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from __future__ import annotations
from typing import TYPE_CHECKING, Union
from comfy_api.latest import io, ComfyExtension
import comfy.patcher_extension
import logging
import torch
import comfy.model_patcher
if TYPE_CHECKING:
from uuid import UUID
def _extract_tensor(data, output_channels):
"""Extract tensor from data, handling both single tensors and lists."""
if isinstance(data, list):
# LTX2 AV tensors: [video, audio]
return data[0][:, :output_channels], data[1][:, :output_channels]
return data[:, :output_channels], None
def easycache_forward_wrapper(executor, *args, **kwargs):
# get values from args
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, ax = _extract_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):
return executor(*args, **kwargs)
# prepare next x_prev
has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
next_x_prev = x
input_change = None
do_easycache = easycache.should_do_easycache(sigmas)
if do_easycache:
easycache.check_metadata(x)
# if there isn't a cache diff for current conds, we cannot skip this step
can_apply_cache_diff = easycache.can_apply_cache_diff(uuids)
# if first cond marked this step for skipping, skip it and use appropriate cached values
if easycache.skip_current_step and can_apply_cache_diff:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
result = easycache.apply_cache_diff(x, uuids)
if ax is not None:
result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
return [result, result_audio]
return result
if easycache.initial_step:
easycache.first_cond_uuid = uuids[0]
has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
easycache.initial_step = False
if has_first_cond_uuid:
if easycache.has_x_prev_subsampled():
input_change = (easycache.subsample(x, uuids, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
easycache.cumulative_change_rate += approx_output_change_rate
if easycache.cumulative_change_rate < easycache.reuse_threshold and can_apply_cache_diff:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
# other conds should also skip this step, and instead use their cached values
easycache.skip_current_step = True
result = easycache.apply_cache_diff(x, uuids)
if ax is not None:
result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
return [result, result_audio]
return result
else:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
easycache.cumulative_change_rate = 0.0
full_output: torch.Tensor = executor(*args, **kwargs)
output, audio_output = _extract_tensor(full_output, easycache.output_channels)
if has_first_cond_uuid and easycache.has_output_prev_norm():
output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
if easycache.verbose:
output_change_rate = output_change / easycache.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) / easycache.output_prev_norm
easycache.approx_output_change_rates.append(approx_output_change_rate.item())
if easycache.verbose:
logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
if input_change is not None:
easycache.relative_transformation_rate = output_change / input_change
if easycache.verbose:
logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
# TODO: allow cache_diff to be offloaded
easycache.update_cache_diff(output, next_x_prev, uuids)
if audio_output is not None:
easycache.update_cache_diff(audio_output, ax, uuids, is_audio=True)
if has_first_cond_uuid:
easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
easycache.output_prev_subsampled = easycache.subsample(output, uuids)
easycache.output_prev_norm = output.flatten().abs().mean()
if easycache.verbose:
logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
return full_output
def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
# get values from args
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)
x: torch.Tensor = args[0][:, :easycache.output_channels]
# prepare next x_prev
next_x_prev = x
input_change = None
do_easycache = easycache.should_do_easycache(timestep)
if do_easycache:
easycache.check_metadata(x)
if easycache.has_x_prev_subsampled():
if easycache.has_x_prev_subsampled():
input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
easycache.cumulative_change_rate += approx_output_change_rate
if easycache.cumulative_change_rate < easycache.reuse_threshold:
if easycache.verbose:
logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
# other conds should also skip this step, and instead use their cached values
easycache.skip_current_step = True
return easycache.apply_cache_diff(x)
else:
if easycache.verbose:
logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
easycache.cumulative_change_rate = 0.0
output: torch.Tensor = executor(*args, **kwargs)
if easycache.has_output_prev_norm():
output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
if easycache.verbose:
output_change_rate = output_change / easycache.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) / easycache.output_prev_norm
easycache.approx_output_change_rates.append(approx_output_change_rate.item())
if easycache.verbose:
logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
if input_change is not None:
easycache.relative_transformation_rate = output_change / input_change
if easycache.verbose:
logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}")
# TODO: allow cache_diff to be offloaded
easycache.update_cache_diff(output, next_x_prev)
easycache.x_prev_subsampled = easycache.subsample(next_x_prev)
easycache.output_prev_subsampled = easycache.subsample(output)
easycache.output_prev_norm = output.flatten().abs().mean()
if easycache.verbose:
logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
return output
def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs):
model_options = args[-1]
easycache: EasyCacheHolder = model_options["transformer_options"]["easycache"]
easycache.skip_current_step = False
# TODO: check if first_cond_uuid is active at this timestep; otherwise, EasyCache needs to be partially reset
return executor(*args, **kwargs)
def easycache_sample_wrapper(executor, *args, **kwargs):
"""
This OUTER_SAMPLE wrapper makes sure easycache is prepped for current run, and all memory usage is cleared at the end.
