Doing some experimentation

comfy_extras/nodes_easysortblock.py

@@ -0,0 +1,503 @@
+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 math
+import comfy.model_patcher
+if TYPE_CHECKING:
+    from uuid import UUID
+
+
+
+def easysortblock_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: EasySortblockHolder = model_options["transformer_options"]["easycache"]
+
+    # initialize predict_ratios
+    if easycache.initial_step:
+        sample_sigmas = model_options["transformer_options"]["sample_sigmas"]
+        relevant_sigmas = []
+        for i,sigma in enumerate(sample_sigmas):
+            if easycache.check_if_within_timesteps(sigma):
+                relevant_sigmas.append((i, sigma))
+        start_index = relevant_sigmas[0][0]
+        end_index = relevant_sigmas[-1][0]
+        easycache.predict_ratios = torch.linspace(easycache.start_predict_ratio, easycache.end_predict_ratio, end_index - start_index + 1)
+        easycache.predict_start_index = start_index
+
+    easycache.skip_current_step = False
+    if easycache.is_past_end_timestep(timestep):
+        return executor(*args, **kwargs)
+    # 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"EasySortblock [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
+                    # other conds should also skip this step
+                    easycache.skip_current_step = True
+                    easycache.steps_skipped.append(easycache.step_count)
+                else:
+                    if easycache.verbose:
+                        logging.info(f"EasySortblock [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"EasySortblock [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"EasySortblock [verbose] - output_change_rate: {output_change_rate}")
+    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"EasySortblock [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
+
+    # increment step count
+    easycache.step_count += 1
+    easycache.initial_step = False
+
+    return output
+
+
+def easysortblock_outer_sample_wrapper(executor, *args, **kwargs):
+    """
+    This OUTER_SAMPLE wrapper makes sure EasySortblock 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: EasySortblockHolder = 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
+        logging.info(f"{easycache.name} - skipped {len(easycache.steps_skipped)}/{total_steps} steps")# ({total_steps/(total_steps-easycache.total_steps_skipped):.2f}x speedup).")
+        logging.info(f"{easycache.name} - skipped steps: {easycache.steps_skipped}")
+        easycache.reset()
+        guider.model_options = orig_model_options
+
+
+def model_forward_wrapper(executor, *args, **kwargs):
+    # TODO: make work with batches of conds
+    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]
+    sigmas = transformer_options["sigmas"]
+    sb_holder: EasySortblockHolder = transformer_options["easycache"]
+
+    # if initial step, prepare everything for Sortblock
+    if sb_holder.initial_step:
+        logging.info(f"EasySortblock: inside model {executor.class_obj.__class__.__name__}")
+        # TODO: generalize for other models
+        # these won't stick around past this step; should store on sb_holder instead
+        logging.info(f"EasySortblock: preparing {len(executor.class_obj.double_blocks)} double blocks and {len(executor.class_obj.single_blocks)} single blocks")
+        if hasattr(executor.class_obj, "double_blocks"):
+            for block in executor.class_obj.double_blocks:
+                prepare_block(block, sb_holder)
+        if hasattr(executor.class_obj, "single_blocks"):
+            for block in executor.class_obj.single_blocks:
+                prepare_block(block, sb_holder)
+        if hasattr(executor.class_obj, "blocks"):
+            for block in executor.class_obj.block:
+                prepare_block(block, sb_holder)
+
+    if sb_holder.skip_current_step:
+        predict_index = max(0, sb_holder.step_count - sb_holder.predict_start_index)
+        predict_ratio = sb_holder.predict_ratios[predict_index]
+        logging.info(f"EasySortblock: skipping step {sb_holder.step_count}, predict_ratio: {predict_ratio}")
+        # reuse_ratio = 1.0 - predict_ratio
+        for block_type, blocks in sb_holder.blocks_per_type.items():
+            for block in blocks:
+                cache: BlockCache = block.__block_cache
+                cache.allowed_to_skip = False
+            sorted_blocks = sorted(blocks, key=lambda x: (x.__block_cache.consecutive_skipped_steps, x.__block_cache.prev_change_rate))
+            # for block in sorted_blocks:
+            #     pass
+            threshold_index = int(len(sorted_blocks) * predict_ratio)
+            # blocks with lower similarity are marked for recomputation
+            for block in sorted_blocks[:threshold_index]:
+                cache: BlockCache = block.__block_cache
+                cache.allowed_to_skip = True
+                logging.info(f"EasySortblock: skip block {block.__class__.__name__} - consecutive_skipped_steps: {block.__block_cache.consecutive_skipped_steps}, prev_change_rate: {block.__block_cache.prev_change_rate}, index: {block.__block_cache.block_index}")
+            not_skipped  = [block for block in blocks if not block.__block_cache.allowed_to_skip]
+            for block in not_skipped:
+                logging.info(f"EasySortblock: reco block {block.__class__.__name__} - consecutive_skipped_steps: {block.__block_cache.consecutive_skipped_steps}, prev_change_rate: {block.__block_cache.prev_change_rate}, index: {block.__block_cache.block_index}")
+            logging.info(f"EasySortblock: for {block_type}, selected {len(sorted_blocks[:threshold_index])} blocks for prediction and {len(sorted_blocks[threshold_index:])} blocks for recomputation")
+        # return executor(*args, **kwargs)
+    to_return = executor(*args, **kwargs)
+
+    return to_return
+
+
+
+
+
+def block_forward_factory(func, block):
+    def block_forward_wrapper(*args, **kwargs):
+        transformer_options: dict[str] = kwargs.get("transformer_options")
+        sigmas = transformer_options["sigmas"]
+        sb_holder: EasySortblockHolder = transformer_options["easycache"]
+        cache: BlockCache = block.__block_cache
+        # make sure stream count is properly set for this block
+        if sb_holder.initial_step:
+            sb_holder.add_to_blocks_per_type(block, transformer_options['block'][0])
+            cache.block_index = transformer_options['block'][1]
+            cache.stream_count = transformer_options['block'][2]
+
+        if sb_holder.is_past_end_timestep(sigmas):
+            return func(*args, **kwargs)
+        # do sortblock stuff
+        x = cache.get_next_x_prev(args, kwargs)
+        # prepare next_x_prev
+        next_x_prev = cache.get_next_x_prev(args, kwargs, clone=True)
+        input_change = None
+        do_sortblock = sb_holder.should_do_easycache(sigmas)
+        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.allowed_to_skip:
+                # if cache.cumulative_change_rate < sb_holder.reuse_threshold:
+                    # accumulate error + skip block
+                    # cache.want_to_skip = True
+                    # if cache.allowed_to_skip:
+                    cache.consecutive_skipped_steps += 1
+                    cache.prev_change_rate = approx_output_change_rate
+                    return cache.apply_cache_diff(x, sb_holder)
+                else:
+                    # reset error; NOT skipping block and recalculating
+                    cache.cumulative_change_rate = 0.0
+                    cache.prev_change_rate = approx_output_change_rate
+                    cache.want_to_skip = False
+                    cache.consecutive_skipped_steps = 0
+        # 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
+
+def prepare_block(block, sb_holder: EasySortblockHolder, stream_count: int=1):
+    sb_holder.add_to_all_blocks(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
+
+class BlockCache:
+    def __init__(self, subsample_factor: int=8, verbose: bool=False):
+        self.subsample_factor = subsample_factor
+        self.verbose = verbose
+        self.stream_count = 1
+        self.block_index = 0
+        # control values
+        self.relative_transformation_rate: float = None
+        self.cumulative_change_rate = 0.0
+        self.prev_change_rate = 0.0
+        # cached 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] = []
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.steps_skipped: list[int] = []
+        self.consecutive_skipped_steps = 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_args: tuple[torch.Tensor, ...], d_kwargs: dict[str, torch.Tensor], clone: bool=False) -> tuple[torch.Tensor, ...]:
+        if self.stream_count == 1:
+            if clone:
+                return d_args[0].clone()
+            return d_args[0]
+        keys = list(d_kwargs.keys())[:self.stream_count]
+        orig_inputs = []
+        for key in keys:
+            if clone:
+                orig_inputs.append(d_kwargs[key].clone())
+            else:
+                orig_inputs.append(d_kwargs[key])
+        return tuple(orig_inputs)
+
+    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, ...]], sb_holder: EasySortblockHolder):
+        self.steps_skipped.append(sb_holder.step_count)
+        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 reset(self):
+        self.relative_transformation_rate = 0.0
+        self.cumulative_change_rate = 0.0
+        self.prev_change_rate = 0.0
+        self.x_prev_subsampled = None
+        self.output_prev_subsampled = None
+        self.output_prev_norm = None
+        self.cache_diff = []
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.steps_skipped = []
+        self.consecutive_skipped_steps = 0
+        self.want_to_skip = False
+        self.allowed_to_skip = False
+        return self
+
+
+class EasySortblockHolder:
+    def __init__(self, reuse_threshold: float, start_predict_ratio: float, end_predict_ratio: float, max_skipped_steps: int,
+                       start_percent: float, end_percent: float, subsample_factor: int, verbose: bool=False):
+        self.name = "EasySortblock"
+        self.reuse_threshold = reuse_threshold
+        self.start_predict_ratio = start_predict_ratio
+        self.end_predict_ratio = end_predict_ratio
+        self.max_skipped_steps = max_skipped_steps
+        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
+        # control values
+        self.relative_transformation_rate: float = None
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        self.step_count = 0
+        self.predict_ratios = []
+        self.skip_current_step = False
+        self.predict_start_index = 0
+        # cache values
+        self.x_prev_subsampled: torch.Tensor = None
+        self.output_prev_subsampled: torch.Tensor = None
+        self.output_prev_norm: torch.Tensor = None
+        self.steps_skipped: list[int] = []
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.state_metadata = None
+        self.all_blocks = []
+        self.blocks_per_type = {}
+
+    def add_to_all_blocks(self, block):
+        self.all_blocks.append(block)
+
+    def add_to_blocks_per_type(self, block, block_type: str):
+        self.blocks_per_type.setdefault(block_type, []).append(block)
+
+    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 check_if_within_timesteps(self, timestep: Union[float, torch.Tensor]) -> bool:
+        return (timestep <= self.start_t).item() and (timestep > self.end_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 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.warning(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
+        self.reset()
+        return False
+
+    def reset(self):
+        logging.info(f"EasySortblock: resetting {len(self.all_blocks)} blocks")
+        for block in self.all_blocks:
+            clean_block(block)
+        self.relative_transformation_rate = 0.0
+        self.cumulative_change_rate = 0.0
+        self.initial_step = True
+        self.step_count = 0
+        self.predict_ratios = []
+        self.skip_current_step = False
+        self.predict_start_index = 0
+        self.x_prev_subsampled = None
+        self.output_prev_subsampled = None
+        self.output_prev_norm = None
+        self.steps_skipped = []
+        self.output_change_rates = []
+        self.approx_output_change_rates = []
+        self.state_metadata = None
+        self.all_blocks = []
+        self.blocks_per_type = {}
+        return self
+
+    def clone(self):
+        return EasySortblockHolder(self.reuse_threshold, self.start_predict_ratio, self.end_predict_ratio, self.max_skipped_steps,
+                                   self.start_percent, self.end_percent, self.subsample_factor, self.verbose)
+
+class EasySortblockScaledNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EasySortblockScaled",
+            display_name="EasySortblockScaled",
+            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 steps."),
+                io.Float.Input("start_predict_ratio", min=0.0, default=0.2, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
+                io.Float.Input("end_predict_ratio", min=0.0, default=0.9, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
+                io.Int.Input("policy_refresh_interval", min=3, default=5, max=100, step=1, tooltip="The interval at which to refresh the policy."),
+                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_predict_ratio: float, end_predict_ratio: float, policy_refresh_interval: int, 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"]["easycache"] = EasySortblockHolder(reuse_threshold, start_predict_ratio, end_predict_ratio, policy_refresh_interval, start_percent, end_percent, subsample_factor=8, verbose=verbose)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "sortblock", easysortblock_predict_noise_wrapper)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", easysortblock_outer_sample_wrapper)
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper)
+        return io.NodeOutput(model)
+
+
+class EasySortblockExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            # EasySortblockNode,
+            EasySortblockScaledNode,
+        ]
+
+def comfy_entrypoint():
+    return EasySortblockExtension()
+

