Fixed math to make threshold work as expected, refactored code to use EasyCacheHolder instead of a dict wrapped by object

comfy_extras/nodes_easycache.py

@@ -4,73 +4,135 @@ import logging
 import torch
 import comfy.model_patcher
 
+def easycache_forward_wrapper(executor, *args, **kwargs):
+    # get values from args
+    x: torch.Tensor = args[0]
+    timestep: torch.Tensor = args[1]
+    transformer_options: dict[str] = args[-1]
+    # x: torch.Tensor = args[0]
+    # timestep: torch.Tensor = args[4]
+    # transformer_options: dict[str] = args[-2]
+    easycache: EasyCacheHolder = transformer_options["easycache"]
+    if easycache.is_past_end_timestep(timestep):
+        return executor(*args, **kwargs)
+    # prepare next x_prev
+    next_x_prev = x.clone()
+    do_easycache = easycache.should_do_easycache(timestep)
+    logging.info(f"easycache_wrapper: do_easycache: {do_easycache}")
+    output_prev_norm = None
+    input_change = None
+    if do_easycache:
+        if easycache.has_x_prev():
+            input_change = (x - easycache.x_prev).flatten().abs().mean()
+        if easycache.has_output_prev() and easycache.has_relative_transformation_rate():
+            output_prev_norm = easycache.output_prev.flatten().abs().mean()
+            approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / output_prev_norm
+            easycache.cumulative_change_rate += approx_output_change_rate
+            if easycache.cumulative_change_rate < easycache.reuse_threshold:
+                logging.info(f"easycache_wrapper: skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                return x + easycache.cache_diff
+            else:
+                easycache.cumulative_change_rate = 0.0
+                logging.info(f"easycache_wrapper: NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                logging.info(f"easycache_wrapper: approx_output_change_rate: {approx_output_change_rate}")
+
+    output: torch.Tensor = executor(*args, **kwargs)
+    if easycache.has_output_prev():
+        output_change = (output - easycache.output_prev).flatten().abs().mean()
+        if output_prev_norm is None:
+            output_prev_norm = easycache.output_prev.flatten().abs().mean()
+        output_change_rate = output_change / output_prev_norm
+        easycache.output_change_rates.append(output_change_rate.item())
+        if easycache.has_relative_transformation_rate():
+            approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / output_prev_norm
+            easycache.approx_output_change_rates.append(approx_output_change_rate.item())
+            logging.info(f"easycache_wrapper: approx_output_change_rate: {approx_output_change_rate}")
+        if input_change is not None:
+            easycache.relative_transformation_rate = output_change / input_change
+        logging.info(f"easycache_wrapper: output_change_rate: {output_change_rate}")
+    easycache.cache_diff = output - next_x_prev
+    easycache.x_prev = next_x_prev
+    easycache.output_prev = output.clone()
+    return output
+
 def easycache_sample_wrapper(executor, *args, **kwargs):
     try:
         guider = executor.class_obj
         orig_model_options = guider.model_options
         guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
-        if "easycache" in orig_model_options["transformer_options"]:
+        # clone and prepare timesteps
-            guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone()
+        guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
-            guider.model_options["transformer_options"]["easycache"].dict["start_timestep"] = guider.model_patcher.model.model_sampling.percent_to_sigma(guider.model_options["transformer_options"]["easycache"].dict["start_percent"])
-            guider.model_options["transformer_options"]["easycache"].dict["end_timestep"] = guider.model_patcher.model.model_sampling.percent_to_sigma(guider.model_options["transformer_options"]["easycache"].dict["end_percent"])
         return executor(*args, **kwargs)
     finally:
+        output_change_rates = guider.model_options['transformer_options']['easycache'].output_change_rates
+        approx_output_change_rates = guider.model_options['transformer_options']['easycache'].approx_output_change_rates
+        logging.info(f"easycache_sample_wrapper: output_change_rates {len(output_change_rates)}: {output_change_rates}")
+        logging.info(f"easycache_sample_wrapper: approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
+        guider.model_options["transformer_options"]["easycache"].reset()
         guider.model_options = orig_model_options
 
