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Daro77/
stable-diffusion-2-inpainting-gaf-mtf-rp-spec

Name: stable-diffusion-2-inpainting-gaf-mtf-rp-spec
Availability: InStock

diffusers

safetensors

Daro77/stable-diffusion-2-inpainting-gaf-mtf-rp-spec-training-data

2112.10752

stabilityai/stable-diffusion-2-inpainting

finetune

apache-2.0

StableDiffusionInpaintPipeline

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Fine-tuned Stable Diffusion 2 Model for Time Series Image Inpainting

This directory contains a fine-tuned Stable Diffusion 2 inpainting model specialized for reconstructing mathematical time series visualizations (GAF, MTF, RP, Spectrogram).

📋 Model Overview

Property	Value
Base Model	`stabilityai/stable-diffusion-2-inpainting`
Specialized For	Mathematical time series image reconstruction
Image Types	GAF, MTF, RP, Spectrogram
Input Size	512×512 pixels
Architecture	UNet2DConditionModel (fine-tuned)
Training Method	Cross-validation with early stopping
Best Validation Loss	0.03623
Training Date	September 2024

🎯 Purpose

This model was fine-tuned to perform inpainting on time series visualizations. It can:

Reconstruct missing regions in GAF (Gramian Angular Field) images
Fill gaps in MTF (Markov Transition Field) images
Complete RP (Recurrence Plot) images
Restore Spectrogram images

The model is specifically trained on synthetic time series data with controlled missing patterns (random, block, periodic, edge).

📁 Model Structure

best_model/
├── README.md                 # This file
├── model_index.json          # Model configuration
├── scheduler/                # DDPM Scheduler
├── text_encoder/            # CLIP Text Encoder (frozen)
├── tokenizer/               # CLIP Tokenizer
├── unet/                    # Fine-tuned UNet (main trainable component)
└── vae/                     # VAE Encoder/Decoder (frozen)

Note: Only the UNet component was fine-tuned. All other components (VAE, text encoder) remain frozen from the base Stable Diffusion 2 model.

🚀 Quick Start

Installation

bash

pip install torch torchvision
pip install diffusers transformers accelerate
pip install pillow numpy

Basic Usage

python

from diffusers import StableDiffusionInpaintPipeline
import torch
from PIL import Image

# Load the fine-tuned model
model_path = "models/stable_diffusion_2_all_4/best_model"
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
    model_path,
    torch_dtype=torch.float16
).to("cuda")

# Load images
image = Image.open("missing_image.png").convert("RGB").resize((512, 512))
mask = Image.open("mask.png").convert("L").resize((512, 512))

# Inpaint with appropriate prompt
prompt = "high quality gramian angular field mathematical visualization"
result = pipeline(
    prompt=prompt,
    image=image,
    mask_image=mask,
    num_inference_steps=50
).images[0]

result.save("reconstructed_image.png")

Image Type-Specific Prompts

Use these prompts for best results:

python

prompts = {
    "gaf": "high quality gramian angular field mathematical visualization",
    "mtf": "high quality markov transition field mathematical visualization",
    "rp": "high quality recurrence plot mathematical visualization",
    "spec": "high quality spectrogram mathematical visualization"
}

🏋️ Training Details

Training Configuration

Parameter	Value
Training Samples	~3,000 per fold
Validation Samples	~1,000 per fold
Batch Size	4
Learning Rate	1e-5
Optimizer	AdamW (weight_decay=0.01)
Training Runs	2 folds
Early Stopping	Patience = 5 epochs
Mixed Precision	No (FP32)
Epochs Trained	9-13 per fold

Training Results

Fold 1:

Best Validation Loss: 0.03802
Epochs: 13
Final Training Loss: 0.03784

Fold 2 (Best Model):

Best Validation Loss: 0.03623 ⭐
Epochs: 9
Final Training Loss: 0.03872

Overall Performance:

Mean Validation Loss: 0.03713 ± 0.00089
This model represents Fold 2 (best performer)

Memory Optimizations Used

Attention slicing enabled
Gradient checkpointing
Only UNet fine-tuned (VAE and text encoder frozen)

📊 Training Dataset

The model was trained on synthetically generated time series with:

2,000 base samples = 8,000 images (4 types each)
Pattern types: sine, cosine, trend, seasonal, noise, spikes, mixed
Missing data types: random, block, periodic, edge
Missing rates: 5% - 30%
Time series lengths: 100 - 1,000 points

Dataset generation script: generate_training_dataset.py

💻 System Requirements

Minimum Requirements

GPU: NVIDIA GPU with 8+ GB VRAM
CUDA: 11.0+
RAM: 16 GB
Storage: ~20 GB for model

Recommended Requirements

GPU: NVIDIA GPU with 16+ GB VRAM (RTX 3090, A5000, or better)
CUDA: 11.8+
RAM: 32 GB
Storage: 50 GB (for model + datasets)

Tested On

GPU: NVIDIA TITAN RTX (24 GB GDDR6)
Driver: 525.147.05
CUDA: 12.0
OS: Ubuntu 18.04.1 LTS

🎨 Example Use Cases

1. Recovering Missing Time Series Data

python

# Load corrupted time series image
corrupted_img = load_corrupted_timeseries_image()
mask = create_missing_data_mask()

# Reconstruct using fine-tuned model
reconstructed = pipeline(
    prompt="high quality gramian angular field mathematical visualization",
    image=corrupted_img,
    mask_image=mask,
    guidance_scale=7.5,
    num_inference_steps=50
).images[0]

2. Batch Processing Multiple Images

python

import glob
from pathlib import Path

# Process all GAF images
for img_path in glob.glob("data/missing/*.png"):
    image = Image.open(img_path).resize((512, 512))
    mask = generate_mask_from_image(image)
    
    result = pipeline(
        prompt="high quality gramian angular field mathematical visualization",
        image=image,
        mask_image=mask
    ).images[0]
    
    output_path = Path("data/reconstructed") / Path(img_path).name
    result.save(output_path)

3. Integration with Forecasting Pipeline

python

# 1. Time series → Image (GAF/MTF/RP/SPEC)
image = time_series_to_gaf(corrupted_series)

# 2. Create mask for missing regions
mask = create_mask_from_nan(corrupted_series)

# 3. Inpaint image
reconstructed_image = pipeline(
    prompt="high quality gramian angular field mathematical visualization",
    image=image,
    mask_image=mask
).images[0]

# 4. Image → Time series
reconstructed_series = gaf_to_time_series(reconstructed_image)

# 5. Use for forecasting
forecast = xgboost_model.predict(reconstructed_series)

🔧 Advanced Configuration

Inference Parameters

python

result = pipeline(
    prompt=prompt,
    image=image,
    mask_image=mask,
    
    # Quality settings
    num_inference_steps=50,        # 20-100, higher = better quality
    guidance_scale=7.5,            # 1-20, higher = closer to prompt
    
    # Generation settings
    num_images_per_prompt=1,
    generator=torch.manual_seed(42),  # For reproducibility
    
    # Advanced
    eta=0.0,                       # DDIM eta parameter
    output_type="pil"              # "pil" or "np"
)

Memory Management

python

# For limited VRAM
pipeline.enable_attention_slicing()
pipeline.enable_vae_slicing()

# Use CPU offloading
pipeline.enable_sequential_cpu_offload()

# Lower precision
pipeline = pipeline.to(torch_dtype=torch.float16)

📈 Performance Metrics

Validation Performance

Metric	Value
Best Val Loss	0.03623
Mean Val Loss	0.03713 ± 0.00089
Training Stability	High (early stopping at epoch 9)

Inference Speed (TITAN RTX)

Inference Steps	Time per Image
20 steps	~2.5 seconds
50 steps	~5.5 seconds
100 steps	~11 seconds

🔬 Technical Details

Model Architecture

Base: Stable Diffusion 2 Inpainting

UNet: 859M parameters (fine-tuned)
- Channels: 320, 640, 1280, 1280
- Attention layers: Self-attention + Cross-attention
- Conditioning: Text embeddings (CLIP)
VAE: 83M parameters (frozen)
- Encoder: 512×512 → 64×64 latents
- Decoder: 64×64 latents → 512×512
- Latent channels: 4
Text Encoder: CLIP (frozen)
- OpenCLIP-ViT-H/14
- 354M parameters

Training Objective

The model minimizes MSE loss in latent space:

L = E[||ε - ε_θ(z_t, t, c)||²]

Where:

ε: True noise
ε_θ: Model prediction
z_t: Noisy latents at timestep t
c: Text conditioning (prompt)

📝 How to Retrain or Fine-tune Further

Re-training from Scratch

bash

python finetune_stable_diffusion.py \
    --data_dir stdiff_training_data \
    --output_dir models/my_new_model \
    --max_samples 4000 \
    --batch_size 4 \
    --learning_rate 1e-5 \
    --max_epochs 300 \
    --n_folds 2 \
    --train_ratio 0.75 \
    --early_stop_patience 5

Continuing Training from This Model

bash

# Load this model and continue training
python finetune_stable_diffusion.py \
    --data_dir stdiff_training_data \
    --output_dir models/stable_diffusion_2_all_4_continued \
    --resume_fold 2 \
    --max_epochs 50

Training on Custom Dataset

Generate your dataset:

bash

python generate_training_dataset.py \
    --samples 5000 \
    --output my_custom_data

Train model:

bash

python finetune_stable_diffusion.py \
    --data_dir my_custom_data \
    --output_dir models/custom_model

🐛 Troubleshooting

Out of Memory (OOM)

python

# Reduce batch size, use attention slicing
pipeline.enable_attention_slicing()
pipeline.enable_vae_slicing()

# Or use float16
pipeline = pipeline.to(torch_dtype=torch.float16)

Poor Reconstruction Quality

Increase inference steps: 20 → 50 or 100
Adjust guidance scale: Try 5.0 - 10.0
Check prompt: Use correct image type prompt
Mask quality: Ensure mask accurately covers missing regions

Slow Inference

python

# Use fewer inference steps (trade quality for speed)
result = pipeline(..., num_inference_steps=20)

# Use DPM++ Sampler (faster)
from diffusers import DPMSolverMultistepScheduler
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
    pipeline.scheduler.config
)

📚 Related Files

Training Script: finetune_stable_diffusion.py
Dataset Generator: generate_training_dataset.py
Integration Script: integrate_custom_model.py
Main Experiment: iterative_experiment.py
Image Encoders: ts_image_inpainting.py

📖 References

Base Model

bibtex

@article{rombach2022high,
  title={High-resolution image synthesis with latent diffusion models},
  author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn},
  journal={CVPR},
  year={2022}
}

Stable Diffusion 2

HuggingFace: https://huggingface.co/stabilityai/stable-diffusion-2-inpainting
Paper: https://arxiv.org/abs/2112.10752

🔗 Integration with Experimental Pipeline

This model can be used in the main experimental pipeline:

python

# In iterative_experiment.py or ts_image_inpainting.py
from models.stdiff import StableDiffusion2MathInpainter

# Initialize inpainter with this model
inpainter = StableDiffusion2MathInpainter(
    model_path="models/stable_diffusion_2_all_4/best_model"
)

# Use for inpainting
reconstructed = inpainter.inpaint(
    image=corrupted_gaf_image,
    mask=missing_mask,
    enc_name="gaf"
)

⚖️ License

This model is a fine-tuned version of Stable Diffusion 2, which is released under the CreativeML OpenRAIL-M license.

Base Model License: CreativeML OpenRAIL-M
Fine-tuned Weights: Same as base model

See: https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/LICENSE

🤝 Citation

If you use this model in your research, please cite:

bibtex

@misc{ts_sd2_finetuned_2024,
  title={Fine-tuned Stable Diffusion 2 for Time Series Image Inpainting},
  author={[Dariusz Kobiela, Jarosław Kobiela, Adam Kurowski, Agnieszka Landowska]},
  year={2025},
  howpublished={Trained on synthetic time series dataset},
  note={Fine-tuned from stabilityai/stable-diffusion-2-inpainting}
}

📧 Support

For questions or issues:

Check the troubleshooting section above
Review training logs in ../cross_validation_results.json
Consult finetune_stable_diffusion.py documentation
Check original Stable Diffusion 2 documentation

Model Version: 1.0
Last Updated: 1.12.2025
Status: Production Ready ✅