43b300498f
Remove explicit gc.collect() after transferring models from device to CPU. I'm not sure why this was there in the first place, but it was taking a significant amount of time (up to ~1sec in my tests).
2023-11-03 13:50:40 -07:00
3781e56e57
Add log_memory_usage param to ModelCache.
2023-11-02 19:20:37 -07:00
40f9e49b5e
Demote model cache logs from warning to debug based on the conversation here: https://discord.com/channels/1020123559063990373/1049495067846524939/1161647290189090816
2023-10-11 12:02:46 -04:00
58b56e9b1e
Add a skip_torch_weight_init() context manager to improve model load times (from disk).
2023-10-09 14:12:56 -04:00
2479a59e5e
Re-enable garbage collection in model cache MemorySnapshots.
2023-10-03 15:18:47 -04:00
22a84930f6
Disable garbage collection in ModelCache calls to MemorySnapshot in order minimize snapshot overhead.
2023-10-03 14:25:34 -04:00
5915a4a51c
Minor fixes.
2023-10-03 14:25:34 -04:00
4580ba0d87
Remove logic to update model cache size estimates dynamically.
2023-10-03 14:25:34 -04:00
b9fd2e9e76
Improve get_pretty_snapshot_diff(...) message formatting.
2023-10-03 14:25:34 -04:00
2a3c0ab5d2
Move MemorySnapshot to its own file.
2023-10-03 14:25:34 -04:00
7d65555a5a
Fix type error in torch device comparison.
2023-10-03 14:25:34 -04:00
123f2b2dbc
Update cache model size estimates based on changes in VRAM when moving models to/from CUDA.
2023-10-03 14:25:34 -04:00
1e4e42556e
Update model cache device comparison to treat 'cuda' and 'cuda:0' as the same device type.
2023-10-03 14:25:34 -04:00
1f6699ac43
Consolidate all model.to(...) calls in the model cache to use a utility function with better logging.
2023-10-03 14:25:34 -04:00
ace8665411
Add warning log if moving a model from cuda to cpu causes unexpected change in VRAM usage.
2023-10-03 14:25:34 -04:00
7fa5bae8fd
Add warning log if moving model from RAM to VRAM causes an unexpected change in VRAM usage.
2023-10-03 14:25:34 -04:00
f9faca7c91
Add warning log if model mis-reports its required cache memory before load from disk.
2023-10-03 14:25:34 -04:00
594fd3ba6d
Add debug logging of changes in RAM and VRAM for all model cache operations.
2023-10-03 14:25:34 -04:00
44d68f5ed5
Auto-format model_cache.py.
2023-10-03 14:25:34 -04:00
2f5e923008
Removed duplicate import in model_cache.py
2023-09-13 19:33:43 -04:00
b7296000e4
made MPS calls conditional on MPS actually being the chosen device with backend available
2023-09-13 19:33:43 -04:00
fab055995e
Add empty_cache() for MPS hardware.
2023-09-13 19:33:43 -04:00
e88d7c242f
isort wip 3
2023-09-12 13:01:58 -04:00
537ae2f901
Resolving merge conflicts for flake8
2023-08-18 15:52:04 +10:00
bb1b8ceaa8
Update invokeai/backend/model_management/model_cache.py
...
Co-authored-by: StAlKeR7779 <stalkek7779@yandex.ru >
2023-08-16 08:48:44 -04:00
f9958de6be
added memory used to load models
2023-08-15 21:56:19 -04:00
ec10aca91e
report RAM and RAM cache statistics
2023-08-15 21:00:30 -04:00
6ad565d84c
folded in changes from 4099
2023-08-04 18:24:47 -04:00
1ac14a1e43
add sdxl lora support
2023-08-04 11:44:56 -04:00
f5ac73b091
Merge branch 'main' into feat/onnx
2023-07-31 10:58:40 -04:00
e20c4dc1e8
blackified
2023-07-30 08:17:10 -04:00
844578ab88
fix lora loading crash
2023-07-30 07:57:10 -04:00
99daa97978
more refactoring; fixed place where rel conversion missed
2023-07-29 13:00:07 -04:00
da751da3dd
Merge branch 'main' into feat/onnx
2023-07-28 09:59:35 -04:00
2b7b3dd4ba
Run python black
2023-07-28 09:46:44 -04:00
bfdc8c80f3
Testing caching onnx sessions
2023-07-27 14:13:29 -04:00
218b6d0546
Apply black
2023-07-27 10:54:01 -04:00
bda0000acd
Cleanup vram after models offloading, tweak to cleanup local variable references on ram offload
2023-07-18 23:21:18 +03:00
bc11296a5e
Disable lazy offloading on disabled vram cache, move resulted tensors to cpu(to not stack vram tensors in cache), fix - text encoder not freed(detach)
2023-07-18 16:20:25 +03:00
dab03fb646
rename gpu_mem_reserved to max_vram_cache_size
...
To be consistent with max_cache_size, the amount of memory to hold in
VRAM for model caching is now controlled by the max_vram_cache_size
configuration parameter.
2023-07-11 15:25:39 -04:00
d32f9f7cb0
reverse logic of gpu_mem_reserved
...
- gpu_mem_reserved now indicates the amount of VRAM that will be reserved
for model caching (similar to max_cache_size).
2023-07-11 15:16:40 -04:00
5759a390f9
introduce gpu_mem_reserved configuration parameter
2023-07-09 18:35:04 -04:00
8d7dba937d
fix undefined variable
2023-07-09 14:37:45 -04:00
d6cb0e54b3
don't unload models from GPU until the space is needed
2023-07-09 14:26:30 -04:00
0a6dccd607
expose max_cache_size to invokeai-configure interface
2023-07-05 20:59:14 -04:00
6935858ef3
add debugging messages to aid in memory leak tracking
2023-07-02 13:34:53 -04:00
0f02915012
remove hardcoded cuda device in model manager init
2023-07-01 21:15:42 -04:00
740c05a0bb
Save models on rescan, uncache model on edit/delete, fixes
2023-06-14 03:12:12 +03:00
e7db6d8120
Fix ckpt and vae conversion, migrate script, remove sd2-base
2023-06-13 18:05:12 +03:00
36eb1bd893
Fixes
2023-06-12 16:14:09 +03:00
