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EasyCache: Fix for mismatch in input/output channels with some models (#10788)

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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
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Jukka Seppänen 2025-11-18 17:00:21 +02:00 committed by GitHub
parent 048f49adbd
commit e1ab6bb394
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GPG Key ID: B5690EEEBB952194
1 changed files with 12 additions and 10 deletions
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22
comfy_extras/nodes_easycache.py
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@ -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)