Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

hacked in stable-fast; can generate one image before crashing

Browse Source
This commit is contained in:
Lincoln Stein
2023-12-20 22:11:16 -05:00
parent 8d2952695d
commit 9c1d250665
1 changed files with 24 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
invokeai/backend/model_management/model_cache.py
View File

@ -39,6 +39,14 @@ from .models import BaseModelType, ModelBase, ModelType, SubModelType
if choose_torch_device() == torch.device("mps"): if choose_torch_device() == torch.device("mps"):
from torch import mps from torch import mps
sfast_available = True
if sfast_available:
from sfast.compilers.diffusion_pipeline_compiler import (compile,
compile_unet,
compile_vae,
CompilationConfig
)
# Maximum size of the cache, in gigs # Maximum size of the cache, in gigs
# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
DEFAULT_MAX_CACHE_SIZE = 6.0 DEFAULT_MAX_CACHE_SIZE = 6.0
@ -276,6 +284,8 @@ class ModelCache(object):
self._cache_stack.remove(key) self._cache_stack.remove(key)
self._cache_stack.append(key) self._cache_stack.append(key)
if sfast_available and submodel:
cache_entry.model = self._compile_model(cache_entry.model, submodel)
return self.ModelLocker(self, key, cache_entry.model, gpu_load, cache_entry.size) return self.ModelLocker(self, key, cache_entry.model, gpu_load, cache_entry.size)
def _move_model_to_device(self, key: str, target_device: torch.device): def _move_model_to_device(self, key: str, target_device: torch.device):
@ -322,6 +332,20 @@ class ModelCache(object):
f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}" f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
) )
def _compile_model(self, model, model_type):
config = CompilationConfig.Default()
config.enable_xformers = True
config.enable_triton = True
config.enable_jit_freeze = True
config.enable_cuda_graph = True
if model_type == SubModelType("unet"):
return compile_unet(model, config)
elif model_type == SubModelType("vae"):
return compile_vae(model, config)
else:
return model
class ModelLocker(object): class ModelLocker(object):
def __init__(self, cache, key, model, gpu_load, size_needed): def __init__(self, cache, key, model, gpu_load, size_needed):
""" """