config.json

{
  "model_type": "diffusion",
  "architectures": ["UNet"],
  "model_name": "conditional_diffusion_medical",
  "description": "Conditional diffusion model for medical image generation",
  "version": "1.0.0",
  "author": "Your Name",
  "license": "MIT",
  "tags": ["diffusion", "medical", "image-generation", "conditional"],
  "model_config": {
    "input_channels": 4,
    "output_channels": 4,
    "image_size": 256,
    "embed_dim": 256,
    "embed_dim_mask": 256,
    "input_dim_mask": 262144,
    "channels": [32, 64, 128, 256, 512],
    "lambda": 25.0,
    "num_steps": 250,
    "eps": 0.001
  },
  "training_config": {
    "learning_rate": 2e-4,
    "batch_size": 1,
    "epochs": 5000,
    "optimizer": "Adam",
    "loss": "score_matching",
    "scheduler": "LambdaLR"
  },
  "dataset": {
    "name": "medical_ct_scans",
    "train_samples": 3346,
    "input_shape": [4, 256, 256],
    "conditioning_shape": [4, 256, 256]
  },
  "inference": {
    "device": "cuda",
    "sampler": "Euler_Maruyama",
    "num_steps": 250,
    "eps": 0.001
  }
}