"""
try:
guider = executor.class_obj
orig_model_options = guider.model_options
guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
# clone and prepare timesteps
guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache']
logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
return executor(*args, **kwargs)
finally:
easycache = guider.model_options['transformer_options']['easycache']
output_change_rates = easycache.output_change_rates
approx_output_change_rates = easycache.approx_output_change_rates
if easycache.verbose:
logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
total_steps = len(args[3])-1
# catch division by zero for log statement; sucks to crash after all sampling is done
try:
speedup = total_steps/(total_steps-easycache.total_steps_skipped)
except ZeroDivisionError:
speedup = 1.0
logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({speedup:.2f}x speedup).")
easycache.reset()
guider.model_options = orig_model_options
class EasyCacheHolder:
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
self.end_percent = end_percent
self.subsample_factor = subsample_factor
self.offload_cache_diff = offload_cache_diff
self.verbose = verbose
# 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
self.initial_step = True
self.skip_current_step = False
# cache values
self.first_cond_uuid = None
self.x_prev_subsampled: torch.Tensor = None
self.output_prev_subsampled: torch.Tensor = None
self.output_prev_norm: torch.Tensor = None
self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
self.uuid_cache_diffs_audio: dict[UUID, torch.Tensor] = {}
self.output_change_rates = []
self.approx_output_change_rates = []
self.total_steps_skipped = 0
# how to deal with mismatched dims
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()
def should_do_easycache(self, timestep: float) -> bool:
return (timestep[0] <= self.start_t).item()
def has_x_prev_subsampled(self) -> bool:
return self.x_prev_subsampled is not None
def has_output_prev_subsampled(self) -> bool:
return self.output_prev_subsampled is not None
def has_output_prev_norm(self) -> bool:
return self.output_prev_norm 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 subsample(self, x: torch.Tensor, uuids: list[UUID], clone: bool = True) -> torch.Tensor:
batch_offset = x.shape[0] // len(uuids)
uuid_idx = uuids.index(self.first_cond_uuid)
if self.subsample_factor > 1:
to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ..., ::self.subsample_factor, ::self.subsample_factor]
if clone:
return to_return.clone()
return to_return
to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ...]
if clone:
return to_return.clone()
return to_return
def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
return all(uuid in self.uuid_cache_diffs for uuid in uuids)
def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
if self.first_cond_uuid in uuids and not is_audio:
self.total_steps_skipped += 1
cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
batch_offset = x.shape[0] // len(uuids)
for i, uuid in enumerate(uuids):
# slice out only what is relevant to this cond
batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
# if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
if x.shape[1:] != cache_diffs[uuid].shape[1:]:
if not self.allow_mismatch:
raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
slicing = []
skip_this_dim = True
for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
if skip_this_dim:
skip_this_dim = False
continue
if dim_u != dim_x:
if self.cut_from_start:
slicing.append(slice(dim_x-dim_u, None))
else:
slicing.append(slice(None, dim_u))
else:
slicing.append(slice(None))
batch_slice = batch_slice + slicing
x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
return x
def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
# if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
if output.shape[1:] != x.shape[1:]:
if not self.allow_mismatch:
raise ValueError(f"Output dims {output.shape} don't match x dims {x.shape} - this is no good")
slicing = []
skip_dim = True
for dim_o, dim_x in zip(output.shape, x.shape):
if not skip_dim and dim_o != dim_x:
if self.cut_from_start:
slicing.append(slice(dim_x-dim_o, None))
else:
slicing.append(slice(None, dim_o))
else:
slicing.append(slice(None))
skip_dim = False
x = x[tuple(slicing)]
diff = output - x
batch_offset = diff.shape[0] // len(uuids)
for i, uuid in enumerate(uuids):
cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
return self.first_cond_uuid in uuids
def check_metadata(self, x: torch.Tensor) -> bool:
metadata = (x.device, x.dtype, x.shape[1:])
if self.state_metadata is None:
self.state_metadata = metadata
return True
if metadata == self.state_metadata:
return True
logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
self.reset()
return False
def reset(self):
self.relative_transformation_rate = 0.0
self.cumulative_change_rate = 0.0
self.initial_step = True
self.skip_current_step = False
self.output_change_rates = []
self.first_cond_uuid = None
del self.x_prev_subsampled
self.x_prev_subsampled = None
del self.output_prev_subsampled
self.output_prev_subsampled = None
del self.output_prev_norm
self.output_prev_norm = None
del self.uuid_cache_diffs
self.uuid_cache_diffs = {}
del self.uuid_cache_diffs_audio
self.uuid_cache_diffs_audio = {}
self.total_steps_skipped = 0
self.state_metadata = None
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, output_channels=self.output_channels)
class EasyCacheNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="EasyCache",
display_name="EasyCache",
description="Native EasyCache implementation.",
category="advanced/debug",
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to add EasyCache to."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
],
outputs=[
io.Model.Output(tooltip="The model with EasyCache."),
],
)
@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, 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)
return io.NodeOutput(model)
class LazyCacheHolder:
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
self.end_percent = end_percent
self.subsample_factor = subsample_factor
self.offload_cache_diff = offload_cache_diff
self.verbose = verbose
# 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
self.initial_step = True
# cache values
self.x_prev_subsampled: torch.Tensor = None
self.output_prev_subsampled: torch.Tensor = None
self.output_prev_norm: torch.Tensor = None
self.cache_diff: torch.Tensor = None
self.output_change_rates = []
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
def is_past_end_timestep(self, timestep: float) -> bool:
return not (timestep[0] > self.end_t).item()
def should_do_easycache(self, timestep: float) -> bool:
return (timestep[0] <= self.start_t).item()
def has_x_prev_subsampled(self) -> bool:
return self.x_prev_subsampled is not None
def has_output_prev_subsampled(self) -> bool:
return self.output_prev_subsampled is not None
def has_output_prev_norm(self) -> bool:
return self.output_prev_norm 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 subsample(self, x: torch.Tensor, clone: bool = True) -> torch.Tensor:
if self.subsample_factor > 1:
to_return = x[..., ::self.subsample_factor, ::self.subsample_factor]
if clone:
return to_return.clone()
return to_return
if clone:
return x.clone()
return x
def apply_cache_diff(self, x: torch.Tensor):
self.total_steps_skipped += 1
return x + self.cache_diff.to(x.device)
def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor):
self.cache_diff = output - x
def check_metadata(self, x: torch.Tensor) -> bool:
metadata = (x.device, x.dtype, x.shape)
if self.state_metadata is None:
self.state_metadata = metadata
return True
if metadata == self.state_metadata:
return True
logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
self.reset()
return False
def reset(self):
self.relative_transformation_rate = 0.0
self.cumulative_change_rate = 0.0
self.initial_step = True
self.output_change_rates = []
self.approx_output_change_rates = []
del self.cache_diff
self.cache_diff = None
del self.x_prev_subsampled
self.x_prev_subsampled = None
del self.output_prev_subsampled
self.output_prev_subsampled = None
del self.output_prev_norm
self.output_prev_norm = None
self.total_steps_skipped = 0
self.state_metadata = None
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, output_channels=self.output_channels)
class LazyCacheNode(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="LazyCache",
display_name="LazyCache",
description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
category="advanced/debug",
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to add LazyCache to."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
],
outputs=[
io.Model.Output(tooltip="The model with LazyCache."),
],
)
@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, 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)
class EasyCacheExtension(ComfyExtension):
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
EasyCacheNode,
LazyCacheNode,
]
def comfy_entrypoint():
return EasyCacheExtension()
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