mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-10 11:35:40 +00:00
fc247150fe
* Attempting a universal implementation of EasyCache, starting with flux as test; I screwed up the math a bit, but when I set it just right it works. * Fixed math to make threshold work as expected, refactored code to use EasyCacheHolder instead of a dict wrapped by object * Use sigmas from transformer_options instead of timesteps to be compatible with a greater amount of models, make end_percent work * Make log statement when not skipping useful, preparing for per-cond caching * Added DIFFUSION_MODEL wrapper around forward function for wan model * Add subsampling for heuristic inputs * Add subsampling to output_prev (output_prev_subsampled now) * Properly consider conds in EasyCache logic * Created SuperEasyCache to test what happens if caching and reuse is moved outside the scope of conds, added PREDICT_NOISE wrapper to facilitate this test * Change max reuse_threshold to 3.0 * Mark EasyCache/SuperEasyCache as experimental (beta) * Make Lumina2 compatible with EasyCache * Add EasyCache support for Qwen Image * Fix missing comma, curse you Cursor * Add EasyCache support to AceStep * Add EasyCache support to Chroma * Added EasyCache support to Cosmos Predict t2i * Make EasyCache not crash with Cosmos Predict ImagToVideo latents, but does not work well at all * Add EasyCache support to hidream * Added EasyCache support to hunyuan video * Added EasyCache support to hunyuan3d * Added EasyCache support to LTXV (not very good, but does not crash) * Implemented EasyCache for aura_flow * Renamed SuperEasyCache to LazyCache, hardcoded subsample_factor to 8 on nodes * Eatra logging when verbose is true for EasyCache
460 lines
23 KiB
Python
460 lines
23 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Union
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from comfy_api.latest import io, ComfyExtension
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import comfy.patcher_extension
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import logging
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import torch
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import comfy.model_patcher
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if TYPE_CHECKING:
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from uuid import UUID
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def easycache_forward_wrapper(executor, *args, **kwargs):
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# get values from args
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x: torch.Tensor = args[0]
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transformer_options: dict[str] = args[-1]
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if not isinstance(transformer_options, dict):
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transformer_options = kwargs.get("transformer_options")
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if not transformer_options:
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transformer_options = args[-2]
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easycache: EasyCacheHolder = transformer_options["easycache"]
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sigmas = transformer_options["sigmas"]
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uuids = transformer_options["uuids"]
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if sigmas is not None and easycache.is_past_end_timestep(sigmas):
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return executor(*args, **kwargs)
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# prepare next x_prev
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has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
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next_x_prev = x
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input_change = None
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do_easycache = easycache.should_do_easycache(sigmas)
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if do_easycache:
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# if first cond marked this step for skipping, skip it and use appropriate cached values
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if easycache.skip_current_step:
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
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return easycache.apply_cache_diff(x, uuids)
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if easycache.initial_step:
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easycache.first_cond_uuid = uuids[0]
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has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
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easycache.initial_step = False
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if has_first_cond_uuid:
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if easycache.has_x_prev_subsampled():
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input_change = (easycache.subsample(x, uuids, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
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if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
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approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
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easycache.cumulative_change_rate += approx_output_change_rate
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if easycache.cumulative_change_rate < easycache.reuse_threshold:
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
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# other conds should also skip this step, and instead use their cached values
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easycache.skip_current_step = True
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return easycache.apply_cache_diff(x, uuids)
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else:
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
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easycache.cumulative_change_rate = 0.0
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output: torch.Tensor = executor(*args, **kwargs)
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if has_first_cond_uuid and easycache.has_output_prev_norm():
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output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
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if easycache.verbose:
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output_change_rate = output_change / easycache.output_prev_norm
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easycache.output_change_rates.append(output_change_rate.item())
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if easycache.has_relative_transformation_rate():
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approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
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easycache.approx_output_change_rates.append(approx_output_change_rate.item())
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
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if input_change is not None:
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easycache.relative_transformation_rate = output_change / input_change
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
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# TODO: allow cache_diff to be offloaded
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easycache.update_cache_diff(output, next_x_prev, uuids)
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if has_first_cond_uuid:
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easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
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easycache.output_prev_subsampled = easycache.subsample(output, uuids)
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easycache.output_prev_norm = output.flatten().abs().mean()
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if easycache.verbose:
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logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
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return output
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def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
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# get values from args
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x: torch.Tensor = args[0]
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timestep: float = args[1]
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model_options: dict[str] = args[2]
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easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
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if easycache.is_past_end_timestep(timestep):
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return executor(*args, **kwargs)
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# prepare next x_prev
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next_x_prev = x
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input_change = None
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do_easycache = easycache.should_do_easycache(timestep)
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if do_easycache:
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if easycache.has_x_prev_subsampled():
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if easycache.has_x_prev_subsampled():
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input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
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if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
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approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
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easycache.cumulative_change_rate += approx_output_change_rate
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if easycache.cumulative_change_rate < easycache.reuse_threshold:
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if easycache.verbose:
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logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
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# other conds should also skip this step, and instead use their cached values
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easycache.skip_current_step = True
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return easycache.apply_cache_diff(x)
