add --sequential_guidance option for low-RAM tradeoff

This commit is contained in:
Kevin Turner 2023-02-19 21:21:14 -08:00
parent 6c8d4b091e
commit 2dded68267
4 changed files with 24 additions and 11 deletions

View File

@ -60,6 +60,7 @@ def main():
Globals.always_use_cpu = args.always_use_cpu
Globals.internet_available = args.internet_available and check_internet()
Globals.disable_xformers = not args.xformers
Globals.sequential_guidance = args.sequential_guidance
Globals.ckpt_convert = args.ckpt_convert
print(f">> Internet connectivity is {Globals.internet_available}")
@ -749,7 +750,7 @@ def import_ckpt_model(
base_name = Path(url_attachment_name(path_or_url)).name if is_a_url else Path(path_or_url).name
default_name = Path(base_name).stem
default_description = f"Imported model {default_name}"
model_name, model_description = _get_model_name_and_desc(
manager,
completer,
@ -834,7 +835,7 @@ def _ask_for_config_file(model_path: Union[str,Path], completer, plural: bool=Fa
'2': 'v2-inference-v.yaml',
'3': 'v1-inpainting-inference.yaml',
}
prompt = '''What type of models are these?:
[1] Models based on Stable Diffusion 1.X
[2] Models based on Stable Diffusion 2.X
@ -843,7 +844,7 @@ def _ask_for_config_file(model_path: Union[str,Path], completer, plural: bool=Fa
[1] A model based on Stable Diffusion 1.X
[2] A model based on Stable Diffusion 2.X
[3] An inpainting models based on Stable Diffusion 1.X
[4] Something else'''
[4] Something else'''
print(prompt)
choice = input(f'Your choice: [{default}] ')
choice = choice.strip() or default

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@ -91,14 +91,14 @@ import pydoc
import re
import shlex
import sys
import ldm.invoke
import ldm.invoke.pngwriter
from ldm.invoke.globals import Globals
from ldm.invoke.prompt_parser import split_weighted_subprompts
from argparse import Namespace
from pathlib import Path
import ldm.invoke
import ldm.invoke.pngwriter
from ldm.invoke.globals import Globals
from ldm.invoke.prompt_parser import split_weighted_subprompts
APP_ID = ldm.invoke.__app_id__
APP_NAME = ldm.invoke.__app_name__
APP_VERSION = ldm.invoke.__version__
@ -484,6 +484,13 @@ class Args(object):
action='store_true',
help='Force free gpu memory before final decoding',
)
model_group.add_argument(
'--sequential_guidance',
dest='sequential_guidance',
action='store_true',
help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
"at the expense of speed",
)
model_group.add_argument(
'--xformers',
action=argparse.BooleanOptionalAction,

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@ -13,8 +13,8 @@ the attributes:
import os
import os.path as osp
from pathlib import Path
from argparse import Namespace
from pathlib import Path
from typing import Union
Globals = Namespace()
@ -48,6 +48,9 @@ Globals.internet_available = True
# Whether to disable xformers
Globals.disable_xformers = False
# Low-memory tradeoff for guidance calculations.
Globals.sequential_guidance = False
# whether we are forcing full precision
Globals.full_precision = False

View File

@ -8,6 +8,7 @@ import torch
from diffusers.models.cross_attention import AttnProcessor
from typing_extensions import TypeAlias
from ldm.invoke.globals import Globals
from ldm.models.diffusion.cross_attention_control import Arguments, \
restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
CrossAttentionType, SwapCrossAttnContext
@ -35,7 +36,7 @@ class InvokeAIDiffuserComponent:
* Hybrid conditioning (used for inpainting)
'''
debug_thresholding = False
sequential_conditioning = False
sequential_guidance = False
@dataclass
class ExtraConditioningInfo:
@ -60,6 +61,7 @@ class InvokeAIDiffuserComponent:
self.is_running_diffusers = is_running_diffusers
self.model_forward_callback = model_forward_callback
self.cross_attention_control_context = None
self.sequential_guidance = Globals.sequential_guidance
@contextmanager
def custom_attention_context(self,
@ -154,7 +156,7 @@ class InvokeAIDiffuserComponent:
unconditioning,
conditioning,
cross_attention_control_types_to_do)
elif self.sequential_conditioning:
elif self.sequential_guidance:
unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning_sequentially(
x, sigma, unconditioning, conditioning)