From fe6b232db357319b09cef85d90d44bf1f964028b Mon Sep 17 00:00:00 2001
From: "curious.jp" <48515264+curiousjp@users.noreply.github.com>
Date: Mon, 7 Oct 2024 07:30:07 +1100
Subject: [PATCH 1/2] only cleanup where prompt graph exists

---
 efficiency_nodes.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/efficiency_nodes.py b/efficiency_nodes.py
index 4da7490..09f3db5 100644
--- a/efficiency_nodes.py
+++ b/efficiency_nodes.py
@@ -134,7 +134,8 @@ def efficientloader(self, ckpt_name, vae_name, clip_skip, lora_name, lora_model_
                         ascore=None, prompt=None, my_unique_id=None, loader_type="regular"):
 
         # Clean globally stored objects
-        globals_cleanup(prompt)
+        if prompt:
+            globals_cleanup(prompt)
 
         # Create Empty Latent
         latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
@@ -722,7 +723,8 @@ def calculate_sigmas(model_sampling, scheduler_name: str, steps):
 
         # ---------------------------------------------------------------------------------------------------------------
         # Clean globally stored objects of non-existant nodes
-        globals_cleanup(prompt)
+        if prompt:
+            globals_cleanup(prompt)
 
         # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
         # If not XY Plotting

From 046fcc554e134bfa971a5f32b648a283b97efa52 Mon Sep 17 00:00:00 2001
From: "curious.jp" <48515264+curiousjp@users.noreply.github.com>
Date: Mon, 14 Oct 2024 19:27:37 +1100
Subject: [PATCH 2/2] added latent shape to cache key

---
 efficiency_nodes.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/efficiency_nodes.py b/efficiency_nodes.py
index 09f3db5..3d1f696 100644
--- a/efficiency_nodes.py
+++ b/efficiency_nodes.py
@@ -518,6 +518,7 @@ def calculate_sigmas(model_sampling, scheduler_name: str, steps):
                 # ------------------------------------------------------------------------------------------------------
                 # Store run parameters as strings. Load previous stored samples if all parameters match.
                 latent_image_hash = tensor_to_hash(latent_image["samples"])
+                latent_image_shape = latent_image["samples"].shape
                 positive_hash = tensor_to_hash(positive[0][0])
                 negative_hash = tensor_to_hash(negative[0][0])
                 refiner_positive_hash = tensor_to_hash(refiner_positive[0][0]) if refiner_positive is not None else None
@@ -528,7 +529,7 @@ def calculate_sigmas(model_sampling, scheduler_name: str, steps):
                                     else [original_model_str]
 
                 parameters = [model_identifier] + [seed, steps, cfg, sampler_name, scheduler, positive_hash, negative_hash,
-                                                  latent_image_hash, denoise, sampler_type, add_noise, start_at_step,
+                                                  latent_image_hash, latent_image_shape, denoise, sampler_type, add_noise, start_at_step,
                                                   end_at_step, return_with_leftover_noise, refiner_model, refiner_positive_hash,
                                                   refiner_negative_hash, rng_source, cfg_denoiser, add_seed_noise, m_seed, m_weight]