-def easycache_forward_wrapper(executor, *args, **kwargs):
-    x: torch.Tensor = args[0]
-    timestep: torch.Tensor = args[1]
-    transformer_options = args[-1]
-    do_easycache = timestep < transformer_options["easycache"].dict["start_timestep"] and timestep > transformer_options["easycache"].dict["end_timestep"]
-    logging.info(f"easycache_wrapper: do_easycache: {do_easycache}")
-    x_prev = None
-    input_change = None
-    # input_data = x.flatten().abs().mean()
-    if do_easycache and "easycache" in transformer_options:
-        if "x_prev" in transformer_options["easycache"].dict:
-            x_prev = transformer_options["easycache"].dict["x_prev"]
-        else:
-            transformer_options["easycache"].dict["x_prev"] = x.clone()
-    if x_prev is not None:
-        input_change = (x_prev - x).flatten().abs().mean()
-    if do_easycache and transformer_options["easycache"].dict.get("change_rate", None) is not None:
-        change_rate = transformer_options["easycache"].dict["change_rate"]
-        output_prev = transformer_options["easycache"].dict["output_prev"]
-        pred_change = change_rate * (input_change / output_prev.flatten().abs().mean())
-        accumulated_change = transformer_options["easycache"].dict["accumulated_change"] + pred_change
-        if transformer_options["easycache"].dict["reuse_threshold"] <= accumulated_change:
-            logging.info(f"easycache_wrapper: skipping step; accumulated_change: {accumulated_change}, reuse_threshold: {transformer_options['easycache'].dict['reuse_threshold']}")
-            transformer_options["easycache"].dict["accumulated_change"] = 0.0
-            return x + transformer_options["easycache"].dict["cache_diff"]
-        else:
-            transformer_options["easycache"].dict["accumulated_change"] = accumulated_change
-            logging.info(f"easycache_wrapper: NOT skipping step; accumulated_change: {accumulated_change}, reuse_threshold: {transformer_options['easycache'].dict['reuse_threshold']}")
-        logging.info(f"easycache_wrapper pred_change: {pred_change}")
-    output: torch.Tensor = executor(*args, **kwargs)
-    if x_prev is not None:
-        # output_data = output.flatten().abs().mean()
-        output_prev = transformer_options["easycache"].dict["output_prev"]
-        output_change = (output_prev - output).flatten().abs().mean()
-        k = output_change / input_change
-        transformer_options["easycache"].dict["change_rate"] = k
-        logging.info(f"easycache_wrapper: {input_change} {output_change} {k}")
-    if do_easycache and "easycache" in transformer_options:
-        transformer_options["easycache"].dict["output_prev"] = output.clone()
-        transformer_options["easycache"].dict["cache_diff"] = output - x
-    if not do_easycache:
-        transformer_options["easycache"].dict["accumulated_change"] = 0.0
-        transformer_options["easycache"].dict["change_rate"] = None
-        transformer_options["easycache"].dict["output_prev"] = None
-        transformer_options["easycache"].dict["cache_diff"] = None
-    return output
 
+class EasyCacheHolder:
+    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float):
+        self.reuse_threshold = reuse_threshold
+        self.start_percent = start_percent
+        self.end_percent = end_percent
+        # timestep values
+        self.start_t = 0.0
+        self.end_t = 0.0
+        # control values
+        self.relative_transformation_rate: float = None
+        self.cumulative_change_rate = 0.0
+        # cache values
+        self.x_prev = None
+        self.output_prev = None
+        self.cache_diff = None
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
 
-class EasyCacheStore:
+    def is_past_end_timestep(self, timestep: float) -> bool:
-    def __init__(self, dict: dict):
+        return not (timestep > self.end_t).item()
-        self.dict = dict
+
+    def should_do_easycache(self, timestep: float) -> bool:
+        return (timestep <= self.start_t).item()
+
+    def has_x_prev(self) -> bool:
+        return self.x_prev is not None
+
+    def has_output_prev(self) -> bool:
+        return self.output_prev is not None
+
+    def has_cache_diff(self) -> bool:
+        return self.cache_diff is not None
+
+    def has_relative_transformation_rate(self) -> bool:
+        return self.relative_transformation_rate is not None
+
+    def prepare_timesteps(self, model_sampling):
+        self.start_t = model_sampling.percent_to_sigma(self.start_percent)
+        self.end_t = model_sampling.percent_to_sigma(self.end_percent)
+        return self
+
+    def apply_cache(self):
+        ...
+
+    def accumulate_change(self):
+        ...
+
+    def reset(self):
+        self.relative_transformation_rate = 0.0
+        self.cumulative_change_rate = 0.0
+        self.output_change_rates = []
+        del self.x_prev
+        self.x_prev = None
+        del self.output_prev
+        self.output_prev = None
+        del self.cache_diff
+        self.cache_diff = None
+        return self
 
     def clone(self):
-        return EasyCacheStore(self.dict.copy())
+        return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent)
 
 
 class EasyCacheNode(io.ComfyNode):
@@ -83,7 +145,7 @@ class EasyCacheNode(io.ComfyNode):
             category="advanced/debug/model",
             inputs=[
                 io.Model.Input("model", tooltip="The model to add EasyCache to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.0, max=100.0, step=0.01, tooltip="The threshold for reusing cached steps."),
+                io.Float.Input("reuse_threshold", min=0.0, default=0.0, max=1.0, step=0.01, tooltip="The threshold for reusing cached steps."),
                 io.Float.Input("start_percent", min=0.0, default=0.0, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."),
                 io.Float.Input("end_percent", min=0.0, default=1.0, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."),
             ],
@@ -95,13 +157,7 @@ class EasyCacheNode(io.ComfyNode):
     @classmethod
     def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float) -> io.NodeOutput:
         model = model.clone()
-        easycache_dict = {
+        model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent)
-            "reuse_threshold": reuse_threshold,
-            "start_percent": start_percent,
-            "end_percent": end_percent,
-            "accumulated_change": 0.0,
-        }
-        model.model_options["transformer_options"]["easycache"] = EasyCacheStore(easycache_dict)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper)
         return io.NodeOutput(model)