diff --git a/ldm_patched/modules/model_sampling.py b/ldm_patched/modules/model_sampling.py
index da5cc3a6..6bb08fd2 100644
--- a/ldm_patched/modules/model_sampling.py
+++ b/ldm_patched/modules/model_sampling.py
@@ -107,9 +107,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         sigma_min = sampling_settings.get("sigma_min", 0.002)
         sigma_max = sampling_settings.get("sigma_max", 120.0)
-        self.set_sigma_range(sigma_min, sigma_max)
+        sigma_data = sampling_settings.get("sigma_data", 1.0)
+        self.set_sigma_range(sigma_min, sigma_max, sigma_data)
 
-    def set_sigma_range(self, sigma_min, sigma_max):
+    def set_sigma_range(self, sigma_min, sigma_max, sigma_data):
+        self.sigma_data = sigma_data
         sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
 
         self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
diff --git a/ldm_patched/modules/supported_models.py b/ldm_patched/modules/supported_models.py
index de21c10d..3e1889bf 100644
--- a/ldm_patched/modules/supported_models.py
+++ b/ldm_patched/modules/supported_models.py
@@ -169,6 +169,11 @@ class SDXL(supported_models_base.BASE):
     def model_type(self, state_dict, prefix=""):
         if "v_pred" in state_dict:
             return model_base.ModelType.V_PREDICTION
+        elif "edm_vpred.sigma_max" in state_dict:
+            self.sampling_settings["sigma_max"] = round(float(state_dict["edm_vpred.sigma_max"].item()),3)
+            if "edm_vpred.sigma_min" in state_dict:
+                self.sampling_settings["sigma_min"] = round(float(state_dict["edm_vpred.sigma_min"].item()),3)
+            return model_base.ModelType.V_PREDICTION_EDM
         else:
             return model_base.ModelType.EPS