16:T5c6, use candle_core::{DType, Device, Module, Result, Tensor}; use candle_nn::{linear, Linear, Optimizer, VarBuilder, VarMap}; struct DiffusionModel { fc1: Linear, fc2: Linear, fc3: Linear, } impl DiffusionModel { fn new(vs: VarBuilder) -> Result { let fc1 = linear(784, 512, vs.pp("fc1"))?; let fc2 = linear(512, 256, vs.pp("fc2"))?; let fc3 = linear(256, 784, vs.pp("fc3"))?; Ok(Self { fc1, fc2, fc3 }) } fn forward(&self, x: &Tensor) -> Result { let x = self.fc1.forward(x)?; let x = x.relu()?; let x = self.fc2.forward(&x)?; let x = x.relu()?; self.fc3.forward(&x) } } fn main() -> Result<()> { let device = Device::cuda_if_available(0)?; let varmap = VarMap::new(); let vs = VarBuilder::from_varmap(&varmap, DType::F32, &device); let model = DiffusionModel::new(vs.clone())?; let dummy_input = Tensor::zeros((1, 784), DType::F32, &device)?; let output = model.forward(&dummy_input)?; println!("Output shape: {:?}", output.shape()); Ok(()) } [package] name = "diffusion-model" version = "0.1.0" edition = "2021" [dependencies] candle-core = { version = "0.1", features = ["cuda"] } candle-nn = "0.1" 17:T44d,use candle_core::{DType, Device, Module, Result, Tensor}; use candle_nn::{linear, Linear, Optimizer, VarBuilder, VarMap}; struct DiffusionModel { fc1: Linear, fc2: Linear, fc3: Linear, } impl DiffusionModel { fn new(vs: VarBuilder) -> Result { let fc1 = linear(784, 512, vs.pp("fc1"))?; let fc2 = linear(512, 256, vs.pp("fc2"))?; let fc3 = linear(256, 784, vs.pp("fc3"))?; Ok(Self { fc1, fc2, fc3 }) } fn forward(&self, x: &Tensor) -> Result { let x = self.fc1.forward(x)?;