diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 2572443f9..0b7ca6330 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -22,7 +22,12 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
-    dnw = K.external.CompVisDenoiser(shared.sd_model)
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
+        dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -37,7 +42,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
         t = dnw.sigma_to_t(sigma_in)
 
         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@@ -69,7 +74,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
-    dnw = K.external.CompVisDenoiser(shared.sd_model)
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
+        dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -84,7 +94,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
 
         if i == 1:
             t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))