The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

87 lines
2.3 KiB

CPU = 0
NO_VRAM = 1
LOW_VRAM = 2
NORMAL_VRAM = 3
accelerate_enabled = False
vram_state = NORMAL_VRAM
total_vram_available_mb = -1
import sys
set_vram_to = NORMAL_VRAM
if "--lowvram" in sys.argv:
set_vram_to = LOW_VRAM
if "--novram" in sys.argv:
set_vram_to = NO_VRAM
if set_vram_to != NORMAL_VRAM:
try:
import accelerate
accelerate_enabled = True
vram_state = set_vram_to
except Exception as e:
import traceback
print(traceback.format_exc())
print("ERROR: COULD NOT ENABLE LOW VRAM MODE.")
try:
import torch
total_vram_available_mb = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
except:
pass
total_vram_available_mb = (total_vram_available_mb - 1024) // 2
total_vram_available_mb = int(max(256, total_vram_available_mb))
print("Set vram state to:", ["CPU", "NO VRAM", "LOW VRAM", "NORMAL VRAM"][vram_state])
current_loaded_model = None
model_accelerated = False
def unload_model():
global current_loaded_model
global model_accelerated
if current_loaded_model is not None:
if model_accelerated:
accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
model_accelerated = False
current_loaded_model.model.cpu()
current_loaded_model.unpatch_model()
current_loaded_model = None
def load_model_gpu(model):
global current_loaded_model
global vram_state
global model_accelerated
if model is current_loaded_model:
return
unload_model()
try:
real_model = model.patch_model()
except Exception as e:
model.unpatch_model()
raise e
current_loaded_model = model
if vram_state == CPU:
pass
elif vram_state == NORMAL_VRAM:
model_accelerated = False
real_model.cuda()
else:
if vram_state == NO_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
elif vram_state == LOW_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda")
model_accelerated = True
return current_loaded_model