704fc78854
Put ROCm version in tuple to make it easier to enable stuff based on it. ( #8348 )
2025-05-30 15:41:02 -04:00
89a84e32d2
Disable initial GPU load when novram is used. ( #8294 )
2025-05-26 16:39:27 -04:00
e5799c4899
Enable pytorch attention by default on AMD gfx1151 ( #8282 )
2025-05-26 04:29:25 -04:00
0b50d4c0db
Add argument to explicitly enable fp8 compute support. ( #8257 )
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This can be used to test if your current GPU/pytorch version supports fp8 matrix mult in combination with --fast or the fp8_e4m3fn_fast dtype.
2025-05-23 17:43:50 -04:00
0a66d4b0af
Per device stream counters for async offload. ( #7873 )
2025-04-29 20:28:52 -04:00
5a50c3c7e5
Fix stream priority to support older pytorch. ( #7856 )
2025-04-28 13:07:21 -04:00
c8cd7ad795
Use stream for casting if enabled. ( #7833 )
2025-04-27 05:38:11 -04:00
0dcc75ca54
Add experimental --async-offload lowvram weight offloading. ( #7820 )
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This should speed up the lowvram mode a bit. It currently is only enabled when --async-offload is used but it will be enabled by default in the future if there are no problems.
2025-04-26 16:11:21 -04:00
2d6805ce57
Add option for using fp8_e8m0fnu for model weights. ( #7733 )
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Seems to break every model I have tried but worth testing?
2025-04-22 06:17:38 -04:00
2222cf67fd
MLU memory optimization ( #7470 )
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Co-authored-by: huzhan <huzhan@cambricon.com >
2025-04-02 19:24:04 -04:00
301e26b131
Add option to store TE in bf16 ( #7461 )
2025-04-01 13:48:53 -04:00
8edc1f44c1
Support more float8 types.
2025-03-25 05:23:49 -04:00
7aceb9f91c
Add --use-flash-attention flag. ( #7223 )
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* Add --use-flash-attention flag.
This is useful on AMD systems, as FA builds are still 10% faster than Pytorch cross-attention.
2025-03-14 03:22:41 -04:00
35504e2f93
Fix.
2025-03-13 15:03:18 -04:00
299436cfed
Print mac version.
2025-03-13 10:05:40 -04:00
0952569493
Fix stable cascade VAE on some lowvram machines.
2025-03-08 20:24:04 -05:00
4d55f16ae8
Use enum list for --fast options ( #7024 )
2025-03-01 02:37:35 -05:00
cf0b549d48
--fast now takes a number as argument to indicate how fast you want it.
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The idea is that you can indicate how much quality vs speed you want.
At the moment:
--fast 2 enables fp16 accumulation if your pytorch supports it.
--fast 5 enables fp8 matrix mult on fp8 models and the optimization above.
--fast without a number enables all optimizations.
2025-02-28 02:48:20 -05:00
eb4543474b
Use fp16 for intermediate for fp8 weights with --fast if supported.
2025-02-28 02:17:50 -05:00
1804397952
Use fp16 if checkpoint weights are fp16 and the model supports it.
2025-02-27 16:39:57 -05:00
89253e9fe5
Support Cambricon MLU ( #6964 )
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Co-authored-by: huzhan <huzhan@cambricon.com >
2025-02-26 20:45:13 -05:00
96d891cb94
Speedup on some models by not upcasting bfloat16 to float32 on mac.
2025-02-24 05:41:32 -05:00
ace899e71a
Prioritize fp16 compute when using allow_fp16_accumulation
2025-02-23 04:45:54 -05:00
072db3bea6
Assume the mac black image bug won't be fixed before v16.
2025-02-21 20:24:07 -05:00
a6deca6d9a
Latest mac still has the black image bug.
2025-02-21 20:14:30 -05:00
41c30e92e7
Let all model memory be offloaded on nvidia.
2025-02-21 06:32:21 -05:00
12da6ef581
Apparently directml supports fp16.
2025-02-20 09:30:24 -05:00
b07258cef2
Fix typo.
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Let me know if this slows things down on 2000 series and below.
2025-02-18 07:28:33 -05:00
31e54b7052
Improve AMD arch detection.
2025-02-17 04:53:40 -05:00
8c0bae50c3
bf16 manual cast works on old AMD.
2025-02-17 04:42:40 -05:00
530412cb9d
Refactor torch version checks to be more future proof.
2025-02-17 04:36:45 -05:00
e2919d38b4
Disable bf16 on AMD GPUs that don't support it.
2025-02-16 05:46:10 -05:00
1cd6cd6080
Disable pytorch attention in VAE for AMD.
2025-02-14 05:42:14 -05:00
d7b4bf21a2
Auto enable mem efficient attention on gfx1100 on pytorch nightly 2.7
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I'm not not sure which arches are supported yet. If you see improvements in
memory usage while using --use-pytorch-cross-attention on your AMD GPU let
me know and I will add it to the list.
2025-02-14 04:18:14 -05:00
8773ccf74d
Better memory estimation for ROCm that support mem efficient attention.
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There is no way to check if the card actually supports it so it assumes
that it does if you use --use-pytorch-cross-attention with yours.
2025-02-13 08:32:36 -05:00
1d5d6586f3
Fix ruff.
2025-02-12 06:49:16 -05:00
35740259de
mix_ascend_bf16_infer_err ( #6794 )
2025-02-12 06:48:11 -05:00
b124256817
Fix for running via DirectML ( #6542 )
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* Fix for running via DirectML
Fix DirectML empty image generation issue with Flux1. add CPU fallback for unsupported path. Verified the model works on AMD GPUs
* fix formating
* update casual mask calculation
2025-02-11 17:11:32 -05:00
af4b7c91be
Make --force-fp16 actually force the diffusion model to be fp16.
2025-02-11 08:33:09 -05:00
43a74c0de1
Allow FP16 accumulation with --fast ( #6453 )
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Currently only applies to PyTorch nightly releases. (>=20250208)
2025-02-08 17:00:56 -05:00
255edf2246
Lower minimum ratio of loaded weights on Nvidia.
2025-01-27 05:26:51 -05:00
67feb05299
Remove redundant code.
2025-01-25 19:04:53 -05:00
d45ebb63f6
Remove old unused function.
2025-01-04 07:20:54 -05:00
9e9c8a1c64
Clear cache as often on AMD as Nvidia.
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I think the issue this was working around has been solved.
If you notice that this change slows things down or causes stutters on
your AMD GPU with ROCm on Linux please report it.
2025-01-02 08:44:16 -05:00
160ca08138
Use python 3.9 in launch test instead of 3.8
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Fix ruff check.
2024-12-26 20:05:54 -05:00
c4bfdba330
Support ascend npu ( #5436 )
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* support ascend npu
Co-authored-by: YukMingLaw <lymmm2@163.com >
Co-authored-by: starmountain1997 <guozr1997@hotmail.com >
Co-authored-by: Ginray <ginray0215@gmail.com >
2024-12-26 19:36:50 -05:00
19a64d6291
Cleanup some mac related code.
2024-12-25 05:32:51 -05:00
b486885e08
Disable bfloat16 on older mac.
2024-12-25 05:18:50 -05:00
0229228f3f
Clean up the VAE dtypes code.
2024-12-25 04:50:34 -05:00
15564688ed
Add a try except block so if torch version is weird it won't crash.
2024-12-23 03:22:48 -05:00