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else:
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if easycache.verbose:
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logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
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easycache.cumulative_change_rate = 0.0
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output: torch.Tensor = executor(*args, **kwargs)
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if easycache.has_output_prev_norm():
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output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
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if easycache.verbose:
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output_change_rate = output_change / easycache.output_prev_norm
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easycache.output_change_rates.append(output_change_rate.item())
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if easycache.has_relative_transformation_rate():
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approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
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easycache.approx_output_change_rates.append(approx_output_change_rate.item())
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if easycache.verbose:
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logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
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if input_change is not None:
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easycache.relative_transformation_rate = output_change / input_change
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if easycache.verbose:
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logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}")
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# TODO: allow cache_diff to be offloaded
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easycache.update_cache_diff(output, next_x_prev)
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easycache.x_prev_subsampled = easycache.subsample(next_x_prev)
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easycache.output_prev_subsampled = easycache.subsample(output)
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easycache.output_prev_norm = output.flatten().abs().mean()
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if easycache.verbose:
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logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
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return output
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def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs):
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model_options = args[-1]
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easycache: EasyCacheHolder = model_options["transformer_options"]["easycache"]
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easycache.skip_current_step = False
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# TODO: check if first_cond_uuid is active at this timestep; otherwise, EasyCache needs to be partially reset
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return executor(*args, **kwargs)
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def easycache_sample_wrapper(executor, *args, **kwargs):
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"""
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This OUTER_SAMPLE wrapper makes sure easycache is prepped for current run, and all memory usage is cleared at the end.
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"""
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try:
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guider = executor.class_obj
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orig_model_options = guider.model_options
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guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
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# clone and prepare timesteps
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guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
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easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache']
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logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
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return executor(*args, **kwargs)
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finally:
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easycache = guider.model_options['transformer_options']['easycache']
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output_change_rates = easycache.output_change_rates
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approx_output_change_rates = easycache.approx_output_change_rates
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if easycache.verbose:
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logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
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logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
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total_steps = len(args[3])-1
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logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({total_steps/(total_steps-easycache.total_steps_skipped):.2f}x speedup).")
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easycache.reset()
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guider.model_options = orig_model_options
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class EasyCacheHolder:
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def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
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self.name = "EasyCache"
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self.reuse_threshold = reuse_threshold
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self.start_percent = start_percent
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self.end_percent = end_percent
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self.subsample_factor = subsample_factor
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self.offload_cache_diff = offload_cache_diff
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self.verbose = verbose
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# timestep values
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self.start_t = 0.0
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self.end_t = 0.0
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# control values
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self.relative_transformation_rate: float = None
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self.cumulative_change_rate = 0.0
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self.initial_step = True
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self.skip_current_step = False
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# cache values
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self.first_cond_uuid = None
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self.x_prev_subsampled: torch.Tensor = None
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self.output_prev_subsampled: torch.Tensor = None
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self.output_prev_norm: torch.Tensor = None
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self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
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self.output_change_rates = []
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self.approx_output_change_rates = []
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self.total_steps_skipped = 0
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# how to deal with mismatched dims
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self.allow_mismatch = True
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self.cut_from_start = True
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def is_past_end_timestep(self, timestep: float) -> bool:
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return not (timestep[0] > self.end_t).item()
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def should_do_easycache(self, timestep: float) -> bool:
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return (timestep[0] <= self.start_t).item()
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def has_x_prev_subsampled(self) -> bool:
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return self.x_prev_subsampled is not None
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def has_output_prev_subsampled(self) -> bool:
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return self.output_prev_subsampled is not None
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def has_output_prev_norm(self) -> bool:
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return self.output_prev_norm is not None
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def has_relative_transformation_rate(self) -> bool:
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return self.relative_transformation_rate is not None
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def prepare_timesteps(self, model_sampling):
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self.start_t = model_sampling.percent_to_sigma(self.start_percent)
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self.end_t = model_sampling.percent_to_sigma(self.end_percent)
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return self
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def subsample(self, x: torch.Tensor, uuids: list[UUID], clone: bool = True) -> torch.Tensor:
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batch_offset = x.shape[0] // len(uuids)
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uuid_idx = uuids.index(self.first_cond_uuid)
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if self.subsample_factor > 1:
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to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ..., ::self.subsample_factor, ::self.subsample_factor]
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if clone:
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return to_return.clone()
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return to_return
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to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ...]
