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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from ..utils import get_logger
from ..utils.torch_utils import unwrap_module
from ._common import _ALL_TRANSFORMER_BLOCK_IDENTIFIERS
from ._helpers import TransformerBlockRegistry
from .hooks import BaseState, HookRegistry, ModelHook, StateManager
logger = get_logger(__name__) # pylint: disable=invalid-name
_MAG_CACHE_LEADER_BLOCK_HOOK = "mag_cache_leader_block_hook"
_MAG_CACHE_BLOCK_HOOK = "mag_cache_block_hook"
# Default Mag Ratios for Flux models (Dev/Schnell) are provided for convenience.
# Users must explicitly pass these to the config if using Flux.
# Reference: https://github.com/Zehong-Ma/MagCache
FLUX_MAG_RATIOS = torch.tensor(
[1.0]
+ [
1.21094,
1.11719,
1.07812,
1.0625,
1.03906,
1.03125,
1.03906,
1.02344,
1.03125,
1.02344,
0.98047,
1.01562,
1.00781,
1.0,
1.00781,
1.0,
1.00781,
1.0,
1.0,
0.99609,
0.99609,
0.98047,
0.98828,
0.96484,
0.95703,
0.93359,
0.89062,
]
)
def nearest_interp(src_array: torch.Tensor, target_length: int) -> torch.Tensor:
"""
Interpolate the source array to the target length using nearest neighbor interpolation.
"""
src_length = len(src_array)
if target_length == 1:
return src_array[-1:]
scale = (src_length - 1) / (target_length - 1)
grid = torch.arange(target_length, device=src_array.device, dtype=torch.float32)
mapped_indices = torch.round(grid * scale).long()
return src_array[mapped_indices]
@dataclass
class MagCacheConfig:
r"""
Configuration for [MagCache](https://github.com/Zehong-Ma/MagCache).
Args:
threshold (`float`, defaults to `0.06`):
The threshold for the accumulated error. If the accumulated error is below this threshold, the block
computation is skipped. A higher threshold allows for more aggressive skipping (faster) but may degrade
quality.
max_skip_steps (`int`, defaults to `3`):
The maximum number of consecutive steps that can be skipped (K in the paper).
retention_ratio (`float`, defaults to `0.2`):
The fraction of initial steps during which skipping is disabled to ensure stability. For example, if
`num_inference_steps` is 28 and `retention_ratio` is 0.2, the first 6 steps will never be skipped.
num_inference_steps (`int`, defaults to `28`):
The number of inference steps used in the pipeline. This is required to interpolate `mag_ratios` correctly.
mag_ratios (`torch.Tensor`, *optional*):
The pre-computed magnitude ratios for the model. These are checkpoint-dependent. If not provided, you must
set `calibrate=True` to calculate them for your specific model. For Flux models, you can use
`diffusers.hooks.mag_cache.FLUX_MAG_RATIOS`.
calibrate (`bool`, defaults to `False`):
If True, enables calibration mode. In this mode, no blocks are skipped. Instead, the hook calculates the
magnitude ratios for the current run and logs them at the end. Use this to obtain `mag_ratios` for new
models or schedulers.
"""
threshold: float = 0.06
max_skip_steps: int = 3
retention_ratio: float = 0.2
num_inference_steps: int = 28
mag_ratios: Optional[Union[torch.Tensor, List[float]]] = None
calibrate: bool = False
def __post_init__(self):
# User MUST provide ratios OR enable calibration.
if self.mag_ratios is None and not self.calibrate:
raise ValueError(
" `mag_ratios` must be provided for MagCache inference because these ratios are model-dependent.\n"
"To get them for your model:\n"
"1. Initialize `MagCacheConfig(calibrate=True, ...)`\n"
"2. Run inference on your model once.\n"
"3. Copy the printed ratios array and pass it to `mag_ratios` in the config.\n"
"For Flux models, you can import `FLUX_MAG_RATIOS` from `diffusers.hooks.mag_cache`."
)
if not self.calibrate and self.mag_ratios is not None:
if not torch.is_tensor(self.mag_ratios):
self.mag_ratios = torch.tensor(self.mag_ratios)
if len(self.mag_ratios) != self.num_inference_steps:
logger.debug(
f"Interpolating mag_ratios from length {len(self.mag_ratios)} to {self.num_inference_steps}"
)
self.mag_ratios = nearest_interp(self.mag_ratios, self.num_inference_steps)
class MagCacheState(BaseState):
def __init__(self) -> None:
super().__init__()
# Cache for the residual (output - input) from the *previous* timestep
self.previous_residual: torch.Tensor = None
# State inputs/outputs for the current forward pass
self.head_block_input: Union[torch.Tensor, Tuple[torch.Tensor, ...]] = None
self.should_compute: bool = True
# MagCache accumulators
self.accumulated_ratio: float = 1.0
self.accumulated_err: float = 0.0
self.accumulated_steps: int = 0
# Current step counter (timestep index)
self.step_index: int = 0
# Calibration storage
self.calibration_ratios: List[float] = []
def reset(self):
self.previous_residual = None
self.should_compute = True
self.accumulated_ratio = 1.0
self.accumulated_err = 0.0
self.accumulated_steps = 0
self.step_index = 0
self.calibration_ratios = []
class MagCacheHeadHook(ModelHook):
_is_stateful = True
def __init__(self, state_manager: StateManager, config: MagCacheConfig):
self.state_manager = state_manager
self.config = config
self._metadata = None
def initialize_hook(self, module):
unwrapped_module = unwrap_module(module)
self._metadata = TransformerBlockRegistry.get(unwrapped_module.__class__)
return module
@torch.compiler.disable
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if self.state_manager._current_context is None:
self.state_manager.set_context("inference")
arg_name = self._metadata.hidden_states_argument_name
hidden_states = self._metadata._get_parameter_from_args_kwargs(arg_name, args, kwargs)
state: MagCacheState = self.state_manager.get_state()
state.head_block_input = hidden_states
should_compute = True
if self.config.calibrate:
# Never skip during calibration
should_compute = True
else:
# MagCache Logic
current_step = state.step_index
if current_step >= len(self.config.mag_ratios):
current_scale = 1.0
else:
current_scale = self.config.mag_ratios[current_step]
retention_step = int(self.config.retention_ratio * self.config.num_inference_steps + 0.5)
if current_step >= retention_step:
state.accumulated_ratio *= current_scale
state.accumulated_steps += 1
state.accumulated_err += abs(1.0 - state.accumulated_ratio)
if (
state.previous_residual is not None
and state.accumulated_err <= self.config.threshold
and state.accumulated_steps <= self.config.max_skip_steps
):
should_compute = False
else:
state.accumulated_ratio = 1.0
state.accumulated_steps = 0
state.accumulated_err = 0.0
state.should_compute = should_compute
if not should_compute:
logger.debug(f"MagCache: Skipping step {state.step_index}")
# Apply MagCache: Output = Input + Previous Residual
output = hidden_states
res = state.previous_residual
if res.device != output.device:
res = res.to(output.device)
# Attempt to apply residual handling shape mismatches (e.g., text+image vs image only)
if res.shape == output.shape:
output = output + res
elif (
output.ndim == 3
and res.ndim == 3
and output.shape[0] == res.shape[0]
and output.shape[2] == res.shape[2]
):
# Assuming concatenation where image part is at the end (standard in Flux/SD3)
diff = output.shape[1] - res.shape[1]
if diff > 0:
output = output.clone()
output[:, diff:, :] = output[:, diff:, :] + res
else:
logger.warning(
f"MagCache: Dimension mismatch. Input {output.shape}, Residual {res.shape}. "
"Cannot apply residual safely. Returning input without residual."
)
else:
logger.warning(
f"MagCache: Dimension mismatch. Input {output.shape}, Residual {res.shape}. "
"Cannot apply residual safely. Returning input without residual."
)
if self._metadata.return_encoder_hidden_states_index is not None:
original_encoder_hidden_states = self._metadata._get_parameter_from_args_kwargs(
"encoder_hidden_states", args, kwargs
)
max_idx = max(
self._metadata.return_hidden_states_index, self._metadata.return_encoder_hidden_states_index
)
ret_list = [None] * (max_idx + 1)
ret_list[self._metadata.return_hidden_states_index] = output
ret_list[self._metadata.return_encoder_hidden_states_index] = original_encoder_hidden_states
return tuple(ret_list)
else:
return output
else:
# Compute original forward
output = self.fn_ref.original_forward(*args, **kwargs)
return output
def reset_state(self, module):
self.state_manager.reset()
return module
class MagCacheBlockHook(ModelHook):
def __init__(self, state_manager: StateManager, is_tail: bool = False, config: MagCacheConfig = None):
super().__init__()
self.state_manager = state_manager
self.is_tail = is_tail
self.config = config
self._metadata = None
def initialize_hook(self, module):
unwrapped_module = unwrap_module(module)
self._metadata = TransformerBlockRegistry.get(unwrapped_module.__class__)
return module
@torch.compiler.disable
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if self.state_manager._current_context is None:
self.state_manager.set_context("inference")
state: MagCacheState = self.state_manager.get_state()
if not state.should_compute:
arg_name = self._metadata.hidden_states_argument_name
hidden_states = self._metadata._get_parameter_from_args_kwargs(arg_name, args, kwargs)
if self.is_tail:
# Still need to advance step index even if we skip
self._advance_step(state)
if self._metadata.return_encoder_hidden_states_index is not None:
encoder_hidden_states = self._metadata._get_parameter_from_args_kwargs(
"encoder_hidden_states", args, kwargs
)
max_idx = max(
self._metadata.return_hidden_states_index, self._metadata.return_encoder_hidden_states_index
)
ret_list = [None] * (max_idx + 1)
ret_list[self._metadata.return_hidden_states_index] = hidden_states
ret_list[self._metadata.return_encoder_hidden_states_index] = encoder_hidden_states
return tuple(ret_list)
return hidden_states
output = self.fn_ref.original_forward(*args, **kwargs)
if self.is_tail:
# Calculate residual for next steps
if isinstance(output, tuple):
out_hidden = output[self._metadata.return_hidden_states_index]
else:
out_hidden = output
in_hidden = state.head_block_input
if in_hidden is None:
return output
# Determine residual
if out_hidden.shape == in_hidden.shape:
residual = out_hidden - in_hidden
elif out_hidden.ndim == 3 and in_hidden.ndim == 3 and out_hidden.shape[2] == in_hidden.shape[2]:
diff = in_hidden.shape[1] - out_hidden.shape[1]
if diff == 0:
residual = out_hidden - in_hidden
else:
residual = out_hidden - in_hidden # Fallback to matching tail
else:
# Fallback for completely mismatched shapes
residual = out_hidden
if self.config.calibrate:
self._perform_calibration_step(state, residual)
state.previous_residual = residual
self._advance_step(state)
return output
def _perform_calibration_step(self, state: MagCacheState, current_residual: torch.Tensor):
if state.previous_residual is None:
# First step has no previous residual to compare against.
# log 1.0 as a neutral starting point.
ratio = 1.0
else:
# MagCache Calibration Formula: mean(norm(curr) / norm(prev))
# norm(dim=-1) gives magnitude of each token vector
curr_norm = torch.linalg.norm(current_residual.float(), dim=-1)
prev_norm = torch.linalg.norm(state.previous_residual.float(), dim=-1)
# Avoid division by zero
ratio = (curr_norm / (prev_norm + 1e-8)).mean().item()
state.calibration_ratios.append(ratio)
def _advance_step(self, state: MagCacheState):
state.step_index += 1
if state.step_index >= self.config.num_inference_steps:
# End of inference loop
if self.config.calibrate:
print("\n[MagCache] Calibration Complete. Copy these values to MagCacheConfig(mag_ratios=...):")
print(f"{state.calibration_ratios}\n")
logger.info(f"MagCache Calibration Results: {state.calibration_ratios}")
# Reset state
state.step_index = 0
state.accumulated_ratio = 1.0
state.accumulated_steps = 0
state.accumulated_err = 0.0
state.previous_residual = None
state.calibration_ratios = []
def apply_mag_cache(module: torch.nn.Module, config: MagCacheConfig) -> None:
"""
Applies MagCache to a given module (typically a Transformer).
Args:
module (`torch.nn.Module`):
The module to apply MagCache to.
config (`MagCacheConfig`):
The configuration for MagCache.
"""
# Initialize registry on the root module so the Pipeline can set context.
HookRegistry.check_if_exists_or_initialize(module)
state_manager = StateManager(MagCacheState, (), {})
remaining_blocks = []
for name, submodule in module.named_children():
if name not in _ALL_TRANSFORMER_BLOCK_IDENTIFIERS or not isinstance(submodule, torch.nn.ModuleList):
continue
for index, block in enumerate(submodule):
remaining_blocks.append((f"{name}.{index}", block))
if not remaining_blocks:
logger.warning("MagCache: No transformer blocks found to apply hooks.")
return
# Handle single-block models
if len(remaining_blocks) == 1:
name, block = remaining_blocks[0]
logger.info(f"MagCache: Applying Head+Tail Hooks to single block '{name}'")
_apply_mag_cache_block_hook(block, state_manager, config, is_tail=True)
_apply_mag_cache_head_hook(block, state_manager, config)
return
head_block_name, head_block = remaining_blocks.pop(0)
tail_block_name, tail_block = remaining_blocks.pop(-1)
logger.info(f"MagCache: Applying Head Hook to {head_block_name}")
_apply_mag_cache_head_hook(head_block, state_manager, config)
for name, block in remaining_blocks:
_apply_mag_cache_block_hook(block, state_manager, config)
logger.info(f"MagCache: Applying Tail Hook to {tail_block_name}")
_apply_mag_cache_block_hook(tail_block, state_manager, config, is_tail=True)
def _apply_mag_cache_head_hook(block: torch.nn.Module, state_manager: StateManager, config: MagCacheConfig) -> None:
registry = HookRegistry.check_if_exists_or_initialize(block)
# Automatically remove existing hook to allow re-application (e.g. switching modes)
if registry.get_hook(_MAG_CACHE_LEADER_BLOCK_HOOK) is not None:
registry.remove_hook(_MAG_CACHE_LEADER_BLOCK_HOOK)
hook = MagCacheHeadHook(state_manager, config)
registry.register_hook(hook, _MAG_CACHE_LEADER_BLOCK_HOOK)
def _apply_mag_cache_block_hook(
block: torch.nn.Module,
state_manager: StateManager,
config: MagCacheConfig,
is_tail: bool = False,
) -> None:
registry = HookRegistry.check_if_exists_or_initialize(block)
# Automatically remove existing hook to allow re-application
if registry.get_hook(_MAG_CACHE_BLOCK_HOOK) is not None:
registry.remove_hook(_MAG_CACHE_BLOCK_HOOK)
hook = MagCacheBlockHook(state_manager, is_tail, config)
registry.register_hook(hook, _MAG_CACHE_BLOCK_HOOK)