added training script for instruct-pix2pix-sdxl

examples/consistency_distillation/README_instruct_pix2pix_sdxl.md

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+# LCM Distillation for InstructPix2Pix SDXL
+
+This repository contains a training script for distilling Latent Consistency Models (LCM) for InstructPix2Pix with Stable Diffusion XL (SDXL). The script enables fast, few-step image editing by distilling knowledge from a teacher model into a student model.
+
+## Overview
+
+This implementation performs **LCM distillation** on InstructPix2Pix SDXL models, which allows for:
+- **Fast image editing** with significantly fewer sampling steps (1-4 steps vs 50+ steps)
+- **Instruction-based image manipulation** using text prompts
+- **High-quality outputs** that maintain the teacher model's capabilities
+
+The training uses a teacher-student distillation approach where:
+- **Teacher**: Pre-trained InstructPix2Pix SDXL model (8-channel input U-Net)
+- **Student**: Lightweight model with time conditioning that learns to match teacher outputs
+- **Target**: EMA (Exponential Moving Average) version of student for stable training
+
+## Requirements
+```bash
+pip install torch torchvision
+pip install diffusers transformers accelerate
+pip install datasets pillow
+pip install tensorboard  # or wandb for logging
+pip install xformers  # optional, for memory efficiency
+pip install bitsandbytes  # optional, for 8-bit Adam optimizer
+```
+## Dataset Format
+
+The script expects datasets with three components per sample:
+1. **Original Image**: The input image to be edited
+2. **Edit Prompt**: Text instruction describing the desired edit
+3. **Edited Image**: The target output after applying the edit
+
+### Supported Formats
+
+**Option 1: HuggingFace Dataset**
+```bash
+python train_lcm_distil_instruct_pix2pix_sdxl.py \
+  --dataset_name="your/dataset-name" \
+  --dataset_config_name="default"
+```
+**Option 2: Local ImageFolder**
+```bash
+python train_lcm_distil_instruct_pix2pix_sdxl.py \
+  --train_data_dir="./data/train"
+```
+## Key Arguments
+
+### Model Configuration
+
+- `--pretrained_teacher_model`: Path/ID of the teacher InstructPix2Pix SDXL model
+- `--pretrained_vae_model_name_or_path`: Optional separate VAE model path
+- `--vae_precision`: VAE precision (`fp16`, `fp32`, `bf16`)
+- `--unet_time_cond_proj_dim`: Time conditioning projection dimension (default: 256)
+
+### Dataset Arguments
+
+- `--dataset_name`: HuggingFace dataset name
+- `--train_data_dir`: Local training data directory
+- `--original_image_column`: Column name for original images
+- `--edit_prompt_column`: Column name for edit prompts
+- `--edited_image_column`: Column name for edited images
+- `--max_train_samples`: Limit number of training samples
+
+### Training Configuration
+
+- `--resolution`: Image resolution (default: 512)
+- `--train_batch_size`: Batch size per device
+- `--num_train_epochs`: Number of training epochs
+- `--max_train_steps`: Maximum training steps
+- `--gradient_accumulation_steps`: Gradient accumulation steps
+- `--learning_rate`: Learning rate (default: 1e-4)
+- `--lr_scheduler`: Learning rate scheduler type
+- `--lr_warmup_steps`: Number of warmup steps
+
+### LCM-Specific Arguments
+
+- `--w_min`: Minimum guidance scale for sampling (default: 3.0)
+- `--w_max`: Maximum guidance scale for sampling (default: 15.0)
+- `--num_ddim_timesteps`: Number of DDIM timesteps (default: 50)
+- `--loss_type`: Loss function type (`l2` or `huber`)
+- `--huber_c`: Huber loss parameter (default: 0.001)
+- `--ema_decay`: EMA decay rate for target network (default: 0.95)
+
+### Optimization
+
+- `--use_8bit_adam`: Use 8-bit Adam optimizer
+- `--adam_beta1`, `--adam_beta2`: Adam optimizer betas
+- `--adam_weight_decay`: Weight decay
