Create README.md

README.md

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+---
+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
+
+## Model Card Contact
+[Contact information via HuggingFace repository]
+
+## 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
+
+---