docs: introduce cache-dit to diffusers #12351

DefTruth · 2025-09-19T04:16:47Z

Hi~, I'm the maintainer of cache-dit. I'd like to introduce cache-dit: A Unified, Flexible and Training-free Cache Acceleration Framework for 🤗Diffusers. 🎉Now, cache-dit covers almost All Diffusers' DiT Pipelines🎉. I think this should be the first cache acceleration system in the community that fully supports 🤗 Diffusers. For more information, please refer to the following details.

@a-r-r-o-w @sayakpaul

A Unified, Flexible and Training-free Cache Acceleration Framework for 🤗Diffusers
♥️ Cache Acceleration with One-line Code ~ ♥️

🔥Wan2.2 MoE | +cache-dit:2.0x↑🎉 | HunyuanVideo | +cache-dit:2.1x↑🎉

🔥Qwen-Image | +cache-dit:1.8x↑🎉 | FLUX.1-dev | +cache-dit:2.1x↑🎉

🔥FLUX-Kontext-dev | Baseline | +cache-dit:1.3x↑🎉 | 1.7x↑🎉 | 2.0x↑ 🎉

🔥Qwen...Lightning | +cache-dit:1.14x↑🎉 | HunyuanImage | +cache-dit:1.7x↑🎉

🔥Qwen-Image-Edit | Input w/o Edit | Baseline | +cache-dit:1.6x↑🎉 | 1.9x↑🎉

🔥HiDream-I1 | +cache-dit:1.9x↑🎉 | CogView4 | +cache-dit:1.4x↑🎉 | 1.7x↑🎉

🔥CogView3 | +cache-dit:1.5x↑🎉 | 2.0x↑🎉| Chroma1-HD | +cache-dit:1.9x↑🎉

🔥Mochi-1-preview | +cache-dit:1.8x↑🎉 | SkyReelsV2 | +cache-dit:1.6x↑🎉

🔥LTX-Video-0.9.7 | +cache-dit:1.7x↑🎉 | CogVideoX1.5 | +cache-dit:2.0x↑🎉

🔥OmniGen-v1 | +cache-dit:1.5x↑🎉 | 3.3x↑🎉 | Lumina2 | +cache-dit:1.9x↑🎉

🔥Allegro | +cache-dit:1.36x↑🎉 | AuraFlow-v0.3 | +cache-dit:2.27x↑🎉

🔥Sana | +cache-dit:1.3x↑🎉 | 1.6x↑🎉| PixArt-Sigma | +cache-dit:2.3x↑🎉

🔥PixArt-Alpha | +cache-dit:1.6x↑🎉 | 1.8x↑🎉| SD 3.5 | +cache-dit:2.5x↑🎉

🔥Asumed | +cache-dit:1.1x↑🎉 | 1.2x↑🎉 | DiT-XL-256 | +cache-dit:1.8x↑🎉

⚙️Installation

You can install the stable release of cache-dit from PyPI:

pip3 install -U cache-dit

Or you can install the latest develop version from GitHub:

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

🔥Supported Pipelines

Currently, cache-dit library supports almost Any Diffusion Transformers (with Transformer Blocks that match the specific Input and Output patterns). Please check 🎉Examples for more details. Here are just some of the tested models listed.

>>> 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*'])

Show all pipelines

🔥Benchmarks

cache-dit will support more mainstream Cache acceleration algorithms in the future. More benchmarks will be released, please stay tuned for update. Here, only the results of some precision and performance benchmarks are presented. The test dataset is DrawBench. For a complete benchmark, please refer to 📚Benchmarks.

📚Text2Image DrawBench: FLUX.1-dev

Comparisons between different FnBn compute block configurations show that more compute blocks result in higher precision. For example, the F8B0_W8MC0 configuration achieves the best Clip Score (33.007) and ImageReward (1.0333). Device: NVIDIA L20. F: Fn_compute_blocks, B: Bn_compute_blocks, 50 steps.

Config	Clip Score(↑)	ImageReward(↑)	PSNR(↑)	TFLOPs(↓)	SpeedUp(↑)
[FLUX.1-dev]: 50 steps	32.9217	1.0412	INF	3726.87	1.00x
F8B0_W4MC0_R0.08	32.9871	1.0370	33.8317	2064.81	1.80x
F8B0_W4MC2_R0.12	32.9535	1.0185	32.7346	1935.73	1.93x
F8B0_W4MC3_R0.12	32.9234	1.0085	32.5385	1816.58	2.05x
F4B0_W4MC3_R0.12	32.8981	1.0130	31.8031	1507.83	2.47x
F4B0_W4MC4_R0.12	32.8384	1.0065	31.5292	1400.08	2.66x

The comparison between cache-dit: DBCache and algorithms such as Δ-DiT, Chipmunk, FORA, DuCa, TaylorSeer and FoCa is as follows. Now, in the comparison with a speedup ratio less than 3x, cache-dit achieved the best accuracy. Please check 📚How to Reproduce? for more details.

Method	TFLOPs(↓)	SpeedUp(↑)	ImageReward(↑)	Clip Score(↑)
[FLUX.1-dev]: 50 steps	3726.87	1.00×	0.9898	32.404
[FLUX.1-dev]: 60% steps	2231.70	1.67×	0.9663	32.312
Δ-DiT(N=2)	2480.01	1.50×	0.9444	32.273
Δ-DiT(N=3)	1686.76	2.21×	0.8721	32.102
[FLUX.1-dev]: 34% steps	1264.63	3.13×	0.9453	32.114
Chipmunk	1505.87	2.47×	0.9936	32.776
FORA(N=3)	1320.07	2.82×	0.9776	32.266
DBCache(F=4,B=0,W=4,MC=4)	1400.08	2.66×	1.0065	32.838
DBCache+TaylorSeer(F=1,B=0,O=1)	1153.05	3.23×	1.0221	32.819
DuCa(N=5)	978.76	3.80×	0.9955	32.241
TaylorSeer(N=4,O=2)	1042.27	3.57×	0.9857	32.413
DBCache(F=1,B=0,W=4,MC=6)	944.75	3.94×	0.9997	32.849
DBCache+TaylorSeer(F=1,B=0,O=1)	944.75	3.94×	1.0107	32.865
FoCa(N=5): arxiv.2508.16211	893.54	4.16×	1.0029	32.948

Show all comparison

Method	TFLOPs(↓)	SpeedUp(↑)	ImageReward(↑)	Clip Score(↑)
[FLUX.1-dev]: 50 steps	3726.87	1.00×	0.9898	32.404
[FLUX.1-dev]: 60% steps	2231.70	1.67×	0.9663	32.312
Δ-DiT(N=2)	2480.01	1.50×	0.9444	32.273
Δ-DiT(N=3)	1686.76	2.21×	0.8721	32.102
[FLUX.1-dev]: 34% steps	1264.63	3.13×	0.9453	32.114
Chipmunk	1505.87	2.47×	0.9936	32.776
FORA(N=3)	1320.07	2.82×	0.9776	32.266
DBCache(F=4,B=0,W=4,MC=4)	1400.08	2.66×	1.0065	32.838
DuCa(N=5)	978.76	3.80×	0.9955	32.241
TaylorSeer(N=4,O=2)	1042.27	3.57×	0.9857	32.413
DBCache+TaylorSeer(F=1,B=0,O=1)	1153.05	3.23×	1.0221	32.819
DBCache(F=1,B=0,W=4,MC=6)	944.75	3.94×	0.9997	32.849
DBCache+TaylorSeer(F=1,B=0,O=1)	944.75	3.94×	1.0107	32.865
FoCa(N=5): arxiv.2508.16211	893.54	4.16×	1.0029	32.948
[FLUX.1-dev]: 22% steps	818.29	4.55×	0.8183	31.772
FORA(N=4)	967.91	3.84×	0.9730	32.142
ToCa(N=8)	784.54	4.74×	0.9451	31.993
DuCa(N=7)	760.14	4.89×	0.9757	32.066
TeaCache(l=0.8)	892.35	4.17×	0.8683	31.704
DBCache(F=4,B=0,W=4,MC=10)	816.65	4.56x	0.8245	32.191
TaylorSeer(N=5,O=2)	893.54	4.16×	0.9768	32.467
FoCa(N=7): arxiv.2508.16211	670.44	5.54×	0.9891	32.920
FORA(N=7)	670.14	5.55×	0.7418	31.519
ToCa(N=12)	644.70	5.77×	0.7155	31.808
DuCa(N=10)	606.91	6.13×	0.8382	31.759
TeaCache(l=1.2)	669.27	5.56×	0.7394	31.704
DBCache(F=1,B=0,W=4,MC=10)	651.90	5.72x	0.8796	32.318
TaylorSeer(N=7,O=2)	670.44	5.54×	0.9128	32.128
FoCa(N=8): arxiv.2508.16211	596.07	6.24×	0.9502	32.706

NOTE: Except for DBCache, other performance data are referenced from the paper FoCa, arxiv.2508.16211.

📚Text2Image Distillation DrawBench: Qwen-Image-Lightning

Surprisingly, cache-dit: DBCache still works in the extremely few-step distill model. For example, Qwen-Image-Lightning w/ 4 steps, with the F16B16 configuration, the PSNR is 34.8163, the Clip Score is 35.6109, and the ImageReward is 1.2614. It maintained a relatively high precision.

Config	PSNR(↑)	Clip Score(↑)	ImageReward(↑)	TFLOPs(↓)	SpeedUp(↑)
[Lightning]: 4 steps	INF	35.5797	1.2630	274.33	1.00x
F24B24_W2MC1_R0.8	36.3242	35.6224	1.2630	264.74	1.04x
F16B16_W2MC1_R0.8	34.8163	35.6109	1.2614	244.25	1.12x
F12B12_W2MC1_R0.8	33.8953	35.6535	1.2549	234.63	1.17x
F8B8_W2MC1_R0.8	33.1374	35.7284	1.2517	224.29	1.22x
F1B0_W2MC1_R0.8	31.8317	35.6651	1.2397	206.90	1.33x

🎉Unified Cache APIs

📚Forward Pattern Matching

Currently, for any Diffusion models with Transformer Blocks that match the specific Input/Output patterns, we can use the Unified Cache APIs from cache-dit, namely, the cache_dit.enable_cache(...) API. The Unified Cache APIs are currently in the experimental phase; please stay tuned for updates. The supported patterns are listed as follows:

♥️Cache Acceleration with One-line Code

In most cases, you only need to call one-line of code, that is cache_dit.enable_cache(...). After this API is called, you just need to call the pipe as normal. The pipe param can be any Diffusion Pipeline. Please refer to Qwen-Image as an example.

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

But in some cases, you may have a modified Diffusion Pipeline or Transformer that is not located in the diffusers library or not officially supported by cache-dit at this time. The BlockAdapter can help you solve this problems. Please refer to 🔥Qwen-Image w/ BlockAdapter as an example.

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,
    ), 
)

For such situations, BlockAdapter can help you quickly apply various cache acceleration features to your own Diffusion Pipelines and Transformers. Please check the 📚BlockAdapter.md for more details.

📚Hybird Forward Pattern

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 can also help you solve this problem. Please refer to 📚FLUX.1 as an example.

# For diffusers <= 0.34.0, FLUX.1 transformer_blocks and 
# single_transformer_blocks have different forward patterns.
cache_dit.enable_cache(
    BlockAdapter(
        pipe=pipe, # FLUX.1, etc.
        transformer=pipe.transformer,
        blocks=[
            pipe.transformer.transformer_blocks,
            pipe.transformer.single_transformer_blocks,
        ],
        forward_pattern=[
            ForwardPattern.Pattern_1,
            ForwardPattern.Pattern_3,
        ],
    ),
)

Even sometimes you have more complex cases, such as Wan 2.2 MoE, which has more than one Transformer (namely transformer and transformer_2) in its structure. Fortunately, cache-dit can also handle this situation very well. Please refer to 📚Wan 2.2 MoE as an example.

from cache_dit import ForwardPattern, BlockAdapter, ParamsModifier

cache_dit.enable_cache(
    BlockAdapter(
        pipe=pipe,
        transformer=[
            pipe.transformer,
            pipe.transformer_2,
        ],
        blocks=[
            pipe.transformer.blocks,
            pipe.transformer_2.blocks,
        ],
        forward_pattern=[
            ForwardPattern.Pattern_2,
            ForwardPattern.Pattern_2,
        ],
        # Setup different cache params for each 'blocks'. You can 
        # pass any specific cache params to ParamModifier, the old 
        # value will be overwrite by the new one.
        params_modifiers=[
            ParamsModifier(
                max_warmup_steps=4,
                max_cached_steps=8,
            ),
            ParamsModifier(
                max_warmup_steps=2,
                max_cached_steps=20,
            ),
        ],
        has_separate_cfg=True,
    ),
)

📚Implement Patch Functor

For any PATTERN not {0...5}, we introduced the simple abstract concept of Patch Functor. Users can implement a subclass of Patch Functor to convert an unknown Pattern into a known PATTERN, and for some models, users may also need to fuse the operations within the blocks for loop into block forward.

Some Patch functors have already been provided in cache-dit: 📚HiDreamPatchFunctor, 📚ChromaPatchFunctor, etc. After implementing Patch Functor, users need to set the patch_functor property of BlockAdapter.

@BlockAdapterRegistry.register("HiDream")
def hidream_adapter(pipe, **kwargs) -> 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,
    )

🤖Cache Acceleration Stats Summary

After finishing each inference of pipe(...), you can call the cache_dit.summary() API on pipe to get the details of the Cache Acceleration Stats for the current inference.

stats = cache_dit.summary(pipe)

You can set details param as True to show more details of cache stats. (markdown table format) Sometimes, this may help you analyze what values of the residual diff threshold would be better.

⚡️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

DBCache: Dual Block Caching for Diffusion Transformers. Different configurations of compute blocks (F8B12, etc.) can be customized in DBCache, enabling a balanced trade-off between performance and precision. Moreover, it can be entirely training-free. Please check DBCache.md docs for more design details.

Fn: 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: 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.

import cache_dit
from diffusers import FluxPipeline

pipe = 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)

# Custom options, F8B8, higher precision
cache_dit.enable_cache(
    pipe,
    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,
)

DBCache, L20x1 , Steps: 28, "A cat holding a sign that says hello world with complex background"

Baseline(L20x1)	F1B0 (0.08)	F1B0 (0.20)	F8B8 (0.15)	F12B12 (0.20)	F16B16 (0.20)
24.85s	15.59s	8.58s	15.41s	15.11s	17.74s

🔥Hybrid TaylorSeer

We have supported the TaylorSeers: From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers algorithm to further improve the precision of DBCache in cases where the cached steps are large, namely, Hybrid TaylorSeer + DBCache. At timesteps with significant intervals, the feature similarity in diffusion models decreases substantially, significantly harming the generation quality.

$$ \mathcal{F}_{\text {pred }, m}\left(x_{t-k}^l\right)=\mathcal{F}\left(x_t^l\right)+\sum_{i=1}^m \frac{\Delta^i \mathcal{F}\left(x_t^l\right)}{i!\cdot N^i}(-k)^i $$

