from __future__ import annotations from typing import TYPE_CHECKING, Union from scipy.sparse.linalg._dsolve.linsolve import is_pydata_spmatrix 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 outer_sample_wrapper(executor, *args, **kwargs): try: logging.info("Sortblock: inside outer_sample!") 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"]["sortblock"] = guider.model_options["transformer_options"]["sortblock"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling) sb_holder: SortblockHolder = guider.model_options["transformer_options"]["sortblock"] logging.info(f"Sortblock: enabled - threshold: {sb_holder.reuse_threshold}, start_percent: {sb_holder.start_percent}, end_percent: {sb_holder.end_percent}") return executor(*args, **kwargs) finally: sb_holder = guider.model_options["transformer_options"]["sortblock"] sb_holder.print_block_info(0) sb_holder.reset() guider.model_options = orig_model_options def model_forward_wrapper(executor, *args, **kwargs): timestep: float = args[1] 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] logging.info(f"Sortblock: inside model {executor.class_obj.__class__.__name__}") sb_holder: SortblockHolder = transformer_options["sortblock"] sb_holder.update_should_do_sortblock(timestep) sb_holder.update_is_past_end_timestep(timestep) if sb_holder.initial_step: transformer_options["total_double_block"] = len(executor.class_obj.double_blocks) transformer_options["total_single_block"] = len(executor.class_obj.single_blocks) # save the original forwards on the blocks logging.info(f"Sortblock: preparing {transformer_options['total_double_block']} double blocks and {transformer_options['total_single_block']} single blocks") for block in executor.class_obj.double_blocks: prepare_block(block, sb_holder) for block in executor.class_obj.single_blocks: prepare_block(block, sb_holder) try: return executor(*args, **kwargs) finally: sb_holder: SortblockHolder = transformer_options["sortblock"] # do double blocks total_double_block = len(executor.class_obj.double_blocks) total_single_block = len(executor.class_obj.single_blocks) perform_sortblock(sb_holder.blocks[:total_double_block]) #perform_sortblock(sb_holder.blocks[total_double_block:]) if sb_holder.initial_step: sb_holder.initial_step = False def perform_sortblock(blocks: list): candidate_blocks = [] for block in blocks: cache: BlockCache = getattr(block, "__block_cache") cache.allowed_to_skip = False if cache.want_to_skip: candidate_blocks.append(block) if len(candidate_blocks) > 0: percentage_to_skip = 1.0 candidate_blocks.sort(key=lambda x: getattr(x, "__block_cache").cumulative_change_rate) blocks_to_skip = int(len(candidate_blocks) * percentage_to_skip) for block in candidate_blocks[:blocks_to_skip]: cache: BlockCache = getattr(block, "__block_cache") cache.allowed_to_skip = True def prepare_block(block, sb_holder: SortblockHolder, stream_count: int=1): sb_holder.add_block(block) block.__original_forward = block.forward block.forward = block_forward_factory(block.__original_forward, block) block.__block_cache = BlockCache(subsample_factor=sb_holder.subsample_factor, verbose=sb_holder.verbose) def clean_block(block): block.forward = block.__original_forward del block.__original_forward del block.__block_cache def block_forward_factory(func, block): def block_forward_wrapper(*args, **kwargs): transformer_options: dict[str] = kwargs.get("transformer_options", None) #logging.info(f"Sortblock: inside block {transformer_options['block']}") sb_holder: SortblockHolder = transformer_options["sortblock"] cache: BlockCache = block.__block_cache if sb_holder.initial_step: cache.stream_count = transformer_options['block'][2] if sb_holder.is_past_end_timestep(): return func(*args, **kwargs) # do sortblock stuff keys = list(kwargs.keys()) x = cache.get_next_x_prev(kwargs) timestep: float = sb_holder.curr_t # prepare next_x_prev next_x_prev = cache.get_next_x_prev(kwargs) input_change = None do_sortblock = sb_holder.should_do_sortblock() if do_sortblock: # TODO: checkmetadata if cache.has_x_prev_subsampled(): input_change = (cache.subsample(x, clone=False) - cache.x_prev_subsampled).flatten().abs().mean() if cache.has_output_prev_norm() and cache.has_relative_transformation_rate(): approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm cache.cumulative_change_rate += approx_output_change_rate if cache.cumulative_change_rate < sb_holder.reuse_threshold: # accumulate error + skip block cache.want_to_skip = True if cache.allowed_to_skip: return cache.apply_cache_diff(x) else: # reset error; NOT skipping block and recalculating cache.cumulative_change_rate = 0.0 cache.want_to_skip = False # output_raw is expected to have cache.stream_count elements if count is greaater than 1 (double block, etc.) output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]] = func(*args, **kwargs) # if more than one stream from block, only use first one if isinstance(output_raw, tuple): output = output_raw[0] else: output = output_raw if cache.has_output_prev_norm(): output_change = (cache.subsample(output, clone=False) - cache.output_prev_subsampled).flatten().abs().mean() # if verbose in future output_change_rate = output_change / cache.output_prev_norm cache.output_change_rates.append(output_change_rate.item()) if cache.has_relative_transformation_rate(): approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm cache.approx_output_change_rates.append(approx_output_change_rate.item()) if input_change is not None: cache.relative_transformation_rate = output_change / input_change # TODO: allow cache_diff to be offloaded cache.update_cache_diff(output_raw, next_x_prev) cache.x_prev_subsampled = cache.subsample(next_x_prev) cache.output_prev_subsampled = cache.subsample(output) cache.output_prev_norm = output.flatten().abs().mean() return output_raw return block_forward_wrapper class SortblockHolder: def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int=8, verbose: bool=False): self.reuse_threshold = reuse_threshold self.start_percent = start_percent self.end_percent = end_percent self.subsample_factor = subsample_factor self.verbose = verbose # timestep values self.start_t = 0.0 self.end_t = 0.0 self.curr_t = 0.0 # control values self.past_timestep = False self.do_sortblock = False self.initial_step = True # cache values self.blocks = [] def add_block(self, block): self.blocks.append(block) 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 update_is_past_end_timestep(self, timestep: float) -> bool: self.past_timestep = not (timestep[0] > self.end_t).item() return self.past_timestep def is_past_end_timestep(self) -> bool: return self.past_timestep def update_should_do_sortblock(self, timestep: float) -> bool: self.do_sortblock = (timestep[0] <= self.start_t).item() self.curr_t = timestep return self.do_sortblock def should_do_sortblock(self) -> bool: return self.do_sortblock def print_block_info(self, index: int): block = self.blocks[index] cache = getattr(block, "__block_cache") logging.info(f"Sortblock: block {index} output_change_rates: {cache.output_change_rates}") logging.info(f"Sortblock: block {index} approx_output_change_rates: {cache.approx_output_change_rates}") def reset(self): self.initial_step = True self.curr_t = 0.0 logging.info(f"Sortblock: resetting {len(self.blocks)} blocks") for block in self.blocks: clean_block(block) self.blocks = [] return self def clone(self): return SortblockHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.verbose) class BlockCache: def __init__(self, subsample_factor: int=8, stream_count: int=1, verbose: bool=False): self.subsample_factor = subsample_factor self.stream_count = stream_count self.verbose = verbose # 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: list[torch.Tensor] = [None for _ in range(stream_count)] self.output_change_rates = [] self.approx_output_change_rates = [] self.total_steps_skipped = 0 self.state_metadata = None self.want_to_skip = False self.allowed_to_skip = False def has_cache_diff(self) -> bool: return self.cache_diff[0] is not None 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 get_next_x_prev(self, d_kwargs: dict[str, torch.Tensor]) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: keys = list(d_kwargs.keys()) if self.stream_count == 1: return d_kwargs[keys[0]] return tuple([d_kwargs[keys[i]] for i in range(self.stream_count)]) def subsample(self, x: Union[torch.Tensor, tuple[torch.Tensor, ...]], clone: bool = True) -> torch.Tensor: # subsample only the first compoenent if isinstance(x, tuple): return self.subsample(x[0], clone) 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: Union[torch.Tensor, tuple[torch.Tensor, ...]]): self.total_steps_skipped += 1 if not isinstance(x, tuple): x = (x, ) to_return = tuple([x[i] + self.cache_diff[i] for i in range(self.stream_count)]) if len(to_return) == 1: return to_return[0] return to_return def update_cache_diff(self, output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]], x: Union[torch.Tensor, tuple[torch.Tensor, ...]]): if not isinstance(output_raw, tuple): output_raw = (output_raw, ) if not isinstance(x, tuple): x = (x, ) self.cache_diff = tuple([output_raw[i] - x[i] for i in range(self.stream_count)]) def check_metadata(self, x: torch.Tensor) -> bool: # TODO: make sure shapes are correct 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.cache_diff = [None for _ in range(self.stream_count)] self.output_change_rates = [] self.approx_output_change_rates = [] self.total_steps_skipped = 0 self.state_metadata = None self.want_to_skip = False self.allowed_to_skip = False class SortblockNode(io.ComfyNode): @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="Sortblock", display_name="Sortblock", 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 Sortblock to."), io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached blocks."), 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 Sortblock."), 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 Sortblock."), io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."), ], outputs=[ io.Model.Output(tooltip="The model with Sortblock."), ], ) @classmethod def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: # TODO: check for specific flavors of supported models model = model.clone() model.model_options["transformer_options"]["sortblock"] = SortblockHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, verbose=verbose) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", outer_sample_wrapper) model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper) # 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 SortblockExtension(ComfyExtension): async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ SortblockNode, ] def comfy_entrypoint(): return SortblockExtension()