Create README.md

README.md

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+# Model Overview
+
+This model is a fine-tuned Denoising Diffusion Probabilistic Model (DDPM) for generating images of flowers using the Oxford Flowers dataset. It builds upon the pretrained google/ddpm-cifar10-32 model and is optimized for training on a GPU.
+
+# Model Details
+```
+
+Architecture: UNet2DModel
+
+Noise Scheduler: DDPMScheduler
+
+Training Data: Oxford Flowers dataset (nelorth/oxford-flowers)
+
+Optimizer: AdamW
+
+Learning Rate: 1e-4, adjusted using a cosine scheduler
+
+Training Steps: 100 epochs
+
+Batch Size: 64
+
+Image Size: 32x32 pixels
+```
+
+# Training Configuration
+
+The training process involves the following steps:
+
+# Data Preprocessing:
+
+Images resized to 32x32.
+
+Random horizontal flipping applied for augmentation.
+
+Normalized to the range [-1, 1].
+
+# Noise Addition:
+
+Random noise added to images using a linear beta schedule.
+
+# Model Training:
+
+The UNet model predicts the noise added to images.
+
+The Mean Squared Error (MSE) loss is used.
+
+The learning rate is adjusted with a cosine scheduler.
+
+# Checkpointing:
+
+Model checkpoints are saved every 1000 steps.
+
+# Usage
+
+Once trained, the model can be used for generating images of flowers. The trained model is saved as a DDPMPipeline and can be loaded for inference.
+
+# Model Inference
+  ```
+python
+
+A quantized version of the model is available for reduced memory usage and faster inference on resource-limited devices.
+
+from optimum.intel.openvino import OVModelForImageGeneration
+
+pipeline = OVModelForImageGeneration.from_pretrained("flower_diffusion_quantized", export=True)
+images = pipeline(batch_size=4, num_inference_steps=50).images
+images[0].show()
+```
+# Model Variants
+
+FP32 Version: Standard precision model.
+
+FP16 Version: Reduced precision for lower memory usage.
+
+# Limitations and Considerations
+
+Image Resolution: Trained at 32x32, which may limit the fine details.
+
+Computational Requirements: A GPU is recommended for inference.
+
+Dataset Bias: The model is trained solely on Oxford Flowers, so its generalization to other datasets is limited.
+
+Quantized Model Accuracy: INT8 quantization may slightly reduce output quality but speeds up inference.
+