up

run.py

@@ -269,20 +269,21 @@ with torch.no_grad():
     for i in range(sde.N):
         t = timesteps[i]
         vec_t = torch.ones(shape[0], device=t.device) * t
-#        x, x_mean = corrector_update_fn(x, vec_t, model=model)
-#        x, x_mean = predictor_update_fn(x, vec_t, model=model)
-        x, x_mean = new_corrector.update_fn(x, vec_t)
-        x, x_mean = new_predictor.update_fn(x, vec_t)
+        x, x_mean = corrector_update_fn(x, vec_t, model=model)
+        x, x_mean = predictor_update_fn(x, vec_t, model=model)
+#        x, x_mean = new_corrector.update_fn(x, vec_t)
+#        x, x_mean = new_predictor.update_fn(x, vec_t)
 
     x, n = inverse_scaler(x_mean if denoise else x), sde.N * (n_steps + 1)
 
 
+save_image(x)
+
 # for 5
-#assert x.abs().sum().cpu().item() - 106114.90625 < 1e-2, "sum wrong"
-#assert x.abs().mean().cpu().item() - 34.5426139831543 < 1e-4, "mean wrong"
+#assert (x.abs().sum() - 106114.90625).cpu().item() < 1e-2, f"sum wrong {x.abs().sum()}"
+#assert (x.abs().mean() - 34.5426139831543).abs().cpu().item() < 1e-4, f"mean wrong {x.abs().mean()}"
 
 # for 1000
-assert x.abs().sum().cpu().item() - 436.5811 < 1e-2, "sum wrong"
-assert x.abs().mean().cpu().item() - 0.1421 < 1e-4, "mean wrong"
+assert (x.abs().sum() - 436.5811).abs().sum().cpu().item() < 1e-2, f"sum wrong {x.abs().sum()}"
+assert (x.abs().mean() - 0.1421).abs().mean().cpu().item() < 1e-4, f"mean wrong {x.abs().mean()}"
 
-save_image(x)