diff --git a/ldm/dream/conditioning.py b/ldm/dream/conditioning.py
index dfa1089..e4908ad 100644
--- a/ldm/dream/conditioning.py
+++ b/ldm/dream/conditioning.py
@@ -12,8 +12,8 @@ log_tokenization()              print out colour-coded tokens and warn if trunca
 import re
 import torch
 
-def get_uc_and_c(prompt, model, log_tokens=False, skip_normalize=False):
-    uc = model.get_learned_conditioning([''])
+def get_uc_and_c(prompt, model, log_tokens=False, skip_normalize=False, negative_prompt=''):
+    uc = model.get_learned_conditioning([negative_prompt])
 
     # get weighted sub-prompts
     weighted_subprompts = split_weighted_subprompts(
diff --git a/ldm/generate.py b/ldm/generate.py
index 8f67403..d88ce2d 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -205,6 +205,7 @@ class Generate:
             init_mask      =    None,
             fit            =    False,
             strength       =    None,
+            init_img_is_path =  True,
             # these are specific to GFPGAN/ESRGAN
             gfpgan_strength=    0,
             save_original  =    False,
@@ -303,11 +304,15 @@ class Generate:
             uc, c = get_uc_and_c(
                 prompt, model=self.model,
                 skip_normalize=skip_normalize,
-                log_tokens=self.log_tokenization
+                log_tokens=self.log_tokenization,
+                negative_prompt=(args['negative_prompt'] if 'negative_prompt' in args else '')
             )
 
-            (init_image,mask_image) = self._make_images(init_img,init_mask, width, height, fit)
-            
+            if init_img_is_path:
+                (init_image,mask_image) = self._make_images(init_img,init_mask, width, height, fit)
+            else:
+                (init_image,mask_image) = (init_img, init_mask)
+
             if (init_image is not None) and (mask_image is not None):
                 generator = self._make_inpaint()
             elif init_image is not None: