diff --git a/scripts/gradio/depth2img.py b/scripts/gradio/depth2img.py
index c791a4d0..e4cc2445 100644
--- a/scripts/gradio/depth2img.py
+++ b/scripts/gradio/depth2img.py
@@ -100,7 +100,7 @@ def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=No
         if not do_full_sample:
             # encode (scaled latent)
             z_enc = sampler.stochastic_encode(
-                z, torch.tensor([t_enc] * num_samples).to(model.device))
+                z, torch.tensor([t_enc - 1] * num_samples).to(model.device))
         else:
             z_enc = torch.randn_like(z)
         # decode it
diff --git a/scripts/img2img.py b/scripts/img2img.py
index 9085ba9d..2f1387a7 100644
--- a/scripts/img2img.py
+++ b/scripts/img2img.py
@@ -244,7 +244,7 @@ def main():
                         c = model.get_learned_conditioning(prompts)
 
                         # encode (scaled latent)
-                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
+                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc - 1] * batch_size).to(device))
                         # decode it
                         samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
                                                  unconditional_conditioning=uc, )
diff --git a/scripts/streamlit/depth2img.py b/scripts/streamlit/depth2img.py
index 7f802234..a7c845ae 100644
--- a/scripts/streamlit/depth2img.py
+++ b/scripts/streamlit/depth2img.py
@@ -93,7 +93,7 @@ def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=No
         uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
         if not do_full_sample:
             # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(z, torch.tensor([t_enc] * num_samples).to(model.device))
+            z_enc = sampler.stochastic_encode(z, torch.tensor([t_enc - 1] * num_samples).to(model.device))
         else:
             z_enc = torch.randn_like(z)
         # decode it