Spaces:
Runtime error
Runtime error
File size: 3,589 Bytes
e0c66e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import torch
from modules.devices import get_optimal_device
module_in_gpu = None
cpu = torch.device("cpu")
device = gpu = get_optimal_device()
def send_everything_to_cpu():
global module_in_gpu
if module_in_gpu is not None:
module_in_gpu.to(cpu)
module_in_gpu = None
def setup_for_low_vram(sd_model, use_medvram):
parents = {}
def send_me_to_gpu(module, _):
"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
we add this as forward_pre_hook to a lot of modules and this way all but one of them will
be in CPU
"""
global module_in_gpu
module = parents.get(module, module)
if module_in_gpu == module:
return
if module_in_gpu is not None:
module_in_gpu.to(cpu)
module.to(gpu)
module_in_gpu = module
# see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
def first_stage_model_encode_wrap(self, encoder, x):
send_me_to_gpu(self, None)
return encoder(x)
def first_stage_model_decode_wrap(self, decoder, z):
send_me_to_gpu(self, None)
return decoder(z)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
sd_model.to(device)
sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
# register hooks for those the first two models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if use_medvram:
sd_model.model.register_forward_pre_hook(send_me_to_gpu)
else:
diff_model = sd_model.model.diffusion_model
# the third remaining model is still too big for 4 GB, so we also do the same for its submodules
# so that only one of them is in GPU at a time
stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
sd_model.model.to(device)
diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
# install hooks for bits of third model
diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.input_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.output_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
|