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#!/usr/bin/env python3
"""
CompI Phase 1.E: LoRA Fine-tuning for Personal Style
This script implements LoRA (Low-Rank Adaptation) fine-tuning for Stable Diffusion
to learn your personal artistic style.
Usage:
python src/generators/compi_phase1e_lora_training.py --dataset-dir datasets/my_style
python src/generators/compi_phase1e_lora_training.py --help
"""
import os
import argparse
import json
import math
from pathlib import Path
from typing import Dict, List, Optional
import logging
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import numpy as np
from tqdm import tqdm
# Diffusers and transformers
from diffusers import (
StableDiffusionPipeline,
UNet2DConditionModel,
DDPMScheduler,
AutoencoderKL
)
from transformers import CLIPTextModel, CLIPTokenizer
from peft import LoraConfig, get_peft_model, TaskType
# -------- 1. CONFIGURATION --------
DEFAULT_MODEL = "runwayml/stable-diffusion-v1-5"
DEFAULT_RESOLUTION = 512
DEFAULT_BATCH_SIZE = 1
DEFAULT_LEARNING_RATE = 1e-4
DEFAULT_EPOCHS = 100
DEFAULT_LORA_RANK = 4
DEFAULT_LORA_ALPHA = 32
# -------- 2. DATASET CLASS --------
class StyleDataset(Dataset):
"""Dataset class for LoRA fine-tuning."""
def __init__(self, dataset_dir: str, split: str = "train", resolution: int = 512):
self.dataset_dir = Path(dataset_dir)
self.split = split
self.resolution = resolution
# Load images and captions
self.images_dir = self.dataset_dir / split
self.captions_file = self.dataset_dir / f"{split}_captions.txt"
if not self.images_dir.exists():
raise FileNotFoundError(f"Images directory not found: {self.images_dir}")
if not self.captions_file.exists():
raise FileNotFoundError(f"Captions file not found: {self.captions_file}")
# Load captions
self.image_captions = {}
with open(self.captions_file, 'r') as f:
for line in f:
if ':' in line:
filename, caption = line.strip().split(':', 1)
self.image_captions[filename.strip()] = caption.strip()
# Get list of images
self.image_files = [f for f in os.listdir(self.images_dir)
if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
# Filter to only images with captions
self.image_files = [f for f in self.image_files if f in self.image_captions]
print(f"Loaded {len(self.image_files)} images for {split} split")
def __len__(self):
return len(self.image_files)
def __getitem__(self, idx):
filename = self.image_files[idx]
image_path = self.images_dir / filename
caption = self.image_captions[filename]
# Load and preprocess image
image = Image.open(image_path).convert('RGB')
image = image.resize((self.resolution, self.resolution), Image.Resampling.LANCZOS)
# Convert to tensor and normalize to [-1, 1]
image = np.array(image).astype(np.float32) / 255.0
image = (image - 0.5) / 0.5
image = torch.from_numpy(image).permute(2, 0, 1)
return {
'pixel_values': image,
'caption': caption,
'filename': filename
}
# -------- 3. TRAINING FUNCTIONS --------
def setup_args():
"""Setup command line arguments."""
parser = argparse.ArgumentParser(
description="CompI Phase 1.E: LoRA Fine-tuning for Personal Style",
formatter_class=argparse.RawDescriptionHelpFormatter
)
parser.add_argument("--dataset-dir", required=True,
help="Directory containing prepared dataset")
parser.add_argument("--output-dir",
help="Output directory for LoRA weights (default: lora_models/{style_name})")
parser.add_argument("--model-name", default=DEFAULT_MODEL,
help=f"Base Stable Diffusion model (default: {DEFAULT_MODEL})")
parser.add_argument("--resolution", type=int, default=DEFAULT_RESOLUTION,
help=f"Training resolution (default: {DEFAULT_RESOLUTION})")
parser.add_argument("--batch-size", type=int, default=DEFAULT_BATCH_SIZE,
help=f"Training batch size (default: {DEFAULT_BATCH_SIZE})")
parser.add_argument("--learning-rate", type=float, default=DEFAULT_LEARNING_RATE,
help=f"Learning rate (default: {DEFAULT_LEARNING_RATE})")
parser.add_argument("--epochs", type=int, default=DEFAULT_EPOCHS,
help=f"Number of training epochs (default: {DEFAULT_EPOCHS})")
parser.add_argument("--lora-rank", type=int, default=DEFAULT_LORA_RANK,
help=f"LoRA rank (default: {DEFAULT_LORA_RANK})")
parser.add_argument("--lora-alpha", type=int, default=DEFAULT_LORA_ALPHA,
help=f"LoRA alpha (default: {DEFAULT_LORA_ALPHA})")
parser.add_argument("--save-steps", type=int, default=100,
help="Save checkpoint every N steps")
parser.add_argument("--validation-steps", type=int, default=50,
help="Run validation every N steps")
parser.add_argument("--mixed-precision", action="store_true",
help="Use mixed precision training")
parser.add_argument("--gradient-checkpointing", action="store_true",
help="Use gradient checkpointing to save memory")
return parser.parse_args()
def load_models(model_name: str, device: str):
"""Load Stable Diffusion components."""
