From e1ab6bb394b82fa654d5bc84043f97479d12f84c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Tue, 18 Nov 2025 17:00:21 +0200 Subject: [PATCH] EasyCache: Fix for mismatch in input/output channels with some models (#10788) Slices model input with output channels so the caching tracks only the noise channels, resolves channel mismatch with models like WanVideo I2V Also fix for slicing deprecation in pytorch 2.9 --- comfy_extras/nodes_easycache.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py index 1359e2f99..11b23ffdb 100644 --- a/comfy_extras/nodes_easycache.py +++ b/comfy_extras/nodes_easycache.py @@ -11,13 +11,13 @@ if TYPE_CHECKING: def easycache_forward_wrapper(executor, *args, **kwargs): # get values from args - x: torch.Tensor = args[0] transformer_options: dict[str] = args[-1] if not isinstance(transformer_options, dict): transformer_options = kwargs.get("transformer_options") if not transformer_options: transformer_options = args[-2] easycache: EasyCacheHolder = transformer_options["easycache"] + x: torch.Tensor = args[0][:, :easycache.output_channels] sigmas = transformer_options["sigmas"] uuids = transformer_options["uuids"] if sigmas is not None and easycache.is_past_end_timestep(sigmas): @@ -82,13 +82,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs): def lazycache_predict_noise_wrapper(executor, *args, **kwargs): # get values from args - x: torch.Tensor = args[0] timestep: float = args[1] model_options: dict[str] = args[2] easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"] if easycache.is_past_end_timestep(timestep): return executor(*args, **kwargs) # prepare next x_prev + x: torch.Tensor = args[0][:, :easycache.output_channels] next_x_prev = x input_change = None do_easycache = easycache.should_do_easycache(timestep) @@ -173,7 +173,7 @@ def easycache_sample_wrapper(executor, *args, **kwargs): class EasyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None): self.name = "EasyCache" self.reuse_threshold = reuse_threshold self.start_percent = start_percent @@ -202,6 +202,7 @@ class EasyCacheHolder: self.allow_mismatch = True self.cut_from_start = True self.state_metadata = None + self.output_channels = output_channels def is_past_end_timestep(self, timestep: float) -> bool: return not (timestep[0] > self.end_t).item() @@ -264,7 +265,7 @@ class EasyCacheHolder: else: slicing.append(slice(None)) batch_slice = batch_slice + slicing - x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device) + x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device) return x def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]): @@ -283,7 +284,7 @@ class EasyCacheHolder: else: slicing.append(slice(None)) skip_dim = False - x = x[slicing] + x = x[tuple(slicing)] diff = output - x batch_offset = diff.shape[0] // len(uuids) for i, uuid in enumerate(uuids): @@ -323,7 +324,7 @@ class EasyCacheHolder: return self def clone(self): - return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels) class EasyCacheNode(io.ComfyNode): @@ -350,7 +351,7 @@ class EasyCacheNode(io.ComfyNode): @classmethod def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: model = model.clone() - model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper) @@ -358,7 +359,7 @@ class EasyCacheNode(io.ComfyNode): class LazyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None): self.name = "LazyCache" self.reuse_threshold = reuse_threshold self.start_percent = start_percent @@ -382,6 +383,7 @@ class LazyCacheHolder: self.approx_output_change_rates = [] self.total_steps_skipped = 0 self.state_metadata = None + self.output_channels = output_channels def has_cache_diff(self) -> bool: return self.cache_diff is not None @@ -456,7 +458,7 @@ class LazyCacheHolder: return self def clone(self): - return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels) class LazyCacheNode(io.ComfyNode): @classmethod @@ -482,7 +484,7 @@ class LazyCacheNode(io.ComfyNode): @classmethod def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: model = model.clone() - model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper) return io.NodeOutput(model)