diff --git a/atoma-inference/src/candle/stable_diffusion.rs b/atoma-inference/src/candle/stable_diffusion.rs
index a8a8b5c7..4122c260 100644
--- a/atoma-inference/src/candle/stable_diffusion.rs
+++ b/atoma-inference/src/candle/stable_diffusion.rs
@@ -304,6 +304,7 @@ impl CandleModel for StableDiffusion {
                         .to_vec1::<f64>()?,
                 )
                 .unwrap();
+                println!("time step: {}", timestep);
                 let mut file = File::create(format!("output-{timestep_index}.json"))?;
                 file.write_all(json_output.as_bytes())?;
 
@@ -315,9 +316,35 @@ impl CandleModel for StableDiffusion {
 
                 let latent_model_input =
                     scheduler.scale_model_input(latent_model_input, timestep)?;
+                let json_output = serde_json::to_string(
+                    &latent_model_input
+                        .clone()
+                        .to_device(&Device::Cpu)?
+                        .flatten_all()?
+                        .to_dtype(DType::F64)?
+                        .to_vec1::<f64>()?,
+                )
+                .unwrap();
+                println!("time step: {}", timestep);
+                let mut file = File::create(format!("latent_model_input-{timestep_index}.json"))?;
+                file.write_all(json_output.as_bytes())?;
+
                 let noise_pred =
                     unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
 
+                let json_output = serde_json::to_string(
+                    &noise_pred
+                        .clone()
+                        .to_device(&Device::Cpu)?
+                        .flatten_all()?
+                        .to_dtype(DType::F64)?
+                        .to_vec1::<f64>()?,
+                )
+                .unwrap();
+                println!("time step: {}", timestep);
+                let mut file = File::create(format!("noise_pred-{timestep_index}.json"))?;
+                file.write_all(json_output.as_bytes())?;
+
                 let noise_pred = if use_guide_scale {
                     let noise_pred = noise_pred.chunk(2, 0)?;
                     let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);