print(f"Loading models from {model_name}...")
# Load tokenizer and text encoder
tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder")
# Load VAE
vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae")
# Load UNet
unet = UNet2DConditionModel.from_pretrained(model_name, subfolder="unet")
# Load noise scheduler
noise_scheduler = DDPMScheduler.from_pretrained(model_name, subfolder="scheduler")
# Move to device
text_encoder.to(device)
vae.to(device)
unet.to(device)
# Set to eval mode (we only train LoRA adapters)
text_encoder.eval()
vae.eval()
unet.train() # UNet needs to be in train mode for LoRA
return tokenizer, text_encoder, vae, unet, noise_scheduler
def setup_lora(unet: UNet2DConditionModel, lora_rank: int, lora_alpha: int):
"""Setup LoRA adapters for UNet."""
print(f"Setting up LoRA with rank={lora_rank}, alpha={lora_alpha}")
# Define LoRA config
lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
target_modules=[
"to_k", "to_q", "to_v", "to_out.0",
"proj_in", "proj_out",
"ff.net.0.proj", "ff.net.2"
],
lora_dropout=0.1,
)
# Apply LoRA to UNet
unet = get_peft_model(unet, lora_config)
# Print trainable parameters
trainable_params = sum(p.numel() for p in unet.parameters() if p.requires_grad)
total_params = sum(p.numel() for p in unet.parameters())
print(f"Trainable parameters: {trainable_params:,} ({100 * trainable_params / total_params:.2f}%)")
return unet
def encode_text(tokenizer, text_encoder, captions: List[str], device: str):
"""Encode text captions."""
inputs = tokenizer(
captions,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt"
)
with torch.no_grad():
text_embeddings = text_encoder(inputs.input_ids.to(device))[0]
return text_embeddings
def training_step(batch, unet, vae, text_encoder, tokenizer, noise_scheduler, device):
"""Single training step."""
pixel_values = batch['pixel_values'].to(device)
captions = batch['caption']
# Encode images to latent space
with torch.no_grad():
latents = vae.encode(pixel_values).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise
noise = torch.randn_like(latents)
batch_size = latents.shape[0]
# Sample random timesteps
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps,
(batch_size,), device=device
).long()
# Add noise to latents
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Encode text
text_embeddings = encode_text(tokenizer, text_encoder, captions, device)
# Predict noise
noise_pred = unet(noisy_latents, timesteps, text_embeddings).sample
# Calculate loss
loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
return loss
def validate_model(val_dataloader, unet, vae, text_encoder, tokenizer, noise_scheduler, device):
"""Validation step."""
unet.eval()
total_loss = 0
num_batches = 0
with torch.no_grad():
for batch in val_dataloader:
loss = training_step(batch, unet, vae, text_encoder, tokenizer, noise_scheduler, device)
total_loss += loss.item()
num_batches += 1
unet.train()
return total_loss / num_batches if num_batches > 0 else 0
def save_lora_weights(unet, output_dir: Path, step: int):
"""Save LoRA weights."""
checkpoint_dir = output_dir / f"checkpoint-{step}"
checkpoint_dir.mkdir(parents=True, exist_ok=True)
# Save LoRA weights
unet.save_pretrained(checkpoint_dir)
print(f"πΎ Saved checkpoint to: {checkpoint_dir}")
return checkpoint_dir
# -------- 4. MAIN TRAINING FUNCTION --------
def train_lora(args):
"""Main training function."""
