From 39ad3aa2c6a7e4345c828d1af8976d2a5010430d Mon Sep 17 00:00:00 2001
From: FizzleDorf <1fizzledorf@gmail.com>
Date: Mon, 18 Sep 2023 22:42:40 -0400
Subject: [PATCH] some more fixes, still trying to figure out sampler

---
 ScheduleFuncs.py  | 11 ++++++-----
 ScheduledNodes.py |  4 ++--
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/ScheduleFuncs.py b/ScheduleFuncs.py
index 89992d3..a5ab1a7 100644
--- a/ScheduleFuncs.py
+++ b/ScheduleFuncs.py
@@ -362,11 +362,12 @@ def interpolate_prompt_series(animation_prompts, max_frames, current_frame, pre_
 def BatchPoolAnimConditioning(cur_prompt_series, nxt_promt_series, weight_series, clip):
     c = []
     for i in range(len(cur_prompt_series)):
-            tokens = clip.tokenize(str(cur_prompt_series[i]))
-            cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
-            tokens = clip.tokenize(str(nxt_promt_series[i]))
-            cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
-            c.append(addWeighted([[cond_to, {"pooled_output": pooled_to}]], [[cond_from, {"pooled_output": pooled_from}]], weight_series[i]))
+        tokens = clip.tokenize(str(cur_prompt_series[i]))
+        cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
+        tokens = clip.tokenize(str(nxt_promt_series[i]))
+        cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True)
+        c.append(addWeighted([[cond_to, {"pooled_output": pooled_to}]], [[cond_from, {"pooled_output": pooled_from}]], weight_series[i]))
+        print(c)
     return c
 
 def SDXLencode(clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
diff --git a/ScheduledNodes.py b/ScheduledNodes.py
index 09796ce..cfc2aa6 100644
--- a/ScheduledNodes.py
+++ b/ScheduledNodes.py
@@ -1,6 +1,6 @@
 #These nodes were made using code from the Deforum extension for A1111 webui
 #You can find the project here: https://github.com/deforum-art/sd-webui-deforum
-
+import comfy
 import numexpr
 import torch
 import numpy as np
@@ -41,7 +41,7 @@ defaultValue="""0:(0),
 
 #This node parses the user's formatted prompt,
 #sequences the current prompt,next prompt, and 
-#conditioning strength, evalates expressions in 
+#conditioning strength, evaluates expressions in
 #the prompts, and then returns either current, 
 #next or averaged conditioning.
 class PromptSchedule: