## CacheDiT  

CacheDiT is a unified, flexible, and training-free cache acceleration framework designed to support nearly all Diffusers' DiT-based pipelines. It provides a unified cache API that supports automatic block adapter, DBCache, and more.

To learn more, refer to the [CacheDiT](https://github.com/vipshop/cache-dit) repository.

Install a stable release of CacheDiT from PyPI or you can install the latest version from GitHub.

```bash
pip3 install -U cache-dit
```

```bash
pip3 install git+https://github.com/vipshop/cache-dit.git
```

Run the command below to view supported DiT pipelines.

```python
>>> import cache_dit
>>> cache_dit.supported_pipelines()
(30, ['Flux*', 'Mochi*', 'CogVideoX*', 'Wan*', 'HunyuanVideo*', 'QwenImage*', 'LTX*', 'Allegro*',
'CogView3Plus*', 'CogView4*', 'Cosmos*', 'EasyAnimate*', 'SkyReelsV2*', 'StableDiffusion3*',
'ConsisID*', 'DiT*', 'Amused*', 'Bria*', 'Lumina*', 'OmniGen*', 'PixArt*', 'Sana*', 'StableAudio*',
'VisualCloze*', 'AuraFlow*', 'Chroma*', 'ShapE*', 'HiDream*', 'HunyuanDiT*', 'HunyuanDiTPAG*'])
```

For a complete benchmark, please refer to [Benchmarks](https://github.com/vipshop/cache-dit/blob/main/bench/).

## Unified Cache API

CacheDiT works by matching specific input/output patterns as shown below.

![](https://github.com/vipshop/cache-dit/raw/main/assets/patterns-v1.png)

Call the `enable_cache()` function on a pipeline to enable cache acceleration. This function is the entry point to many of CacheDiT's features.

```python
import cache_dit
from diffusers import DiffusionPipeline 

# Can be any diffusion pipeline
pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image")

# One-line code with default cache options.
cache_dit.enable_cache(pipe) 

# Just call the pipe as normal.
output = pipe(...)

# Disable cache and run original pipe.
cache_dit.disable_cache(pipe)
```

## Automatic Block Adapter

For custom or modified pipelines or transformers not included in Diffusers, use the `BlockAdapter` in `auto` mode or via manual configuration. Please check the [BlockAdapter](https://github.com/vipshop/cache-dit/blob/main/docs/User_Guide.md#automatic-block-adapter) docs for more details. Refer to [Qwen-Image w/ BlockAdapter](https://github.com/vipshop/cache-dit/blob/main/examples/adapter/run_qwen_image_adapter.py) as an example.

```python
from cache_dit import ForwardPattern, BlockAdapter

# Use 🔥BlockAdapter with `auto` mode.
cache_dit.enable_cache(
    BlockAdapter(
        # Any DiffusionPipeline, Qwen-Image, etc.  
        pipe=pipe, auto=True,
        # Check `📚Forward Pattern Matching` documentation and hack the code of
        # of Qwen-Image, you will find that it has satisfied `FORWARD_PATTERN_1`.
        forward_pattern=ForwardPattern.Pattern_1,
    ),   
)

# Or, manually setup transformer configurations.
cache_dit.enable_cache(
    BlockAdapter(
        pipe=pipe, # Qwen-Image, etc.
        transformer=pipe.transformer,
        blocks=pipe.transformer.transformer_blocks,
        forward_pattern=ForwardPattern.Pattern_1,
    ), 
)
```

Sometimes, a Transformer class will contain more than one transformer `blocks`. For example, FLUX.1 (HiDream, Chroma, etc) contains `transformer_blocks` and `single_transformer_blocks` (with different forward patterns). The BlockAdapter is able to detect this hybrid pattern type as well. 
Refer to [FLUX.1](https://github.com/vipshop/cache-dit/blob/main/examples/adapter/run_flux_adapter.py) as an example.

```python
# For diffusers  BlockAdapter:
    from diffusers import HiDreamImageTransformer2DModel
    from cache_dit.cache_factory.patch_functors import HiDreamPatchFunctor

    assert isinstance(pipe.transformer, HiDreamImageTransformer2DModel)
    return BlockAdapter(
        pipe=pipe,
        transformer=pipe.transformer,
        blocks=[
            pipe.transformer.double_stream_blocks,
            pipe.transformer.single_stream_blocks,
        ],
        forward_pattern=[
            ForwardPattern.Pattern_0,
            ForwardPattern.Pattern_3,
        ],
        # NOTE: Setup your custom patch functor here.
        patch_functor=HiDreamPatchFunctor(),
        **kwargs,
    )
```

Finally, you can call the `cache_dit.summary()` function on a pipeline after its completed inference to get the cache acceleration details.

```python
stats = cache_dit.summary(pipe)
```

```python
⚡️Cache Steps and Residual Diffs Statistics: QwenImagePipeline

| Cache Steps | Diffs Min | Diffs P25 | Diffs P50 | Diffs P75 | Diffs P95 | Diffs Max |
|-------------|-----------|-----------|-----------|-----------|-----------|-----------|
| 23          | 0.045     | 0.084     | 0.114     | 0.147     | 0.241     | 0.297     |
```

## DBCache: Dual Block Cache  

![](https://github.com/vipshop/cache-dit/raw/main/assets/dbcache-v1.png)

DBCache (Dual Block Caching) supports different configurations of compute blocks (F8B12, etc.) to enable a balanced trade-off between performance and precision.
- Fn_compute_blocks: Specifies that DBCache uses the **first n** Transformer blocks to fit the information at time step t, enabling the calculation of a more stable L1 diff and delivering more accurate information to subsequent blocks.
- Bn_compute_blocks: Further fuses approximate information in the **last n** Transformer blocks to enhance prediction accuracy. These blocks act as an auto-scaler for approximate hidden states that use residual cache.

```python
import cache_dit
from diffusers import FluxPipeline

pipe_or_adapter = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
).to("cuda")

# Default options, F8B0, 8 warmup steps, and unlimited cached 
# steps for good balance between performance and precision
cache_dit.enable_cache(pipe_or_adapter)

# Custom options, F8B8, higher precision
from cache_dit import BasicCacheConfig

cache_dit.enable_cache(
    pipe_or_adapter,
    cache_config=BasicCacheConfig(
        max_warmup_steps=8,  # steps do not cache
        max_cached_steps=-1, # -1 means no limit
        Fn_compute_blocks=8, # Fn, F8, etc.
        Bn_compute_blocks=8, # Bn, B8, etc.
        residual_diff_threshold=0.12,
    ),
)
```  
Check the [DBCache](https://github.com/vipshop/cache-dit/blob/main/docs/DBCache.md) and [User Guide](https://github.com/vipshop/cache-dit/blob/main/docs/User_Guide.md#dbcache) docs for more design details.

## TaylorSeer Calibrator

The [TaylorSeers](https://huggingface.co/papers/2503.06923) algorithm further improves the precision of DBCache in cases where the cached steps are large (Hybrid TaylorSeer + DBCache). At timesteps with significant intervals, the feature similarity in diffusion models decreases substantially, significantly harming the generation quality. 

TaylorSeer employs a differential method to approximate the higher-order derivatives of features and predict features in future timesteps with Taylor series expansion. The TaylorSeer implemented in CacheDiT supports both hidden states and residual cache types. F_pred can be a residual cache or a hidden-state cache.

```python
from cache_dit import BasicCacheConfig, TaylorSeerCalibratorConfig

cache_dit.enable_cache(
    pipe_or_adapter,
    # Basic DBCache w/ FnBn configurations
    cache_config=BasicCacheConfig(
        max_warmup_steps=8,  # steps do not cache
        max_cached_steps=-1, # -1 means no limit
        Fn_compute_blocks=8, # Fn, F8, etc.
        Bn_compute_blocks=8, # Bn, B8, etc.
        residual_diff_threshold=0.12,
    ),
    # Then, you can use the TaylorSeer Calibrator to approximate 
    # the values in cached steps, taylorseer_order default is 1.
    calibrator_config=TaylorSeerCalibratorConfig(
        taylorseer_order=1,
    ),
)
``` 

> [!TIP]  
> The `Bn_compute_blocks` parameter of DBCache can be set to `0` if you use TaylorSeer as the calibrator for approximate hidden states. DBCache's `Bn_compute_blocks` also acts as a calibrator, so you can choose either `Bn_compute_blocks` > 0 or TaylorSeer. We recommend using the configuration scheme of TaylorSeer + DBCache FnB0.

## Hybrid Cache CFG

CacheDiT supports caching for CFG (classifier-free guidance). For models that fuse CFG and non-CFG into a single forward step, or models that do not include CFG in the forward step, please set `enable_separate_cfg` parameter  to `False (default, None)`. Otherwise, set it to `True`. 

```python
from cache_dit import BasicCacheConfig

cache_dit.enable_cache(
    pipe_or_adapter, 
    cache_config=BasicCacheConfig(
        ...,
        # For example, set it as True for Wan 2.1, Qwen-Image 
        # and set it as False for FLUX.1, HunyuanVideo, etc.
        enable_separate_cfg=True,
    ),
)
```

## torch.compile

CacheDiT is designed to work with torch.compile for even better performance. Call `torch.compile` after enabling the cache.

```python
cache_dit.enable_cache(pipe)

# Compile the Transformer module
pipe.transformer = torch.compile(pipe.transformer)
```

If you're using CacheDiT with dynamic input shapes, consider increasing the `recompile_limit` of `torch._dynamo`. Otherwise, the `recompile_limit` error may be triggered, causing the module to fall back to eager mode. 

```python
torch._dynamo.config.recompile_limit = 96  # default is 8
torch._dynamo.config.accumulated_recompile_limit = 2048  # default is 256
```

Please check [perf.py](https://github.com/vipshop/cache-dit/blob/main/bench/perf.py) for more details.