+- `--adam_epsilon`: Adam epsilon
+- `--max_grad_norm`: Maximum gradient norm for clipping
+- `--mixed_precision`: Mixed precision training (`no`, `fp16`, `bf16`)
+- `--gradient_checkpointing`: Enable gradient checkpointing
+- `--enable_xformers_memory_efficient_attention`: Use xFormers
+- `--allow_tf32`: Allow TF32 on Ampere GPUs
+
+### Validation
+
+- `--val_image_url_or_path`: Validation image path/URL
+- `--validation_prompt`: Validation edit prompt
+- `--num_validation_images`: Number of validation images to generate
+- `--validation_steps`: Validate every N steps
+
+### Logging & Checkpointing
+
+- `--output_dir`: Output directory for checkpoints
+- `--logging_dir`: TensorBoard logging directory
+- `--report_to`: Reporting integration (`tensorboard`, `wandb`)
+- `--checkpointing_steps`: Save checkpoint every N steps
+- `--checkpoints_total_limit`: Maximum number of checkpoints to keep
+- `--resume_from_checkpoint`: Resume from checkpoint path
+
+### Hub Integration
+
+- `--push_to_hub`: Push model to HuggingFace Hub
+- `--hub_token`: HuggingFace Hub token
+- `--hub_model_id`: Hub model ID
+
+## Training Example
+
+### Basic Training
+
+```bash
+python train_lcm_distil_instruct_pix2pix_sdxl.py \
+  --pretrained_teacher_model="diffusers/sdxl-instructpix2pix" \
+  --dataset_name="your/instruct-pix2pix-dataset" \
+  --output_dir="./output/lcm-sdxl-instruct" \
+  --resolution=768 \
+  --train_batch_size=4 \
+  --gradient_accumulation_steps=4 \
+  --learning_rate=1e-4 \
+  --max_train_steps=10000 \
+  --validation_steps=500 \
+  --checkpointing_steps=500 \
+  --mixed_precision="fp16" \
+  --seed=42
+```
+### Advanced Training with Optimizations
+
+```bash
+accelerate launch --multi_gpu train_lcm_distil_instruct_pix2pix_sdxl.py \
+  --pretrained_teacher_model="diffusers/sdxl-instructpix2pix" \
+  --dataset_name="your/instruct-pix2pix-dataset" \
+  --output_dir="./output/lcm-sdxl-instruct" \
+  --resolution=768 \
+  --train_batch_size=2 \
+  --gradient_accumulation_steps=8 \
+  --learning_rate=5e-5 \
+  --max_train_steps=20000 \
+  --num_ddim_timesteps=50 \
+  --w_min=3.0 \
+  --w_max=15.0 \
+  --ema_decay=0.95 \
+  --loss_type="huber" \
+  --huber_c=0.001 \
+  --mixed_precision="bf16" \
+  --gradient_checkpointing \
+  --enable_xformers_memory_efficient_attention \
+  --use_8bit_adam \
+  --validation_steps=250 \
+  --val_image_url_or_path="path/to/val_image.jpg" \
+  --validation_prompt="make it sunset" \
+  --num_validation_images=4 \
+  --checkpointing_steps=500 \
+  --checkpoints_total_limit=3 \
+  --push_to_hub \
+  --hub_model_id="your-username/lcm-sdxl-instruct" \
+  --report_to="wandb"
+```
+## How It Works
+
+### Architecture
+
+1. **Teacher U-Net**: Pre-trained 8-channel InstructPix2Pix SDXL U-Net
+   - Input: Concatenated noisy latent + original image latent (8 channels)
+   - Performs multi-step diffusion with classifier-free guidance
+
+2. **Student U-Net**: Distilled model with time conditioning
+   - Learns to predict in a single step what teacher predicts in multiple steps
+   - Uses guidance scale embedding for conditioning
+
+3. **Target U-Net**: EMA copy of student
+   - Provides stable training targets
+   - Updated with exponential moving average
+
+### Training Process
+
+The training loop implements the LCM distillation algorithm:
+
+1. **Sample timestep** from DDIM schedule
+2. **Add noise** to latents at sampled timestep
+3. **Sample guidance scale** $w$ from uniform distribution $[w_{min}, w_{max}]$
+4. **Student prediction**: Single-step prediction from noisy latents
+5. **Teacher prediction**: Multi-step DDIM prediction with CFG
+6. **Target prediction**: Prediction from EMA target network
+7. **Compute loss**: L2 or Huber loss between student and target
+8. **Update**: Backpropagate and update student, then EMA update target