print(f"π¨ CompI Phase 1.E: Starting LoRA Training")
print("=" * 50)
# Setup device
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"π₯οΈ Using device: {device}")
# Load dataset info
dataset_dir = Path(args.dataset_dir)
info_file = dataset_dir / "dataset_info.json"
if info_file.exists():
with open(info_file) as f:
dataset_info = json.load(f)
style_name = dataset_info.get('style_name', 'custom_style')
print(f"π― Training style: {style_name}")
else:
style_name = dataset_dir.name
print(f"β οΈ No dataset info found, using directory name: {style_name}")
# Setup output directory
if args.output_dir:
output_dir = Path(args.output_dir)
else:
output_dir = Path("lora_models") / style_name
output_dir.mkdir(parents=True, exist_ok=True)
print(f"π Output directory: {output_dir}")
# Load datasets
print(f"π Loading datasets...")
train_dataset = StyleDataset(args.dataset_dir, "train", args.resolution)
try:
val_dataset = StyleDataset(args.dataset_dir, "validation", args.resolution)
has_validation = True
except FileNotFoundError:
print("β οΈ No validation set found, using train set for validation")
val_dataset = train_dataset
has_validation = False
# Create data loaders
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True
)
val_dataloader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True
)
# Load models
tokenizer, text_encoder, vae, unet, noise_scheduler = load_models(args.model_name, device)
# Setup LoRA
unet = setup_lora(unet, args.lora_rank, args.lora_alpha)
# Setup optimizer
optimizer = torch.optim.AdamW(
unet.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
weight_decay=0.01,
eps=1e-08
)
# Calculate total steps
total_steps = len(train_dataloader) * args.epochs
print(f"π Total training steps: {total_steps}")
# Training loop
print(f"\nπ Starting training...")
global_step = 0
best_val_loss = float('inf')
for epoch in range(args.epochs):
print(f"\nπ
Epoch {epoch + 1}/{args.epochs}")
epoch_loss = 0
progress_bar = tqdm(train_dataloader, desc=f"Training")
for batch in progress_bar:
# Training step
loss = training_step(batch, unet, vae, text_encoder, tokenizer, noise_scheduler, device)
# Backward pass
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Update metrics
epoch_loss += loss.item()
global_step += 1
# Update progress bar
progress_bar.set_postfix({
'loss': f"{loss.item():.4f}",
'avg_loss': f"{epoch_loss / (progress_bar.n + 1):.4f}"
})
# Validation
if global_step % args.validation_steps == 0:
val_loss = validate_model(val_dataloader, unet, vae, text_encoder, tokenizer, noise_scheduler, device)
print(f"\nπ Step {global_step}: Train Loss = {loss.item():.4f}, Val Loss = {val_loss:.4f}")
# Save best model
if val_loss < best_val_loss:
best_val_loss = val_loss
save_lora_weights(unet, output_dir, global_step)
# Save checkpoint
if global_step % args.save_steps == 0:
save_lora_weights(unet, output_dir, global_step)
# End of epoch
avg_epoch_loss = epoch_loss / len(train_dataloader)
print(f"π Epoch {epoch + 1} complete. Average loss: {avg_epoch_loss:.4f}")
# Save final model
final_checkpoint = save_lora_weights(unet, output_dir, global_step)
# Save training info
training_info = {
'style_name': style_name,
'model_name': args.model_name,
'total_steps': global_step,
'epochs': args.epochs,
'learning_rate': args.learning_rate,
'lora_rank': args.lora_rank,
'lora_alpha': args.lora_alpha,
'final_checkpoint': str(final_checkpoint),
'best_val_loss': best_val_loss
}
with open(output_dir / "training_info.json", 'w') as f:
json.dump(training_info, f, indent=2)
print(f"\nπ Training complete!")
print(f"π LoRA weights saved to: {output_dir}")
print(f"π‘ Next steps:")
print(f" 1. Test your style: python src/generators/compi_phase1e_style_generation.py --lora-path {final_checkpoint}")
print(f" 2. Integrate with UI: Use the style in your Streamlit interface")
def main():
"""Main function."""
args = setup_args()
try:
train_lora(args)
except Exception as e:
print(f"β Training failed: {e}")
import traceback
traceback.print_exc()
return 1
return 0
if __name__ == "__main__":
exit(main())
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