0ec513d877
Add a --force-channels-last to inference models in channel last mode.
2024-06-15 01:08:12 -04:00
5eb98f0092
Exempt IPEX from non_blocking previews fixing segmentation faults. ( #3708 )
2024-06-13 18:51:14 -04:00
0e49211a11
Load the SD3 T5xxl model in the same dtype stored in the checkpoint.
2024-06-11 17:03:26 -04:00
104fcea0c8
Add function to get the list of currently loaded models.
2024-06-05 23:25:16 -04:00
b1fd26fe9e
pytorch xpu should be flash or mem efficient attention?
2024-06-04 17:44:14 -04:00
b249862080
Add an annoying print to a function I want to remove.
2024-06-01 12:47:31 -04:00
bf3e334d46
Disable non_blocking when --deterministic or directml.
2024-05-30 11:07:38 -04:00
0920e0e5fe
Remove some unused imports.
2024-05-27 19:08:27 -04:00
6c23854f54
Fix OSX latent2rgb previews.
2024-05-22 13:56:28 -04:00
8508df2569
Work around black image bug on Mac 14.5 by forcing attention upcasting.
2024-05-21 16:56:33 -04:00
09e069ae6c
Log the pytorch version.
2024-05-20 06:22:29 -04:00
19300655dd
Don't automatically switch to lowvram mode on GPUs with low memory.
2024-05-17 00:31:32 -04:00
f509c6fe21
Fix Intel GPU memory allocation accuracy and documentation update. ( #3459 )
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* Change calculation of memory total to be more accurate, allocated is actually smaller than reserved.
* Update README.md install documentation for Intel GPUs.
2024-05-12 06:36:30 -04:00
fa6dd7e5bb
Fix lowvram issue with saving checkpoints.
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The previous fix didn't cover the case where the model was loaded in
lowvram mode right before.
2024-05-12 06:13:45 -04:00
49c20cdc70
No longer necessary.
2024-05-12 05:34:43 -04:00
e1489ad257
Fix issue with lowvram mode breaking model saving.
2024-05-11 21:55:20 -04:00
a56d02efc7
Change torch.xpu to ipex.optimize, xpu device initialization and remove workaround for text node issue from older IPEX. ( #3388 )
2024-05-02 03:26:50 -04:00
258dbc06c3
Fix some memory related issues.
2024-04-14 12:08:58 -04:00
0a03009808
Fix issue with controlnet models getting loaded multiple times.
2024-04-06 18:38:39 -04:00
5d8898c056
Fix some performance issues with weight loading and unloading.
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Lower peak memory usage when changing model.
Fix case where model weights would be unloaded and reloaded.
2024-03-28 18:04:42 -04:00
c6de09b02e
Optimize memory unload strategy for more optimized performance.
2024-03-24 02:36:30 -04:00
4b9005e949
Fix regression with model merging.
2024-03-20 13:56:12 -04:00
c18a203a8a
Don't unload model weights for non weight patches.
2024-03-20 02:27:58 -04:00
db8b59ecff
Lower memory usage for loras in lowvram mode at the cost of perf.
2024-03-13 20:07:27 -04:00
0ed72befe1
Change log levels.
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Logging level now defaults to info. --verbose sets it to debug.
2024-03-11 13:54:56 -04:00
65397ce601
Replace prints with logging and add --verbose argument.
2024-03-10 12:14:23 -04:00
dce3555339
Add some tesla pascal GPUs to the fp16 working but slower list.
2024-03-02 17:16:31 -05:00
88f300401c
Enable fp16 by default on mps.
2024-02-19 12:00:48 -05:00
929e266f3e
Manual cast for bf16 on older GPUs.
2024-02-17 09:01:17 -05:00
0b3c50480c
Make --force-fp32 disable loading models in bf16.
2024-02-16 23:01:54 -05:00
f83109f09b
Stable Cascade Stage C.
2024-02-16 10:55:08 -05:00
aeaeca10bd
Small refactor of is_device_* functions.
2024-02-15 21:10:10 -05:00
66e28ef45c
Don't use is_bf16_supported to check for fp16 support.
2024-02-04 20:53:35 -05:00
24129d78e6
Speed up SDXL on 16xx series with fp16 weights and manual cast.
2024-02-04 13:23:43 -05:00
4b0239066d
Always use fp16 for the text encoders.
2024-02-02 10:02:49 -05:00
f9e55d8463
Only auto enable bf16 VAE on nvidia GPUs that actually support it.
2024-01-15 03:10:22 -05:00
1b103e0cb2
Add argument to run the VAE on the CPU.
2023-12-30 05:49:07 -05:00
e1e322cf69
Load weights that can't be lowvramed to target device.
2023-12-28 21:41:10 -05:00
a252963f95
--disable-smart-memory now unloads everything like it did originally.
2023-12-23 04:25:06 -05:00
36a7953142
Greatly improve lowvram sampling speed by getting rid of accelerate.
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Let me know if this breaks anything.
2023-12-22 14:38:45 -05:00
2f9d6a97ec
Add --deterministic option to make pytorch use deterministic algorithms.
2023-12-17 16:59:21 -05:00
b0aab1e4ea
Add an option --fp16-unet to force using fp16 for the unet.
2023-12-11 18:36:29 -05:00
ba07cb748e
Use faster manual cast for fp8 in unet.
2023-12-11 18:24:44 -05:00
57926635e8
Switch text encoder to manual cast.
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Use fp16 text encoder weights for CPU inference to lower memory usage.
2023-12-10 23:00:54 -05:00
340177e6e8
Disable non blocking on mps.
2023-12-10 01:30:35 -05:00
9ac0b487ac
Make --gpu-only put intermediate values in GPU memory instead of cpu.
2023-12-08 02:35:45 -05:00
2db86b4676
Slightly faster lora applying.
2023-12-06 05:13:14 -05:00
ca82ade765
Use .itemsize to get dtype size for fp8.
2023-12-04 11:52:06 -05:00
31b0f6f3d8
UNET weights can now be stored in fp8.
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--fp8_e4m3fn-unet and --fp8_e5m2-unet are the two different formats
supported by pytorch.
2023-12-04 11:10:00 -05:00
0cf4e86939
Add some command line arguments to store text encoder weights in fp8.
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Pytorch supports two variants of fp8:
--fp8_e4m3fn-text-enc (the one that seems to give better results)
--fp8_e5m2-text-enc
2023-11-17 02:56:59 -05:00
comfyanonymous
Simon Lui