diff --git a/py/capture.py b/py/capture.py
index fb01d30..3eec2d6 100644
--- a/py/capture.py
+++ b/py/capture.py
@@ -7,6 +7,7 @@ from .defs.meta import MetaField
 
 from nodes import NODE_CLASS_MAPPINGS
 from execution import get_input_data
+from comfy_execution.graph import DynamicPrompt
 
 
 class Capture:
@@ -15,14 +16,14 @@ class Capture:
         inputs = {}
         prompt = hook.current_prompt
         extra_data = hook.current_extra_data
-        outputs = hook.prompt_executer.outputs
+        outputs = hook.prompt_executer.caches.outputs
 
         for node_id, obj in prompt.items():
             class_type = obj["class_type"]
             obj_class = NODE_CLASS_MAPPINGS[class_type]
             node_inputs = prompt[node_id]["inputs"]
             input_data = get_input_data(
-                node_inputs, obj_class, node_id, outputs, prompt, extra_data
+                node_inputs, obj_class, node_id, outputs, DynamicPrompt(prompt), extra_data
             )
             for node_class, metas in CAPTURE_FIELD_LIST.items():
                 if class_type == node_class:
@@ -54,7 +55,7 @@ class Capture:
                             continue
 
                         field_name = field_data["field_name"]
-                        value = input_data.get(field_name)
+                        value = input_data[0].get(field_name)
                         if value is not None:
                             format = field_data.get("format")
                             v = value
diff --git a/py/defs/ext/efficiency_nodes.py b/py/defs/ext/efficiency_nodes.py
index bf15b86..d341ee7 100644
--- a/py/defs/ext/efficiency_nodes.py
+++ b/py/defs/ext/efficiency_nodes.py
@@ -17,22 +17,22 @@ def get_lora_model_hash_stack(node_id, obj, prompt, extra_data, outputs, input_d
 def get_lora_strength_model_stack(
     node_id, obj, prompt, extra_data, outputs, input_data
 ):
-    if input_data["input_mode"][0] == "advanced":
+    if input_data[0]["input_mode"][0] == "advanced":
         return get_lora_data_stack(input_data, "model_str")
     return get_lora_data_stack(input_data, "lora_wt")
 
 
 def get_lora_strength_clip_stack(node_id, obj, prompt, extra_data, outputs, input_data):
-    if input_data["input_mode"][0] == "advanced":
+    if input_data[0]["input_mode"][0] == "advanced":
         return get_lora_data_stack(input_data, "clip_str")
     return get_lora_data_stack(input_data, "lora_wt")
 
 
 def get_lora_data_stack(input_data, attribute):
-    lora_count = input_data["lora_count"][0]
+    lora_count = input_data[0]["lora_count"][0]
     return [
         v[0]
-        for k, v in input_data.items()
+        for k, v in input_data[0].items()
         if k.startswith(attribute) and v[0] != "None"
     ][:lora_count]
 
diff --git a/py/defs/ext/rgthree.py b/py/defs/ext/rgthree.py
index aad025f..887ecd2 100644
--- a/py/defs/ext/rgthree.py
+++ b/py/defs/ext/rgthree.py
@@ -21,7 +21,7 @@ def get_lora_strength(node_id, obj, prompt, extra_data, outputs, input_data):
 def get_lora_data(input_data, attribute):
     return [
         v[0][attribute]
-        for k, v in input_data.items()
+        for k, v in input_data[0].items()
         if k.startswith("lora_") and v[0]["on"]
     ]
 
@@ -44,7 +44,7 @@ def get_lora_strength_stack(node_id, obj, prompt, extra_data, outputs, input_dat
 def get_lora_data_stack(input_data, attribute):
     return [
         v[0]
-        for k, v in input_data.items()
+        for k, v in input_data[0].items()
         if k.startswith(attribute + "_") and v[0] != "None"
     ]
 
diff --git a/py/defs/formatters.py b/py/defs/formatters.py
index 7c68d66..d885f89 100644
--- a/py/defs/formatters.py
+++ b/py/defs/formatters.py
@@ -38,12 +38,12 @@ def convert_skip_clip(stop_at_clip_layer, input_data):
 
 
 def get_scaled_width(scaled_by, input_data):
-    samples = input_data["samples"][0]["samples"]
+    samples = input_data[0]["samples"][0]["samples"]
     return round(samples.shape[3] * scaled_by * 8)
 
 
 def get_scaled_height(scaled_by, input_data):
-    samples = input_data["samples"][0]["samples"]
+    samples = input_data[0]["samples"][0]["samples"]
     return round(samples.shape[2] * scaled_by * 8)
 
 
@@ -64,7 +64,7 @@ def extract_embedding_hashes(text, input_data):
 
 
 def _extract_embedding_names(text, input_data):
-    clip = input_data["clip"][0]
+    clip = input_data[0]["clip"][0]
     tokenizer = clip.tokenizer
     if isinstance(tokenizer, SD1Tokenizer):
         tokenizer = tokenizer.clip_l