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 cache-dit supports both hidden states and residual cache types. That is $\mathcal{F}_{\text {pred }, m}\left(x_{t-k}^l\right)$ can be a residual cache or a hidden-state cache.

cache_dit.enable_cache(
    pipe,
    enable_taylorseer=True,
    enable_encoder_taylorseer=True,
    # Taylorseer cache type cache be hidden_states or residual.
    taylorseer_cache_type="residual",
    # Higher values of order will lead to longer computation time
    taylorseer_order=1, # default is 1.
    max_warmup_steps=3, # prefer: >= order + 1
    residual_diff_threshold=0.12
)s

Important

Please note that if you have used TaylorSeer as the calibrator for approximate hidden states, the Bn param of DBCache can be set to 0. In essence, DBCache's Bn is also act as a calibrator, so you can choose either Bn > 0 or TaylorSeer. We recommend using the configuration scheme of TaylorSeer + DBCache FnB0.

DBCache F1B0 + TaylorSeer, L20x1, Steps: 28,
"A cat holding a sign that says hello world with complex background"

Baseline(L20x1)	F1B0 (0.12)	+TaylorSeer	F1B0 (0.15)	+TaylorSeer	+compile
24.85s	12.85s	12.86s	10.27s	10.28s	8.48s

⚡️Hybrid Cache CFG

cache-dit 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 (classifier-free guidance) in the forward step, please set enable_separate_cfg param to False (default, None). Otherwise, set it to True. For examples:

cache_dit.enable_cache(
    pipe, 
    ...,
    # CFG: classifier free guidance or not
    # For model that fused CFG and non-CFG into single forward step,
    # should set enable_separate_cfg as False. For example, set it as True 
    # for Wan 2.1/Qwen-Image and set it as False for FLUX.1, HunyuanVideo, 
    # CogVideoX, Mochi, LTXVideo, Allegro, CogView3Plus, EasyAnimate, SD3, etc.
    enable_separate_cfg=True, # Wan 2.1, Qwen-Image, CogView4, Cosmos, SkyReelsV2, etc.
    # Compute cfg forward first or not, default False, namely, 
    # 0, 2, 4, ..., -> non-CFG step; 1, 3, 5, ... -> CFG step.
    cfg_compute_first=False,
    # Compute separate diff values for CFG and non-CFG step, 
    # default True. If False, we will use the computed diff from 
    # current non-CFG transformer step for current CFG step.
    cfg_diff_compute_separate=True,
)

⚙️Torch Compile

By the way, cache-dit is designed to work compatibly with torch.compile. You can easily use cache-dit with torch.compile to further achieve a better performance. For example:

cache_dit.enable_cache(pipe)

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

However, users intending to use cache-dit for DiT with dynamic input shapes should 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.

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

Please check perf.py for more details.

🛠Metrics CLI

You can utilize the APIs provided by cache-dit to quickly evaluate the accuracy losses caused by different cache configurations. For example:

from cache_dit.metrics import compute_psnr
from cache_dit.metrics import compute_ssim
from cache_dit.metrics import compute_fid
from cache_dit.metrics import compute_lpips
from cache_dit.metrics import compute_clip_score
from cache_dit.metrics import compute_image_reward

psnr,       n = compute_psnr("true.png", "test.png") # Num: n
psnr,       n = compute_psnr("true_dir", "test_dir")
ssim,       n = compute_ssim("true_dir", "test_dir")
fid,        n = compute_fid("true_dir", "test_dir")
lpips,      n = compute_lpips("true_dir", "test_dir")
clip_score, n = compute_clip_score("DrawBench200.txt", "test_dir")
reward,     n = compute_image_reward("DrawBench200.txt", "test_dir")

Please check test_metrics.py for more details. Or, you can use cache-dit-metrics-cli tool. For examples:

cache-dit-metrics-cli -h  # show usage
# all: PSNR, FID, SSIM, MSE, ..., etc.
cache-dit-metrics-cli all  -i1 true.png -i2 test.png  # image
cache-dit-metrics-cli all  -i1 true_dir -i2 test_dir  # image dir