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if clone:
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return to_return.clone()
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return to_return
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def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]):
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if self.first_cond_uuid in uuids:
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self.total_steps_skipped += 1
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batch_offset = x.shape[0] // len(uuids)
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for i, uuid in enumerate(uuids):
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# if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
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if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]:
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if not self.allow_mismatch:
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raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
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slicing = []
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skip_this_dim = True
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for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape):
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if skip_this_dim:
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skip_this_dim = False
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continue
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if dim_u != dim_x:
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if self.cut_from_start:
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slicing.append(slice(dim_x-dim_u, None))
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else:
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slicing.append(slice(None, dim_u))
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else:
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slicing.append(slice(None))
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slicing = [slice(i*batch_offset,(i+1)*batch_offset)] + slicing
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x = x[slicing]
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x += self.uuid_cache_diffs[uuid].to(x.device)
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return x
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def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
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# if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
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if output.shape[1:] != x.shape[1:]:
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if not self.allow_mismatch:
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raise ValueError(f"Output dims {output.shape} don't match x dims {x.shape} - this is no good")
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slicing = []
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skip_dim = True
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for dim_o, dim_x in zip(output.shape, x.shape):
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if not skip_dim and dim_o != dim_x:
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if self.cut_from_start:
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slicing.append(slice(dim_x-dim_o, None))
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else:
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slicing.append(slice(None, dim_o))
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else:
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slicing.append(slice(None))
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skip_dim = False
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x = x[slicing]
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diff = output - x
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batch_offset = diff.shape[0] // len(uuids)
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for i, uuid in enumerate(uuids):
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self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
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def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
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return self.first_cond_uuid in uuids
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def reset(self):
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self.relative_transformation_rate = 0.0
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self.cumulative_change_rate = 0.0
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self.initial_step = True
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self.skip_current_step = False
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self.output_change_rates = []
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self.first_cond_uuid = None
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del self.x_prev_subsampled
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self.x_prev_subsampled = None
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del self.output_prev_subsampled
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self.output_prev_subsampled = None
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del self.output_prev_norm
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self.output_prev_norm = None
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del self.uuid_cache_diffs
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self.uuid_cache_diffs = {}
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self.total_steps_skipped = 0
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return self
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def clone(self):
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return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose)
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class EasyCacheNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="EasyCache",
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display_name="EasyCache",
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description="Native EasyCache implementation.",
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category="advanced/debug/model",
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is_experimental=True,
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inputs=[
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io.Model.Input("model", tooltip="The model to add EasyCache to."),
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io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
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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."),
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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."),
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io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
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],
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outputs=[
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io.Model.Output(tooltip="The model with EasyCache."),
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],
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)
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@classmethod
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def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
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model = model.clone()
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model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose)
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model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper)
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model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper)
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model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper)
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return io.NodeOutput(model)
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class LazyCacheHolder:
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def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
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self.name = "LazyCache"
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self.reuse_threshold = reuse_threshold
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self.start_percent = start_percent
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self.end_percent = end_percent
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self.subsample_factor = subsample_factor
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self.offload_cache_diff = offload_cache_diff
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self.verbose = verbose
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# timestep values
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self.start_t = 0.0
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self.end_t = 0.0
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# control values
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self.relative_transformation_rate: float = None
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self.cumulative_change_rate = 0.0
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self.initial_step = True
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# cache values
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self.x_prev_subsampled: torch.Tensor = None
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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
|
|
|
|
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 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
|
|
self.total_steps_skipped = 0
|
|
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)
|
|
|
|
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/model",
|
|
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."),
|
|
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."),
|
|
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."),
|
|
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
|
|
],
|
|
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)
|
|
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()
|