1} module {module_key_name} has no significant difference.")
# Free memory
lora_o.org_module.weight = None
lora_t.org_module.weight = None
del org_weight, tuned_weight
# Create LoRA network
kwargs = {"conv_dim": conv_dim, "conv_alpha": conv_dim} if conv_dim else {}
# UNet
for lora_o, lora_t in zip(lora_network_o.unet_loras, lora_network_t.unet_loras):
module_key_name = lora_o.lora_name # e.g. "lora_unet_input_blocks_1_1_proj_in"
org_weight = lora_o.org_module.weight.to(device=work_device, dtype=torch.float)
tuned_weight = lora_t.org_module.weight.to(device=work_device, dtype=torch.float)
diff = tuned_weight - org_weight
# Define a small initial dimension for memory efficiency
init_dim = 4 # Small value to minimize memory usage
if torch.max(torch.abs(diff)) > min_diff:
logger.info(f"UNet module {module_key_name} differs: max diff {torch.max(torch.abs(diff))}")
diffs[module_key_name] = diff
else:
logger.info(f"UNet module {module_key_name} has no significant difference.")
# Create LoRA networks with minimal dimension
lora_network_o = lora.create_network(1.0, init_dim, init_dim, None, text_encoders_o, unet_o, **kwargs)
lora_network_t = lora.create_network(1.0, init_dim, init_dim, None, text_encoders_t, unet_t, **kwargs)
assert len(lora_network_o.text_encoder_loras) == len(lora_network_t.text_encoder_loras), "Model versions differ (SD1.x vs SD2.x)"
# Compute differences
diffs = {}
text_encoder_different = False
for lora_o, lora_t in zip(lora_network_o.text_encoder_loras, lora_network_t.text_encoder_loras):
lora_name = lora_o.lora_name
diff = lora_t.org_module.weight.to(work_device) - lora_o.org_module.weight.to(work_device)
lora_o.org_module.weight = None
lora_t.org_module.weight = None
del org_weight, tuned_weight
if not text_encoder_differs:
logger.warning("Text encoder weights are identical or below min_diff. Text encoder LoRA modules will not be included.")
# Remove text encoder diffs if none were significant
diffs = {k: v for k, v in diffs.items() if "unet" in k}
if not text_encoder_different and torch.max(torch.abs(diff)) > min_diff:
text_encoder_different = True
logger.info(f"Text encoder differs: max diff {torch.max(torch.abs(diff))} > {min_diff}")
diffs[lora_name] = diff
del lora_network_o, lora_network_t, text_encoders_o, text_encoders_t, unet_o, unet_t
torch.cuda.empty_cache()
for text_encoder in text_encoders_t:
del text_encoder
lora_module_weights = {} # This will store the final decomposed weights for LoRA/LoHA
if not text_encoder_different:
logger.warning("Text encoders are identical. Extracting U-Net only.")
lora_network_o.text_encoder_loras = []
diffs.clear()
logger.info(f"Extracting and resizing {algo.upper()} modules via SVD")
for lora_o, lora_t in zip(lora_network_o.unet_loras, lora_network_t.unet_loras):
lora_name = lora_o.lora_name
diff = lora_t.org_module.weight.to(work_device) - lora_o.org_module.weight.to(work_device)
lora_o.org_module.weight = None
lora_t.org_module.weight = None
diffs[lora_name] = diff
del lora_network_t, unet_t
# Filter relevant modules
lora_names = set(lora.lora_name for lora in lora_network_o.text_encoder_loras + lora_network_o.unet_loras)
# Extract and resize LoRA using SVD
logger.info("Extracting and resizing LoRA via SVD")
lora_weights = {}
with torch.no_grad():
for module_key_name, mat_diff in tqdm(diffs.items()):
if compute_device != "cpu": # Move to GPU for SVD if specified
mat_diff = mat_diff.to(compute_device)
# Determine if the layer is convolutional and its properties
is_conv = len(mat_diff.shape) == 4
kernel_size = None
if is_conv:
kernel_size = mat_diff.shape[2:4]
# For LoRA, conv_dim/alpha are specific to 3x3 convs. For others, it uses network_dim/alpha.
# For LoHA, we use conv_dim/alpha for any conv layer, and network_dim/alpha for linear.
# This logic can be refined based on how LyCORIS typically handles different conv kernel sizes for LoHA.
# Here, we'll use conv parameters if it's a conv layer, otherwise network parameters.
current_dim_target = conv_dim if is_conv else network_dim
current_alpha_target = conv_alpha if is_conv else network_alpha
for lora_name in tqdm(lora_names):
mat = diffs[lora_name]
if device:
mat = mat.to(device)
mat = mat.to(torch.float)
# For LoHA, 'alpha_target' is the rank of the first SVD (rank_initial)
# and 'dim_target' is the rank of the second SVD (rank_factor).
# For LoRA, 'dim_target' is the rank of the SVD, and 'alpha_target' is the scaling factor.
conv2d = len(mat.size()) == 4
kernel_size = mat.size()[2:4] if conv2d else None
conv2d_3x3 = conv2d and kernel_size != (1, 1)
out_dim, in_dim = mat.size()[0:2]
# Reshape convolutional weights for SVD
original_shape = mat_diff.shape
if is_conv:
if kernel_size == (1, 1):
mat_diff = mat_diff.squeeze() # Becomes (out_channels, in_channels)
else: # kernel_size (3,3) or others
mat_diff = mat_diff.flatten(start_dim=1) # Becomes (out_channels, in_channels*k_w*k_h)
out_features, in_features = mat_diff.shape[0], mat_diff.shape[1]
if conv2d:
mat = mat.flatten(start_dim=1) if conv2d_3x3 else mat.squeeze()
# Perform first SVD
try:
U, S, Vh = torch.linalg.svd(mat_diff)
except Exception as e:
logger.error(f"SVD failed for {module_key_name} with shape {mat_diff.shape}: {e}")
continue
if compute_device != "cpu": # Move results back to CPU if computation was on GPU
U, S, Vh = U.cpu(), S.cpu(), Vh.cpu()
U, S, Vh = torch.linalg.svd(mat)
# Determine rank for the first SVD (rank_initial for LoHA, rank for LoRA)
max_rank_initial = min(out_features, in_features) # Theoretical max rank
# Default rank_initial to current_alpha_target (which is network_alpha or conv_alpha)
rank_initial = current_alpha_target
# Determine rank
max_rank = dim if not conv2d_3x3 or conv_dim is None else conv_dim
if dynamic_method:
if S[0] <= MIN_SV:
determined_rank = 1
elif dynamic_method == "sv_ratio": determined_rank = index_sv_ratio(S, dynamic_param)
elif dynamic_method == "sv_cumulative": determined_rank = index_sv_cumulative(S, dynamic_param)
elif dynamic_method == "sv_fro": determined_rank = index_sv_fro(S, dynamic_param)
elif dynamic_method == "sv_knee": determined_rank = index_sv_knee_improved(S, MIN_SV)
elif dynamic_method == "sv_cumulative_knee": determined_rank = index_sv_cumulative_knee(S, MIN_SV)
elif dynamic_method == "sv_rel_decrease": determined_rank = index_sv_rel_decrease(S, dynamic_param)
else: determined_rank = rank_initial # Fallback if dynamic method unknown
rank_initial = min(determined_rank, current_alpha_target, max_rank_initial)
rank = 1
elif dynamic_method == "sv_ratio":
rank = index_sv_ratio(S, dynamic_param)
elif dynamic_method == "sv_cumulative":
rank = index_sv_cumulative(S, dynamic_param)
elif dynamic_method == "sv_fro":
rank = index_sv_fro(S, dynamic_param)
elif dynamic_method == "sv_knee":
rank = index_sv_knee_improved(S, MIN_SV) # Pass MIN_SV or a specific threshold
elif dynamic_method == "sv_cumulative_knee": # New method
rank = index_sv_cumulative_knee(S, MIN_SV) # Pass MIN_SV or a specific threshold
elif dynamic_method == "sv_rel_decrease":
rank = index_sv_rel_decrease(S, dynamic_param)
rank = min(rank, max_rank, in_dim, out_dim)
else:
rank_initial = min(current_alpha_target, max_rank_initial)
rank_initial = max(1, rank_initial) # Ensure rank is at least 1
# --- LoRA specific decomposition ---
if algo == 'lora':
lora_down = Vh[:rank_initial, :]
lora_up = U[:, :rank_initial] @ torch.diag(S[:rank_initial])
# Clamp values
dist = torch.cat([lora_up.flatten(), lora_down.flatten()])
hi_val = torch.quantile(dist, clamp_quantile) if clamp_quantile < 1.0 else dist.abs().max()
lora_up = lora_up.clamp(-hi_val, hi_val)
lora_down = lora_down.clamp(-hi_val, hi_val)
# Reshape for conv layers if necessary
if is_conv:
if kernel_size == (1,1):
# These are already (out_c, rank) and (rank, in_c)
# Some LoRA impls might expect them to be 4D (out_c, rank, 1, 1) and (rank, in_c, 1, 1)
lora_up = lora_up.reshape(out_features, rank_initial, 1, 1)
lora_down = lora_down.reshape(rank_initial, in_features, 1, 1)
else: # e.g. 3x3 conv
# lora_down was (rank, in_c*k_w*k_h), needs to be (rank, in_c, k_w, k_h)
# lora_up was (out_c, rank)
lora_up = lora_up.reshape(out_features, rank_initial, 1, 1) # often up is kept as 1x1 conv like
lora_down = lora_down.reshape(rank_initial, original_shape[1], *kernel_size)
lora_module_weights[f"{module_key_name}.lora_down.weight"] = lora_down.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.lora_up.weight"] = lora_up.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.alpha"] = torch.tensor(float(current_alpha_target), dtype=save_dtype) # Use actual alpha target from params
# --- LoHA specific decomposition ---
elif algo == 'loha':
lora_down_equivalent = Vh[:rank_initial, :]
lora_up_equivalent = U[:, :rank_initial] @ torch.diag(S[:rank_initial])
# current_dim_target is the "factor" for LoHA's second SVD
rank_factor = min(current_dim_target, rank_initial) # Factor cannot exceed rank_initial
if is_conv and kernel_size != (1,1): # For conv3x3, factor also limited by in/out features
rank_factor = min(rank_factor, original_shape[1], original_shape[0]) #original_shape[1] is in_channels
else: # Linear or Conv1x1
rank_factor = min(rank_factor, in_features, out_features)
rank_factor = max(1, rank_factor)
# Decompose Lora_Down_Equivalent (shape: rank_initial x in_features_eff)
# Target: hada_w1_b (rank_initial x rank_factor) @ hada_w1_a (rank_factor x in_features_eff)
if compute_device != "cpu": lora_down_equivalent = lora_down_equivalent.to(compute_device)
Ud, Sd, Vhd = torch.linalg.svd(lora_down_equivalent)
if compute_device != "cpu": Ud, Sd, Vhd = Ud.cpu(), Sd.cpu(), Vhd.cpu()
hada_w1_a = Vhd[:rank_factor, :]
hada_w1_b = Ud[:, :rank_factor] @ torch.diag(Sd[:rank_factor])
# Decompose Lora_Up_Equivalent (shape: out_features_eff x rank_initial)
# Target: hada_w2_b (out_features_eff x rank_factor) @ hada_w2_a (rank_factor x rank_initial)
if compute_device != "cpu": lora_up_equivalent = lora_up_equivalent.to(compute_device)
Uu, Su, Vhu = torch.linalg.svd(lora_up_equivalent)
if compute_device != "cpu": Uu, Su, Vhu = Uu.cpu(), Su.cpu(), Vhu.cpu()
hada_w2_a = Vhu[:rank_factor, :]
hada_w2_b = Uu[:, :rank_factor] @ torch.diag(Su[:rank_factor])
# Clamp LoHA components
dist_w1a = hada_w1_a.flatten()
dist_w1b = hada_w1_b.flatten()
dist_w2a = hada_w2_a.flatten()
dist_w2b = hada_w2_b.flatten()
if clamp_quantile < 1.0:
hi_val_w1a = torch.quantile(dist_w1a.abs(), clamp_quantile)
hi_val_w1b = torch.quantile(dist_w1b.abs(), clamp_quantile)
hi_val_w2a = torch.quantile(dist_w2a.abs(), clamp_quantile)
hi_val_w2b = torch.quantile(dist_w2b.abs(), clamp_quantile)
else: # Use max abs value if quantile is 1.0 or more
hi_val_w1a = dist_w1a.abs().max()
hi_val_w1b = dist_w1b.abs().max()
hi_val_w2a = dist_w2a.abs().max()
hi_val_w2b = dist_w2b.abs().max()
hada_w1_a = hada_w1_a.clamp(-hi_val_w1a, hi_val_w1a)
hada_w1_b = hada_w1_b.clamp(-hi_val_w1b, hi_val_w1b)
hada_w2_a = hada_w2_a.clamp(-hi_val_w2a, hi_val_w2a)
hada_w2_b = hada_w2_b.clamp(-hi_val_w2b, hi_val_w2b)
# LoHA weights are typically stored as 2D matrices.
# LyCORIS library handles reshaping or uses 1x1 convs internally.
lora_module_weights[f"{module_key_name}.hada_w1_a"] = hada_w1_a.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.hada_w1_b"] = hada_w1_b.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.hada_w2_a"] = hada_w2_a.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.hada_w2_b"] = hada_w2_b.to(work_device, dtype=save_dtype).contiguous()
lora_module_weights[f"{module_key_name}.alpha"] = torch.tensor(float(current_alpha_target), dtype=save_dtype) # This is rank_initial
rank = min(max_rank, in_dim, out_dim)
rank = max(1, rank) # Ensure rank is at least 1
# Truncate SVD components and distribute sqrt(S)
S_k = S[:rank]
U_k = U[:, :rank]
Vh_k = Vh[:rank, :]
# Ensure S_k values are non-negative before sqrt to avoid NaN from tiny negative SVD artifacts
S_k_non_negative = torch.clamp(S_k, min=0.0) # Use 0.0 for float tensor
s_sqrt = torch.sqrt(S_k_non_negative)
# Distribute s_sqrt: U_final = U_k * diag(s_sqrt), Vh_final = diag(s_sqrt) * Vh_k
# Using efficient broadcasting for multiplication:
U_final = U_k * s_sqrt.unsqueeze(0) # (out_dim, rank) * (1, rank)
Vh_final = Vh_k * s_sqrt.unsqueeze(1) # (rank, in_dim_effective) * (rank, 1)
# Clamp values (applied to U_final, Vh_final)
# The distribution of values in U_final and Vh_final might be different
# than the original U and Vh, so the effect of clamping might change.
dist = torch.cat([U_final.flatten(), Vh_final.flatten()])
hi_val = torch.quantile(dist, clamp_quantile)
U_clamped = U_final.clamp(-hi_val, hi_val)
Vh_clamped = Vh_final.clamp(-hi_val, hi_val)
if conv2d:
# U_clamped is (out_dim, rank)
U_clamped = U_clamped.reshape(out_dim, rank, 1, 1)
# Vh_clamped is (rank, in_dim * possibly_kernel_dims)
# It needs to be reshaped back to (rank, in_dim, kernel_h, kernel_w)
if conv2d_3x3 : # Original mat was (out_dim, in_dim * k_h * k_w)
Vh_clamped = Vh_clamped.reshape(rank, in_dim, *kernel_size)
else: # Original mat was (out_dim, in_dim) for 1x1 conv, kernel_size is (1,1)
Vh_clamped = Vh_clamped.reshape(rank, in_dim, *kernel_size) # kernel_size is (1,1) here
U_clamped = U_clamped.to(work_device, dtype=save_dtype).contiguous()
Vh_clamped = Vh_clamped.to(work_device, dtype=save_dtype).contiguous()
lora_weights[lora_name] = (U_clamped, Vh_clamped)
# Verbose output (S values are pre-modification for accurate reporting of original SVD properties)
if verbose:
s_sum = float(torch.sum(S))
s_rank_initial = float(torch.sum(S[:rank_initial]))
fro_initial = float(torch.sqrt(torch.sum(S.pow(2))))
fro_rank_initial = float(torch.sqrt(torch.sum(S[:rank_initial].pow(2))))