©️Citations

@misc{cache-dit@2025,
  title={cache-dit: A Unified, Flexible and Training-free Cache Acceleration Framework for Diffusers.},
  url={https://github.com/vipshop/cache-dit.git},
  note={Open-source software available at https://github.com/vipshop/cache-dit.git},
  author={vipshop.com},
  year={2025}
}

sayakpaul · 2025-09-19T05:06:33Z

Thanks for the very comprehensive PR description. Based on the results, I think it might be cool to try to support this caching technique more natively in Diffusers. We have a few and here is the doc: https://huggingface.co/docs/diffusers/main/en/optimization/cache. Would you be willing to contribute that?

DefTruth · 2025-09-19T05:17:09Z

Thanks for the very comprehensive PR description. Based on the results, I think it might be cool to try to support this caching technique more natively in Diffusers. We have a few and here is the doc: https://huggingface.co/docs/diffusers/main/en/optimization/cache. Would you be willing to contribute that?

@sayakpaul yes, my pleasure~ However, I don't think this is something that can be accomplished in the short term. Many designs in cache-dit (such as BlockAdapter) may not be directly integrated into Diffusers. Meanwhile, cache-dit incorporates various hybrid strategies for Cache algorithms and will continuously integrate new ones like TaylorSeer and FoCa, which may also make it difficult to maintain full synchronization with Diffusers. In the short term, I believe it would be more appropriate to consider adding cache-dit to the Community Optimization.

sayakpaul · 2025-09-19T05:26:17Z

Sure upto you. But having the integration natively supported also helps with adoption, testing, and maintainence.

DefTruth · 2025-09-19T05:43:38Z

Sure upto you. But having the integration natively supported also helps with adoption, testing, and maintainence.

I will submit a new PR to support DBCache + TaylorSeer instead of the entire cache-dit, which I think will be easier to implement. I can find some references, such as: https://github.com/huggingface/diffusers/blob/main/src/diffusers/hooks/first_block_cache.py and https://github.com/huggingface/diffusers/blob/main/src/diffusers/hooks/faster_cache.py

Another way is to integrate the codebase of cache-dit into diffusers, but this may be quite inelegant and will also involve a great deal of modifications.

DefTruth · 2025-09-19T10:40:38Z

Let's hold off for now. The cache-dit team is simplifying the API, and we'll resubmit the PR once that's done.

sayakpaul · 2025-09-19T12:08:03Z

No problem but please give us a heads up once you feel it's ready :) More than happy to revive that.

Also, I am happy to help with reviewing cache-dit if you think that would be beneficial :)

DefTruth added 2 commits September 19, 2025 04:08

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sayakpaul requested a review from stevhliu September 19, 2025 05:05

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DefTruth closed this Sep 19, 2025

DefTruth deleted the intro-cache-dit branch September 22, 2025 07:16

DefTruth mentioned this pull request Sep 22, 2025

Introduce cache-dit to community optimization #12366

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docs: introduce cache-dit to diffusers #12351

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@a-r-r-o-w @sayakpaul

📖Contents

⚙️Installation

🔥Supported Pipelines

🔥Benchmarks

📚Text2Image DrawBench: FLUX.1-dev

📚Text2Image Distillation DrawBench: Qwen-Image-Lightning

🎉Unified Cache APIs

📚Forward Pattern Matching

♥️Cache Acceleration with One-line Code

🔥Automatic Block Adapter

📚Hybird Forward Pattern

📚Implement Patch Functor

🤖Cache Acceleration Stats Summary

⚡️DBCache: Dual Block Cache

🔥Hybrid TaylorSeer

⚡️Hybrid Cache CFG

⚙️Torch Compile

🛠Metrics CLI

©️Citations

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