bb4940d837
Only enable attention upcasting on models that actually need it.
2024-05-14 17:00:50 -04:00
2a813c3b09
Switch some more prints to logging.
2024-03-11 16:34:58 -04:00
cb7c3a2921
Allow image_only_indicator to be None.
2024-02-29 13:11:30 -05:00
b3e97fc714
Koala 700M and 1B support.
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Use the UNET Loader node to load the unet file to use them.
2024-02-28 12:10:11 -05:00
c661a8b118
Don't use numpy for calculating sigmas.
2024-02-07 18:52:51 -05:00
89507f8adf
Remove some unused imports.
2024-01-25 23:42:37 -05:00
8c6493578b
Implement noise augmentation for SD 4X upscale model.
2024-01-03 14:27:11 -05:00
79f73a4b33
Remove useless code.
2024-01-02 01:50:29 -05:00
61b3f15f8f
Fix lowvram mode not working with unCLIP and Revision code.
2023-12-26 05:02:02 -05:00
d0165d819a
Fix SVD lowvram mode.
2023-12-24 07:13:18 -05:00
261bcbb0d9
A few missing comfy ops in the VAE.
2023-12-22 04:05:42 -05:00
77755ab8db
Refactor comfy.ops
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comfy.ops -> comfy.ops.disable_weight_init
This should make it more clear what they actually do.
Some unused code has also been removed.
2023-12-11 23:27:13 -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
af365e4dd1
All the unet ops with weights are now handled by comfy.ops
2023-12-04 03:12:18 -05:00
50dc39d6ec
Clean up the extra_options dict for the transformer patches.
...
Now everything in transformer_options gets put in extra_options.
2023-11-26 03:13:56 -05:00
871cc20e13
Support SVD img2vid model.
2023-11-23 19:41:33 -05:00
72741105a6
Remove useless code.
2023-11-21 17:27:28 -05:00
7e3fe3ad28
Make deep shrink behave like it should.
2023-11-16 15:26:28 -05:00
7ea6bb038c
Print warning when controlnet can't be applied instead of crashing.
2023-11-16 12:57:12 -05:00
94cc718e9c
Add a way to add patches to the input block.
2023-11-14 00:08:12 -05:00
794dd2064d
Fix typo.
2023-11-07 23:41:55 -05:00
a527d0c795
Code refactor.
2023-11-07 19:33:40 -05:00
2a23ba0b8c
Fix unet ops not entirely on GPU.
2023-11-07 04:30:37 -05:00
6ec3f12c6e
Support SSD1B model and make it easier to support asymmetric unets.
2023-10-27 14:45:15 -04:00
d44a2de49f
Make VAE code closer to sgm.
2023-10-17 15:18:51 -04:00
23680a9155
Refactor the attention stuff in the VAE.
2023-10-17 03:19:29 -04:00
9a55dadb4c
Refactor code so model can be a dtype other than fp32 or fp16.
2023-10-13 14:41:17 -04:00
88733c997f
pytorch_attention_enabled can now return True when xformers is enabled.
2023-10-11 21:30:57 -04:00
1a4bd9e9a6
Refactor the attention functions.
...
There's no reason for the whole CrossAttention object to be repeated when
only the operation in the middle changes.
2023-10-11 20:38:48 -04:00
afa2399f79
Add a way to set output block patches to modify the h and hsp.
2023-09-22 20:26:47 -04:00
1938f5c5fe
Add a force argument to soft_empty_cache to force a cache empty.
2023-09-04 00:58:18 -04:00
bed116a1f9
Remove optimization that caused border.
2023-08-29 11:21:36 -04:00
1c794a2161
Fallback to slice attention if xformers doesn't support the operation.
2023-08-27 22:24:42 -04:00
d935ba50c4
Make --bf16-vae work on torch 2.0
2023-08-27 21:33:53 -04:00
cf5ae46928
Controlnet/t2iadapter cleanup.
2023-08-22 01:06:26 -04:00
b80c3276dc
Fix issue with gligen.
2023-08-18 16:32:23 -04:00
d6e4b342e6
Support for Control Loras.
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Control loras are controlnets where some of the weights are stored in
"lora" format: an up and a down low rank matrice that when multiplied
together and added to the unet weight give the controlnet weight.
This allows a much smaller memory footprint depending on the rank of the
matrices.
These controlnets are used just like regular ones.
2023-08-18 11:59:51 -04:00
2b13939044
Remove some useless code.
2023-07-30 14:13:33 -04:00
95d796fc85
Faster VAE loading.
2023-07-29 16:28:30 -04:00
4b957a0010
Initialize the unet directly on the target device.
2023-07-29 14:51:56 -04:00
ddc6f12ad5
Disable autocast in unet for increased speed.
2023-07-05 21:58:29 -04:00
05676942b7
Add some more transformer hooks and move tomesd to comfy_extras.
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Tomesd now uses q instead of x to decide which tokens to merge because
it seems to give better results.
2023-06-24 03:30:22 -04:00
fa28d7334b
Remove useless code.
2023-06-23 12:35:26 -04:00
f87ec10a97
Support base SDXL and SDXL refiner models.
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Large refactor of the model detection and loading code.
2023-06-22 13:03:50 -04:00
ae43f09ef7
All the unet weights should now be initialized with the right dtype.
2023-06-15 18:42:30 -04:00
7bf89ba923
Initialize more unet weights as the right dtype.
2023-06-15 15:00:10 -04:00
e21d9ad445
Initialize transformer unet block weights in right dtype at the start.
2023-06-15 14:29:26 -04:00
21f04fe632
Disable default weight values in unet conv2d for faster loading.
2023-06-14 19:46:08 -04:00
b8636a44aa
Make scaled_dot_product switch to sliced attention on OOM.
2023-05-20 16:01:02 -04:00
797c4e8d3b
Simplify and improve some vae attention code.
2023-05-20 15:07:21 -04:00
comfyanonymous