diff --git a/comfy/ldm/models/diffusion/ddim.py b/comfy/ldm/models/diffusion/ddim.py
index d5649089a..108fce1cf 100644
--- a/comfy/ldm/models/diffusion/ddim.py
+++ b/comfy/ldm/models/diffusion/ddim.py
@@ -180,6 +180,12 @@ class DDIMSampler(object):
                                                     )
         return samples, intermediates
 
+    def q_sample(self, x_start, t, noise=None):
+        if noise is None:
+            noise = torch.randn_like(x_start)
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
     @torch.no_grad()
     def ddim_sampling(self, cond, shape,
                       x_T=None, ddim_use_original_steps=False,
@@ -214,7 +220,7 @@ class DDIMSampler(object):
 
             if mask is not None:
                 assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img_orig = self.q_sample(x0, ts)  # TODO: deterministic forward pass?
                 img = img_orig * mask + (1. - mask) * img
 
             if ucg_schedule is not None:
diff --git a/comfy/sd.py b/comfy/sd.py
index 3f36b8c03..dbfbdbe38 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -285,6 +285,11 @@ def model_lora_keys(model, key_map={}):
         if key_in:
             counter += 1
 
+    for k in sdk:
+        if k.startswith("diffusion_model.") and k.endswith(".weight"):
+            key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+            key_map["lora_unet_{}".format(key_lora)] = k
+
     return key_map