diff --git a/modules/infotext_utils.py b/modules/infotext_utils.py
index a938aa2a7..e04a7bee9 100644
--- a/modules/infotext_utils.py
+++ b/modules/infotext_utils.py
@@ -359,6 +359,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Emphasis" not in res:
         res["Emphasis"] = "Original"
 
+    if "Refiner switch by sampling steps" not in res:
+        res["Refiner switch by sampling steps"] = False
+
     infotext_versions.backcompat(res)
 
     for key in skip_fields:
diff --git a/modules/infotext_versions.py b/modules/infotext_versions.py
index 23b45c3f9..b5552a312 100644
--- a/modules/infotext_versions.py
+++ b/modules/infotext_versions.py
@@ -5,6 +5,7 @@ import re
 
 v160 = version.parse("1.6.0")
 v170_tsnr = version.parse("v1.7.0-225")
+v180 = version.parse("1.8.0")
 
 
 def parse_version(text):
@@ -40,3 +41,5 @@ def backcompat(d):
     if ver < v170_tsnr:
         d["Downcast alphas_cumprod"] = True
 
+    if ver < v180 and d.get('Refiner'):
+        d["Refiner switch by sampling steps"] = True
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 045b9e2fe..6df423912 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -155,14 +155,16 @@ def replace_torchsde_browinan():
 replace_torchsde_browinan()
 
 
-def apply_refiner(cfg_denoiser, sigma):
-    if opts.refiner_switch_by_sample_steps:
+def apply_refiner(cfg_denoiser, sigma=None):
+    if opts.refiner_switch_by_sample_steps or not sigma:
         completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+        cfg_denoiser.p.extra_generation_params["Refiner switch by sampling steps"] = True
+
     else:
         # torch.max(sigma) only to handle rare case where we might have different sigmas in the same batch
         try:
             timestep = torch.argmin(torch.abs(cfg_denoiser.inner_model.sigmas - torch.max(sigma)))
-        except AttributeError: # for samplers that dont use sigmas (DDIM) sigma is actually the timestep
+        except AttributeError:  # for samplers that don't use sigmas (DDIM) sigma is actually the timestep
             timestep = torch.max(sigma).to(dtype=int)
         completed_ratio = (999 - timestep) / 1000