diff --git a/modules/processing.py b/modules/processing.py
index 68a1912c3..6d51d6d1d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -231,6 +231,7 @@ class StableDiffusionProcessing:
         self.hdr_maximize = hdr_maximize
         self.hdr_max_center = hdr_max_center
         self.hdr_max_boundry = hdr_max_boundry
+        self.scheduled_prompt: bool = False
         self.prompt_embeds = []
         self.positive_pooleds = []
         self.negative_embeds = []
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 25f6fe646..66c607fe9 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -89,13 +89,15 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
         if kwargs.get('latents', None) is None:
             return kwargs
         kwargs = correction_callback(p, timestep, kwargs)
-        try:
-            kwargs["prompt_embeds"] = p.prompt_embeds[step + 1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
-                kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
-            kwargs["negative_prompt_embeds"] = p.negative_embeds[step + 1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
-                kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
-        except:
-            pass
+        if p.scheduled_prompt:
+            try:
+                i = (step + 1) % len(p.prompt_embeds)
+                kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
+                      kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
+                kwargs["negative_prompt_embeds"] = p.negative_embeds[i][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
+                      kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
+            except Exception as e:
+              shared.log.debug(f"Callback: {e}")
         shared.state.current_latent = kwargs['latents']
         if shared.cmd_opts.profile and shared.profiler is not None:
             shared.profiler.step()
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 16fab82d3..ae5acb8f6 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -58,33 +58,39 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
         debug(f'Prompt: expand={prompt}')
         return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
 
-def get_prompt_schedule(prompt, steps):
+def get_prompt_schedule(p, prompt, steps):
     temp = []
     schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
     for chunk in schedule:
         for s in range(steps):
             if len(temp) < s + 1 <= chunk[0]:
                 temp.append(chunk[1])
-    return temp
+    return temp, len(schedule) > 1
 
 def encode_prompts(pipe, p,  prompts: list, negative_prompts: list, steps: int, step: int = 1, clip_skip: typing.Optional[int] = None):
     if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion':
         shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
         return None, None, None, None
     else:
-        positive_schedule = get_prompt_schedule(prompts[0], steps)
-        negative_schedule = get_prompt_schedule(negative_prompts[0], steps)
+        positive_schedule, scheduled = get_prompt_schedule(p, prompts[0], steps)
+        negative_schedule, neg_scheduled = get_prompt_schedule(p, negative_prompts[0], steps)
+        p.scheduled_prompt = scheduled or neg_scheduled
 
         p.prompt_embeds = []
         p.positive_pooleds = []
         p.negative_embeds = []
         p.negative_pooleds = []
 
+        cache = {}
         for i in range(len(positive_schedule)):
-            prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
-                                                                                                          positive_schedule[i],
-                                                                                                          negative_schedule[i],
-                                                                                                          clip_skip)
+            cached = cache.get(positive_schedule[i]+negative_schedule[i], None)
+            if cached is not None:
+              prompt_embed, positive_pooled, negative_embed, negative_pooled = cached
+            else:
+              prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
+                                                                                                            positive_schedule[i],
+                                                                                                            negative_schedule[i],
+                                                                                                            clip_skip)
             if prompt_embed is not None:
                 p.prompt_embeds.append(torch.cat([prompt_embed]*len(prompts), dim=0))
             if negative_embed is not None: