---
license: mit
datasets:
- maryzhang/vessels-motifs-dataset
language:
- en
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
---
# Chinese Porcelain Motif LoRA

## Model Details

### Model Description
This is a LoRA (Low-Rank Adaptation) fine-tuned version of `runwayml/stable-diffusion-v1-5` specifically trained to generate Chinese porcelain motif patterns. The model specializes in creating intricate line-based decorative patterns characteristic of traditional Chinese porcelain artwork.

- **Developed by:** Mary Zhang
- **Model type:** LoRA fine-tuned Stable Diffusion v1.5
- **Language(s):** English prompts
- **License:** [Inherit from base model - CreativeML Open RAIL-M]
- **Finetuned from model:** runwayml/stable-diffusion-v1-5
- **Repository:** https://huggingface.co/maryzhang/motif_lora

### Model Sources
- **Repository:** https://huggingface.co/maryzhang/motif_lora
- **Base Model:** https://huggingface.co/runwayml/stable-diffusion-v1-5 currently redirected to https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5

## Uses

### Direct Use
This model is designed for generating Chinese porcelain motif patterns for:
- Digital art creation
- Pattern design for ceramics
- Educational materials about Chinese decorative arts
- Inspiration for traditional craft designs
- Cultural heritage visualization

### Downstream Use
The model can be integrated into:
- Design software for ceramic artists
- Educational applications for art history
- Digital restoration projects for historical porcelain patterns
- Commercial design workflows for products inspired by Chinese motifs

### Out-of-Scope Use
This model should not be used for:
- Generating photorealistic images unrelated to porcelain motifs
- Creating culturally insensitive or inappropriate content
- Authenticating or verifying historical artifacts
- Commercial reproduction of copyrighted porcelain designs

## Bias, Risks, and Limitations

### Biases
- **Cultural Representation:** The model is trained specifically on Chinese porcelain motifs and may not accurately represent other ceramic traditions
- **Historical Period Bias:** Depending on the training dataset, certain dynasties or periods may be overrepresented
- **Style Bias:** May favor certain decorative styles (e.g., blue and white porcelain) over others

### Risks
- **Cultural Appropriation:** Users should be mindful of respectful use of traditional Chinese artistic elements
- **Historical Accuracy:** Generated patterns should not be presented as authentic historical designs
- **Quality Variance:** Output quality may vary based on prompt specificity

### Limitations
- Limited to decorative motif generation, not full porcelain object rendering
- Best results with prompts including "porcelain motif" trigger phrase
- Resolution optimized for 512×512 pixels
- May struggle with highly specific regional or temporal style variations

### Recommendations
Users should:
- Include cultural context when sharing generated images
- Verify historical accuracy if using for educational purposes
- Test various prompt formulations for optimal results
- Consider combining with other models for complete ceramic designs

## How to Get Started with the Model

```python
from diffusers import StableDiffusionPipeline
import torch

# Load the pipeline with LoRA weights
model_id = "runwayml/stable-diffusion-v1-5"
lora_model_id = "maryzhang/motif_lora"

pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

# Load LoRA weights
pipe.load_lora_weights(lora_model_id)

# Generate an image
prompt = "porcelain motif, intricate blue and white floral pattern, traditional Chinese design"
image = pipe(
    prompt,
    num_inference_steps=50,
    guidance_scale=7.5,
    width=512,
    height=512
).images[0]

image.save("porcelain_motif.png")
```

## Training Details

### Training Data
The model was fine-tuned on a curated dataset of Chinese porcelain motif images, focusing on:
- Traditional decorative patterns from various dynasties
- Line-based artistic elements
- Both geometric and naturalistic motifs
- Various color schemes with emphasis on traditional palettes

### Training Procedure

#### Preprocessing
- Images resized to 512×512 pixels
- Normalization applied consistent with SD v1.5 preprocessing
- Augmentation: minimal to preserve pattern integrity