# This verbose output is for the first SVD. Adding verbose for second SVD would be more complex.
s_sum_total = float(torch.sum(S))
s_sum_rank = float(torch.sum(S[:rank])) # Sum of the singular values actually used for reconstruction
fro_orig_total = float(torch.sqrt(torch.sum(S.pow(2))))
fro_reconstructed_rank = float(torch.sqrt(torch.sum(S[:rank].pow(2)))) # Frobenius norm of the matrix part represented by chosen rank
# Ratio of the largest retained singular value to the smallest retained singular value
# S is sorted, S[0] is max. S[rank-1] is the smallest singular value included if rank > 0.
ratio_sv = S[0] / S[rank - 1] if rank > 0 and S[rank - 1].abs() > MIN_SV else float('inf') # Avoid division by zero or tiny number
# Ensure denominators are not zero for percentages
sum_s_retained_percentage = (s_sum_rank / s_sum_total) if s_sum_total > MIN_SV else 1.0
fro_retained_percentage = (fro_reconstructed_rank / fro_orig_total) if fro_orig_total > MIN_SV else 1.0
logger.info(
f"{module_key_name[:75]:75} | Algo: {algo.upper()}, Rank/Alpha (initial): {rank_initial}, Dim/Factor (final): {rank_factor if algo=='loha' else rank_initial} "
f"| Sum(S) Retained (1st SVD): {s_rank_initial/s_sum if s_sum > 0 else 0:.1%}, Fro Retained (1st SVD): {fro_rank_initial/fro_initial if fro_initial > 0 else 0:.1%}"
f"{lora_name:75} | rank: {rank}, "
f"sum(S) retained: {sum_s_retained_percentage:.2%}, "
f"Frobenius norm retained: {fro_retained_percentage:.2%}, "
f"max_retained_sv/min_retained_sv ratio: {ratio_sv:.2f}"
)
del U, S, Vh, mat_diff
if algo == 'loha':
del lora_down_equivalent, lora_up_equivalent, Ud, Sd, Vhd, Uu, Su, Vhu
del hada_w1_a, hada_w1_b, hada_w2_a, hada_w2_b
else: # lora
del lora_down, lora_up
if compute_device != "cpu":
torch.cuda.empty_cache()
# Create state dict
lora_sd = {}
for lora_name, (up_weight, down_weight) in lora_weights.items():
lora_sd[lora_name + ".lora_up.weight"] = up_weight
lora_sd[lora_name + ".lora_down.weight"] = down_weight
lora_sd[lora_name + ".alpha"] = torch.tensor(down_weight.size()[0], dtype=save_dtype) # alpha is rank
# Load and save LoRA
lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoders_o, unet_o, weights_sd=lora_sd)
lora_network_save.apply_to(text_encoders_o, unet_o) # This applies weights, not strictly necessary if just saving sd
info = lora_network_save.load_state_dict(lora_sd) # This populates the network object with the weights from lora_sd
logger.info(f"Loaded extracted and resized LoRA weights into network object: {info}")
if not lora_module_weights:
logger.error("No LoRA/LoHA modules were generated. This might be due to models being too similar or min_diff being too high.")