comfy_extras/nodes_sortblock.py

@@ -25,6 +25,7 @@ def prepare_noise_wrapper(executor, *args, **kwargs):
             start_index = relevant_sigmas[0][0]
             end_index = relevant_sigmas[-1][0]
             sb_holder.predict_ratios = torch.linspace(sb_holder.start_predict_ratio, sb_holder.end_predict_ratio, end_index - start_index + 1)
+            sb_holder.predict_start_index = start_index
 
         return executor(*args, **kwargs)
     finally:
@@ -95,7 +96,8 @@ def model_forward_wrapper(executor, *args, **kwargs):
 
     # when 1: Select DiT blocks(4)
     if sb_holder.step_modulus == 1:
-        predict_ratio = sb_holder.predict_ratios[sb_holder.active_steps-1]
+        predict_index = max(0, sb_holder.step_count - sb_holder.predict_start_index)
+        predict_ratio = sb_holder.predict_ratios[predict_index]
         logging.info(f"Sortblock: for step {sb_holder.step_count}, selecting blocks for recomputation and prediction, predict_ratio: {predict_ratio}")
         reuse_ratio = 1.0 - predict_ratio
         for block_type, blocks in sb_holder.blocks_per_type.items():
@@ -322,6 +324,7 @@ class SortblockHolder:
         self.do_sortblock = False
         self.active_steps = 0
         self.predict_ratios = []
+        self.predict_start_index = 0
 
         # cache values
         self.all_blocks = []
@@ -371,6 +374,7 @@ class SortblockHolder:
         self.active_steps = 0
         self.predict_ratios = []
         self.do_sortblock = False
+        self.predict_start_index = 0
         return self
 
     def clone(self):

nodes.py

@@ -2326,6 +2326,7 @@ async def init_builtin_extra_nodes():
         "nodes_easycache.py",
         "nodes_audio_encoder.py",
         "nodes_sortblock.py",
+        "nodes_easysortblock.py",
     ]
 
     import_failed = []