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TeaCache4Lumina2/README.md
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<!-- ## **TeaCache4LuminaT2X** -->
# TeaCache4Lumina2
[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [Lumina-Image-2.0](https://github.com/Alpha-VLLM/Lumina-Image-2.0) without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Image-2.0 with various rel_l1_thresh values: 0 (original), 0.1 (1.05x speedup), 0.2 (1.15x speedup), 0.3 (1.25x speedup).
[TeaCache](https://github.com/LiewFeng/TeaCache) can speedup [Lumina-Image-2.0](https://github.com/Alpha-VLLM/Lumina-Image-2.0) without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Image-2.0 with various rel_l1_thresh values: 0 (original), 0.2 (1.25x speedup), 0.3 (1.5625x speedup), 0.4 (2.0833x speedup), 0.5 (2.5x speedup).
<p align="center">
<img src="https://private-user-images.githubusercontent.com/179383288/447037531-1b80195e-57a4-4f8c-a081-9870a1793d09.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDgxNjU3NTgsIm5iZiI6MTc0ODE2NTQ1OCwicGF0aCI6Ii8xNzkzODMyODgvNDQ3MDM3NTMxLTFiODAxOTVlLTU3YTQtNGY4Yy1hMDgxLTk4NzBhMTc5M2QwOS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwNTI1JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDUyNVQwOTMwNThaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1iNzU3ODQyNDY2OTQxMzJiMjVkMjVhMGMzOTk3MGY5MTQwYzdkNTI1NGQzNTcwZTU5YjQ4NTY2NWY0NTI1ZjZmJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.3WarqdpZ9mLUJynP4a0IpB6aPCIojPku0d2ClvdgOrA" width="150" style="margin: 5px;">
<img src="https://private-user-images.githubusercontent.com/179383288/447037765-9491dee3-9cb7-4807-8153-7cfaec8582f0.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDgxNjU3NTgsIm5iZiI6MTc0ODE2NTQ1OCwicGF0aCI6Ii8xNzkzODMyODgvNDQ3MDM3NzY1LTk0OTFkZWUzLTljYjctNDgwNy04MTUzLTdjZmFlYzg1ODJmMC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwNTI1JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDUyNVQwOTMwNThaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0zNjcxZjBhOWZmMzdkNDE1N2EwMjNiNmI3NWQyZTlmYTdjZmY2NTA1YTcwNmFjOTE2ZmQ0NDdlNjcwNmJhZmY5JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.g5LF6WCXqMzSgMtq1IlLxZiJuzGPtQErzik969D9r84" width="150" style="margin: 5px;">
<img src="https://private-user-images.githubusercontent.com/179383288/447037881-fcd39c74-ecb2-4e99-ab63-8a6325536193.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDgxNjU3NTgsIm5iZiI6MTc0ODE2NTQ1OCwicGF0aCI6Ii8xNzkzODMyODgvNDQ3MDM3ODgxLWZjZDM5Yzc0LWVjYjItNGU5OS1hYjYzLThhNjMyNTUzNjE5My5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwNTI1JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDUyNVQwOTMwNThaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1kYTU2MjQwMDMzOGZjYmNhNmY4MWU2YjBlY2M3MDc5ODAwMDQ3ODdjNTlhZDc5MThiN2Q1OGZlMTdhYjAzZDAyJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.lME_Lu6NvY8Ju56yr9wxxH9UOSrhfleQA_DmSkwKk5Y" width="150" style="margin: 5px;">
<img src="https://private-user-images.githubusercontent.com/179383288/447037987-ca788c20-a03a-4660-a646-3f454bf4b19f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDgxNjU3NTgsIm5iZiI6MTc0ODE2NTQ1OCwicGF0aCI6Ii8xNzkzODMyODgvNDQ3MDM3OTg3LWNhNzg4YzIwLWEwM2EtNDY2MC1hNjQ2LTNmNDU0YmY0YjE5Zi5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwNTI1JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDUyNVQwOTMwNThaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1mOGM5YmEyN2NhMGE0MzZiMjU4MWVkZjYzMTdkNTdmM2E1MTBmYTE5MTgzZmM2ZTQ1YWZlYTc1ZjRiYWNhN2ZjJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9._AN5xub4BCEBCE-DPvxyrVZX7ssg-a5nq7ayg0xiPRs" width="150" style="margin: 5px;">
<img src="https://github.com/user-attachments/assets/d2c87b99-e4ac-4407-809a-caf9750f41ef" width="150" style="margin: 5px;">
<img src="https://github.com/user-attachments/assets/411ff763-9c31-438d-8a9b-3ec5c88f6c27" width="150" style="margin: 5px;">
<img src="https://github.com/user-attachments/assets/e57dfb60-a07f-4e17-837e-e46a69d8b9c0" width="150" style="margin: 5px;">
<img src="https://github.com/user-attachments/assets/6e3184fe-e31a-452c-a447-48d4b74fcc10" width="150" style="margin: 5px;">
<img src="https://github.com/user-attachments/assets/d6a52c4c-bd22-45c0-9f40-00a2daa85fc8" width="150" style="margin: 5px;">
</p>
## 📈 Inference Latency Comparisons on a 4070 laptop(size 1024 x 1536)
## 📈 Inference Latency Comparisons on a single 4090 (step 50)
| Lumina-Image-2.0 | TeaCache (0.1) | TeaCache (0.2) | TeaCache (0.3) |
|:---------------------------:|:-----------------------------:|:--------------------:|:---------------------:|
| ~97.74s | ~93.19s | ~84.72s | ~78.43s |
| Lumina-Image-2.0 | TeaCache (0.2) | TeaCache (0.3) | TeaCache (0.4) | TeaCache (0.5) |
|:-------------------------:|:---------------------------:|:--------------------:|:---------------------:|:---------------------:|
| ~25 s | ~20 s | ~16 s | ~12 s | ~10 s |
## Installation