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README.md

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+---
+license: mit
+tags:
+- pytorch
+- diffusers
+- unconditional-image-generation
+- diffusion-models-class
+---
+
+### Fine-tuned Skin Rash Detection Model v2
+
+#### Model Overview
+- **Base Model**: `google/ddpm-celebahq-256`
+- **Fine-tuning Dataset**: Skin Rash Dataset
+- **Output Image Size**: 256 x 256 pixels
+
+#### Updates and Changes
+1. **Parameter Increase**:
+   - Previous Model: 18.54 Million Parameters
+   - Current Model: 113.67 Million Parameters
+
+2. **Image Output Size**:
+   - Previous: 32 x 32 pixels
+   - Current: 256 x 256 pixels
+
+3. **Batch Size**:
+   - Previous: 64 samples per batch
+   - Current: 6 samples per batch
+
+4. **Gradient Accumulation**:
+   - Implemented gradient accumulation to simulate a larger batch size without exceeding GPU memory limits.
+
+5. **Timesteps**:
+   - Reduced timesteps from 1000 to 40 for faster training and inference.
+
+#### Training Details
+- **Epochs**: 12
+- **Number of Training Images**: 656
+- **Optimizer**: AdamW with learning rate adjusted for gradient accumulation
+- **Learning Rate**: Initially set at \(1 	imes 10^-5\), adjusted for gradient accumulation
+- **Gradient Accumulation Steps**: 2
+- **Loss Function**: Mean Squared Error (MSE) Loss
+
+#### Performance
+- **Training Loss**: Monitored and recorded over 12 epochs, with periodic visualization of generated images to ensure the model learns effectively.
+
+#### Training Environment
+- **Hardware**: GPU with 15GB RAM
+- **Software**: PyTorch, torchvision, diffusers library
+
+#### Usage
+- The fine-tuned model can generate high-resolution images of skin rashes, which can be useful for medical analysis and diagnosis.
+- Due to the increased parameter count and higher resolution, the model is more accurate in capturing fine details in the images.
+
+#### Example Usage Code
+```python
+from diffusers import DDPMPipeline
+
+pipeline = DDPMPipeline.from_pretrained('DhruvParth/ddpm-celebahq-256-fineTuned-skin_rash_v2_12epochs')
+image = pipeline().images[0]
+image
+```
+```
+
+#### Considerations
+- **Batch Size**: Due to GPU memory constraints, batch size is limited to 6 samples.
+- **Training Time**: Reduced timesteps to 40, allowing faster training and inference without significantly compromising on quality.
+- **Gradient Accumulation**: Enabled to manage large model parameter count and small batch size effectively.
+
+This model card provides an overview of the fine-tuning process and the modifications made to enhance the model's performance on the skin rash dataset. The details should help users understand the changes and the expected usage of the model.