return
os.makedirs(os.path.dirname(save_to), exist_ok=True)
# Metadata
net_kwargs = {"conv_dim": str(conv_dim), "conv_alpha": str(float(conv_dim))} if conv_dim else {}
# Determine network_dim and network_alpha for metadata based on dynamic method
if dynamic_method:
network_dim_meta = "Dynamic"
network_alpha_meta = "Dynamic" # Alpha is rank, which is dynamic
else:
network_dim_meta = str(dim)
network_alpha_meta = str(float(dim)) # Alpha is rank, which is dim
metadata = {
"ss_v2": str(v2) if v2 is not None else "false", # More explicit "false"
"ss_v2": str(v2),
"ss_base_model_version": model_version,
"ss_sdxl_model_version": "1.0" if sdxl else "null", # LyCORIS convention
"ss_network_module": "networks.lora" if algo == "lora" else "lycoris.kohya", # For LoHA
# For LoRA, dim and alpha are usually the same as what's passed.
# For LoHA, network_dim is the 'factor' (our network_dim/conv_dim),
# and network_alpha is the 'rank_initial' (our network_alpha/conv_alpha).
"ss_network_dim": str(network_dim) if not dynamic_method or algo == "loha" else "Dynamic", # For LoHA, this is 'factor'
"ss_network_alpha": str(network_alpha) if not dynamic_method or algo == "loha" else "Dynamic", # For LoHA, this is 'rank_initial'
"ss_network_module": "networks.lora",
"ss_network_dim": network_dim_meta,
"ss_network_alpha": network_alpha_meta, # Alpha is typically the rank
"ss_network_args": json.dumps(net_kwargs),
"ss_lowram": "False", # Assuming not specifically lowram mode
"ss_num_train_images": "N/A", # Not applicable for extraction
# Add other relevant metadata as per sai_model_spec or conventions
}
net_kwargs = {}
if algo == "lora":
if conv_dim is not None: # Only add if conv_dim was actually specified for LoRA
net_kwargs["conv_dim"] = str(conv_dim)
net_kwargs["conv_alpha"] = str(float(conv_alpha if conv_alpha is not None else conv_dim))
elif algo == "loha":
net_kwargs["algo"] = "loha"
# For LoHA, conv_dim and conv_alpha are distinct concepts (factor and rank_initial for conv layers)
net_kwargs["conv_dim"] = str(conv_dim)
net_kwargs["conv_alpha"] = str(float(conv_alpha))
# LyCORIS sometimes uses dropout, but we are not implementing it here.
# net_kwargs["dropout"] = "0" # Example if we had dropout
metadata["ss_network_args"] = json.dumps(net_kwargs) if net_kwargs else "null" # LyCORIS uses "null" for empty
if not no_metadata:
title = os.path.splitext(os.path.basename(save_to))[0]
# sai_model_spec might need adjustment if it expects specific lora types not lycoris
try:
sai_metadata = sai_model_spec.build_metadata(
None, v2, v_parameterization, sdxl, True,
is_lycoris=(algo != "lora"), # Pass if it's LyCORIS
is_lora=(algo == "lora"), # Pass if it's LoRA
creation_time=time.time(), title=title
)
metadata.update(sai_metadata)
except TypeError as e:
logger.warning(f"Could not generate full SAI metadata, possibly due to outdated sai_model_spec.py or new flags: {e}")
logger.warning("Falling back to basic metadata for SAI fields.")
metadata.update({ # Basic fallback
"sai_model_name": title,
"sai_base_model": model_version,
"sai_is_sdxl": str(sdxl).lower(),
})
# Build sai_metadata, ensuring it includes necessary fields like 'ss_sd_model_hash' if possible
# For extraction, some training-specific metadata might not be relevant or available.
sai_metadata = sai_model_spec.build_metadata(
None, # training_info (usually from train_util or fine_tune) - can be None for extraction
v2,
v_parameterization,
sdxl,
True, # is_sd2
False, # is_v_pred_like
time.time(),
title=title,
# model_hash=None, # Original model hash if available
# tuned_model_hash=None # Tuned model hash if available
)
# Filter out None values from sai_metadata if any, or handle them in build_metadata
sai_metadata_cleaned = {k: v for k, v in sai_metadata.items() if v is not None}
metadata.update(sai_metadata_cleaned)
save_to_file(save_to, lora_module_weights, save_dtype, metadata)
logger.info(f"{algo.upper()} saved to: {save_to}")
# Use the state_dict 'lora_sd' for saving, not the network object 'lora_network_save'
save_to_file(save_to, lora_sd, lora_sd, save_dtype, metadata) # Pass lora_sd as the model/state_dict to save
logger.info(f"LoRA saved to: {save_to}")
def setup_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--v2", action="store_true", help="Load Stable Diffusion v2.x model")
parser.add_argument("--v_parameterization", action="store_true", help="Set v-parameterization metadata (defaults to v2 if applicable)")
parser.add_argument("--v_parameterization", action="store_true", help="Set v-parameterization metadata (defaults to v2)")
parser.add_argument("--sdxl", action="store_true", help="Load Stable Diffusion SDXL base model")
parser.add_argument("--model_org_path", type=str, required=True, help="Path to the original Stable Diffusion model (ckpt/safetensors)")
parser.add_argument("--model_tuned_path", type=str, required=True, help="Path to the tuned Stable Diffusion model (ckpt/safetensors)")
parser.add_argument("--save_to", type=str, required=True, help="Output file name for the LoRA/LoHA (ckpt/safetensors)")
parser.add_argument("--algo", type=str, default="lora", choices=["lora", "loha"], help="Algorithm to use: lora or loha")
parser.add_argument("--network_dim", type=int, default=4, help="Network dimension. For LoRA: rank. For LoHA: 'factor' or 'hada_dim'.")
parser.add_argument("--network_alpha", type=int, default=None, help="Network alpha. For LoRA: alpha (often same as rank). For LoHA: 'rank_initial' or 'hada_alpha'. Defaults to network_dim if not set.")
parser.add_argument("--conv_dim", type=int, default=None, help="Conv dimension for conv layers. For LoRA: rank. For LoHA: 'factor'. Defaults to network_dim if not set.")
parser.add_argument("--conv_alpha", type=int, default=None, help="Conv alpha for conv layers. For LoRA: alpha. For LoHA: 'rank_initial'. Defaults to conv_dim if not set.")
parser.add_argument("--load_precision", type=str, choices=[None, "float", "fp16", "bf16"], default=None, help="Precision for loading models (None means default float32)")
parser.add_argument("--save_precision", type=str, choices=[None, "float", "fp16", "bf16"], default=None, help="Precision for saving LoRA/LoHA (None means float32)")
parser.add_argument("--device", type=str, default=None, help="Device for SVD computation (e.g., 'cuda', 'cpu'). If None, defaults to 'cuda' if available, else 'cpu'. SVD results are moved to CPU for storage.")
parser.add_argument("--clamp_quantile", type=float, default=0.99, help="Quantile for clamping weights (0.0 to 1.0). 1.0 means clamp to max abs value.")
parser.add_argument("--min_diff", type=float, default=1e-6, help="Minimum weight difference threshold for a module to be considered for extraction.") # Lowered default
parser.add_argument("--no_metadata", action="store_true", help="Do not save detailed metadata (minimal ss_ metadata will still be saved).")
parser.add_argument("--load_original_model_to", type=str, default=None, help="Device to load original model to (SDXL only, e.g., 'cpu', 'cuda:0')")
parser.add_argument("--load_tuned_model_to", type=str, default=None, help="Device to load tuned model to (SDXL only, e.g., 'cpu', 'cuda:1')")
parser.add_argument("--dynamic_method", type=str, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative", "sv_knee", "sv_rel_decrease", "sv_cumulative_knee"], default=None, help="Dynamic method to determine rank/alpha (for the first SVD in LoHA). Overrides fixed network_alpha/conv_alpha if set.")
parser.add_argument("--dynamic_param", type=float, default=0.9, help="Parameter for the chosen dynamic_method (e.g., target ratio/cumulative sum/Frobenius norm percentage).")
parser.add_argument("--verbose", action="store_true", help="Show detailed SVD info for each module.")
