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Add check for psutil

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brkirch 2022-10-10 23:55:48 -04:00 committed by AUTOMATIC1111
parent c0484f1b98
commit 98fd5cde72
2 changed files with 23 additions and 6 deletions
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10
modules/sd_hijack.py
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@ -10,6 +10,7 @@ from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts from modules.shared import opts, device, cmd_opts
from modules.sd_hijack_optimizations import invokeAI_mps_available
import ldm.modules.attention import ldm.modules.attention
import ldm.modules.diffusionmodules.model import ldm.modules.diffusionmodules.model
@ -31,8 +32,13 @@ def apply_optimizations():
print("Applying v1 cross attention optimization.") print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
print("Applying cross attention optimization (InvokeAI).") if not invokeAI_mps_available and shared.device.type == 'mps':
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
else:
print("Applying cross attention optimization (InvokeAI).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
print("Applying cross attention optimization (Doggettx).") print("Applying cross attention optimization (Doggettx).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward

19
modules/sd_hijack_optimizations.py
View File

@ -1,7 +1,7 @@
import math import math
import sys import sys
import traceback import traceback
import psutil import importlib
import torch import torch
from torch import einsum from torch import einsum
@ -117,9 +117,20 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
return self.to_out(r2) return self.to_out(r2)
# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) --
mem_total_gb = psutil.virtual_memory().total // (1 << 30) def check_for_psutil():
try:
spec = importlib.util.find_spec('psutil')
return spec is not None
except ModuleNotFoundError:
return False
invokeAI_mps_available = check_for_psutil()
# -- Taken from https://github.com/invoke-ai/InvokeAI --
if invokeAI_mps_available:
import psutil
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
def einsum_op_compvis(q, k, v): def einsum_op_compvis(q, k, v):
s = einsum('b i d, b j d -> b i j', q, k) s = einsum('b i d, b j d -> b i j', q, k)
@ -193,7 +204,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
r = einsum_op(q, k, v) r = einsum_op(q, k, v)
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py -- # -- End of code from https://github.com/invoke-ai/InvokeAI --
def xformers_attention_forward(self, x, context=None, mask=None): def xformers_attention_forward(self, x, context=None, mask=None):
h = self.heads h = self.heads