#### Training Hyperparameters
- **Training regime:** fp16 mixed precision
- **Epochs:** 3
- **Base Learning Rate:** 1e-4 (assumed standard for LoRA)
- **LoRA Rank:** 4-16 (typical range)
- **LoRA Alpha:** Same as rank or 2x rank
- **Target Modules:** Cross-attention layers (q, k, v, out projections)
- **Batch Size:** [Not specified - likely 1-4 given hardware constraints]
- **Training Resolution:** 512×512
- **Trigger Phrase:** "porcelain motif"

### Development Process

#### AI-Assisted Development
**Transparency Note:** ChatGPT was utilized during the development process for:
- **Training Script Generation:** Initial boilerplate code for LoRA fine-tuning setup
- **Data Preprocessing Pipeline:** Code for image resizing and dataset preparation
- **Hyperparameter Optimization:** Suggestions for learning rate schedules and LoRA configurations
- **Debugging Support:** Troubleshooting CUDA memory issues and gradient accumulation
- **Documentation:** Assistance with code comments and README formatting

Example AI-assisted code components:
```python
# Dataset preprocessing code structure suggested by ChatGPT
class PorcelainMotifDataset(Dataset):
    def __init__(self, image_paths, captions, transform=None):
        self.image_paths = image_paths
        self.captions = captions
        self.transform = transform
    
    def __getitem__(self, idx):
        # Implementation guided by AI assistance
        image = Image.open(self.image_paths[idx])
        if self.transform:
            image = self.transform(image)
        return image, self.captions[idx]
```

## Evaluation

### Testing Data
Evaluated on held-out set of porcelain motif images not seen during training, including:
- Historical museum photographs
- Contemporary porcelain designs
- Various regional styles

### Metrics
Qualitative evaluation based on:
- **Pattern Coherence:** Logical flow and completeness of decorative elements
- **Style Authenticity:** Resemblance to traditional Chinese porcelain motifs
- **Line Quality:** Clarity and precision of line-based patterns
- **Cultural Accuracy:** Appropriate use of traditional symbols and elements

### Results
- Successfully generates recognizable Chinese porcelain motif patterns
- Best performance with blue and white color schemes
- Strong reproduction of floral and geometric patterns
- Occasional blending of different historical periods in single outputs

## Environmental Impact

- **Hardware Type:** [Likely consumer GPU - RTX 3090/4090 or similar]
- **Hours used:** Approximately 6-12 hours for 3 epochs
- **Cloud Provider:** [If applicable]
- **Carbon Emitted:** Estimated 2-5 kg CO2eq based on typical GPU training

## Technical Specifications

### Model Architecture and Objective
- **Architecture:** U-Net with LoRA adaptations on cross-attention layers
- **Objective:** Denoising diffusion probabilistic model with LoRA fine-tuning

### Compute Infrastructure
#### Hardware
- GPU: [Specific model not provided - likely NVIDIA consumer/prosumer GPU]
- VRAM Requirements: Minimum 10GB recommended

#### Software
- **Framework:** PyTorch with Diffusers library
- **LoRA Implementation:** PEFT (Parameter-Efficient Fine-Tuning) library
- **Base Dependencies:** 
  - diffusers >= 0.21.0
  - transformers >= 4.25.0
  - accelerate >= 0.20.0
  - peft >= 0.4.0

## Citation

**BibTeX:**
```bibtex
@misc{zhang2024porcelainlora,
  author = {Zhang, Mary},
  title = {Chinese Porcelain Motif LoRA: Fine-Tuned Stable Diffusion for Traditional Pattern Generation},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/maryzhang/motif_lora}
}
```

**APA:**
Zhang, M. (2024). *Chinese Porcelain Motif LoRA: Fine-Tuned Stable Diffusion for Traditional Pattern Generation* [Model]. HuggingFace. https://huggingface.co/maryzhang/motif_lora

## Model Card Authors
Mary Zhang, Cassie Li


## Acknowledgments
- Runway ML for the base Stable Diffusion v1.5 model
- The open-source community for LoRA implementation tools
- ChatGPT (OpenAI) for development assistance with code generation and debugging
- Museums and cultural institutions whose publicly available porcelain collections inspired this work

## Version History
- **v1.0** (Current): Initial release with 3-epoch training on Chinese porcelain motifs

---