parser.add_argument("--load_precision", choices=[None, "float", "fp16", "bf16"], help="Precision for loading models")
parser.add_argument("--save_precision", choices=[None, "float", "fp16", "bf16"], default=None, help="Precision for saving LoRA")
parser.add_argument("--model_org", required=True, help="Original Stable Diffusion model (ckpt/safetensors)")
parser.add_argument("--model_tuned", required=True, help="Tuned Stable Diffusion model (ckpt/safetensors)")
parser.add_argument("--save_to", required=True, help="Output file name (ckpt/safetensors)")
parser.add_argument("--dim", type=int, default=4, help="Max dimension (rank) of LoRA for linear layers")
parser.add_argument("--conv_dim", type=int, help="Max dimension (rank) of LoRA for Conv2d-3x3")
parser.add_argument("--device", default="cuda", help="Device for computation (e.g., cuda)")
parser.add_argument("--clamp_quantile", type=float, default=0.99, help="Quantile for clamping weights")
parser.add_argument("--min_diff", type=float, default=0.01, help="Minimum weight difference to extract")
parser.add_argument("--no_metadata", action="store_true", help="Omit detailed metadata")
parser.add_argument("--load_original_model_to", help="Device for original model (SDXL only)")
parser.add_argument("--load_tuned_model_to", help="Device for tuned model (SDXL only)")
parser.add_argument("--dynamic_param", type=float, help="Parameter for dynamic rank reduction")
parser.add_argument("--verbose", action="store_true", help="Show detailed rank reduction info")
parser.add_argument(
"--dynamic_method",
choices=[None, "sv_ratio", "sv_fro", "sv_cumulative", "sv_knee", "sv_rel_decrease", "sv_cumulative_knee"], # Added "sv_cumulative_knee"
help="Dynamic rank reduction method"
)
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
if args.dynamic_method and (args.dynamic_param is None):
# Default dynamic_param for methods if not specified, or raise error
if args.dynamic_method in ["sv_cumulative", "sv_fro"]:
args.dynamic_param = 0.99 # Example: 99% variance/energy
logger.info(f"Dynamic method {args.dynamic_method} chosen, dynamic_param defaulted to {args.dynamic_param}")
elif args.dynamic_method in ["sv_ratio"]:
args.dynamic_param = 1000 # Example: ratio of 1000
logger.info(f"Dynamic method {args.dynamic_method} chosen, dynamic_param defaulted to {args.dynamic_param}")
elif args.dynamic_method in ["sv_rel_decrease"]:
args.dynamic_param = 0.05 # Example: 5% relative decrease
logger.info(f"Dynamic method {args.dynamic_method} chosen, dynamic_param defaulted to {args.dynamic_param}")
# sv_knee and sv_cumulative_knee do not require dynamic_param in this implementation.
elif args.dynamic_method not in ["sv_knee", "sv_cumulative_knee"]:
parser.error("--dynamic_method requires --dynamic_param for most methods.")
# Default device selection
if args.device is None:
args.device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {args.device} for SVD computation.")
# Rename args for clarity before passing to function
func_args = vars(args).copy()
func_args["model_org_path"] = func_args.pop("model_org_path")
func_args["model_tuned_path"] = func_args.pop("model_tuned_path")
methods_requiring_param = ["sv_ratio", "sv_fro", "sv_cumulative", "sv_rel_decrease"]
if args.dynamic_method in methods_requiring_param and args.dynamic_param is None:
raise ValueError(f"Dynamic method '{args.dynamic_method}' requires --dynamic_param to be set.")
svd_decomposition(**func_args)
# Add a check for rank > 0 if not dynamic, or ensure dynamic methods return rank >= 1
if not args.dynamic_method and args.dim <= 0:
raise ValueError(f"--dim (rank) must be > 0. Got {args.dim}")
if args.conv_dim is not None and args.conv_dim <=0:
raise ValueError(f"--conv_dim (rank) must be > 0 if specified. Got {args.conv_dim}")
svd(**vars(args))