+
+### Loss Functions
+
+**L2 Loss:**
+$$\mathcal{L} = \text{MSE}(\text{model\_pred}, \text{target})$$
+
+**Huber Loss:**
+$$\mathcal{L} = \sqrt{(\text{model\_pred} - \text{target})^2 + c^2} - c$$
+
+## Output Structure
+
+After training, the output directory contains:
+
+
+output_dir/
+├── unet/                      # Final student U-Net
+├── unet_target/               # Final target U-Net (recommended for inference)
+├── text_encoder/              # Text encoder (copied from teacher)
+├── text_encoder_2/            # Second text encoder (SDXL)
+├── tokenizer/                 # Tokenizer
+├── tokenizer_2/               # Second tokenizer
+├── vae/                       # VAE
+├── scheduler/                 # LCM Scheduler
+├── checkpoint-{step}/         # Training checkpoints
+└── logs/                      # TensorBoard logs
+
+## Inference
+
+After training, use the model for fast image editing:
+
+python
+from diffusers import StableDiffusionXLInstructPix2PixPipeline, LCMScheduler
+from PIL import Image
+
+# Load the trained model
+```bash
+pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(
+"./output/lcm-sdxl-instruct",
+torch_dtype=torch.float16
+).to("cuda")
+
+# Load image
+image = Image.open("input.jpg")
+
+# Edit with just 4 steps!
+edited_image = pipeline(
+prompt="make it a sunset scene",
+image=image,
+num_inference_steps=4,
+guidance_scale=7.5,
+image_guidance_scale=1.5
+).images[0]
+
+edited_image.save("output.jpg")
+```
+## Tips & Best Practices
+
+### Memory Optimization
+- Use `--gradient_checkpointing` to reduce memory usage
+- Enable `--enable_xformers_memory_efficient_attention` for efficiency
+- Use `--mixed_precision="fp16"` or `"bf16"`
+- Reduce `--train_batch_size` and increase `--gradient_accumulation_steps`
+
+### Training Stability
+- Start with `--ema_decay=0.95` for stable target updates
+- Use `--loss_type="huber"` for more robust training
+- Adjust `--w_min` and `--w_max` based on your dataset
+- Monitor validation outputs regularly
+
+### Quality vs Speed
+- More `--num_ddim_timesteps` = better teacher guidance but slower training
+- Higher `--ema_decay` = more stable but slower convergence
+- Experiment with different `--learning_rate` values (1e-5 to 5e-4)
+
+### Multi-GPU Training
+Use Accelerate for distributed training:
+bash
+accelerate config  # Configure once
+accelerate launch train_lcm_distil_instruct_pix2pix_sdxl.py [args]
+
+## Troubleshooting
+
+**NaN Loss**: 
+- Try `--vae_precision="fp32"`
+- Reduce learning rate
+- Use gradient clipping with appropriate `--max_grad_norm`
+
+**Out of Memory**:
+- Enable gradient checkpointing
+- Reduce batch size
+- Lower resolution
+- Use xFormers attention
+
+**Poor Quality**:
+- Increase training steps
+- Adjust guidance scale range
+- Check dataset quality
+- Validate teacher model performance first
+
+## Citation
+
+If you use this code, please cite the relevant papers:
+
+bibtex
+@article{luo2023latent,
+  title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference},
+  author={Luo, Simian and Tan, Yiqin and Huang, Longbo and Li, Jian and Zhao, Hang},
+  journal={arXiv preprint arXiv:2310.04378},
+  year={2023}
+}
+
+@article{brooks2023instructpix2pix,
+  title={InstructPix2Pix: Learning to Follow Image Editing Instructions},
+  author={Brooks, Tim and Holynski, Aleksander and Efros, Alexei A},
+  journal={CVPR},
+  year={2023}
+}
+
+## License
+
+Please refer to the original model licenses and dataset licenses when using this code.
+
+## Acknowledgments
+
+This implementation is based on:
+- [Diffusers](https://github.com/huggingface/diffusers) library
+- Latent Consistency Models paper
+- InstructPix2Pix methodology
+- Stable Diffusion XL architecture
+
+Developer by (https://medium.com/@mzeynali01)