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Merge branch 'bugfix/embed-loading-messages' of github.com:invoke-ai/InvokeAI into bugfix/embed-loading-messages

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This commit is contained in:
Lincoln Stein 2023-01-20 17:16:50 -05:00
commit 67d91dc550
8 changed files with 51 additions and 30 deletions
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10
ldm/invoke/CLI.py
View File

@ -45,6 +45,7 @@ def main():
Globals.try_patchmatch = args.patchmatch
Globals.always_use_cpu = args.always_use_cpu
Globals.internet_available = args.internet_available and check_internet()
Globals.disable_xformers = not args.xformers
print(f'>> Internet connectivity is {Globals.internet_available}')
if not args.conf:
@ -124,7 +125,7 @@ def main():
# preload the model
try:
gen.load_model()
except KeyError as e:
except KeyError:
pass
except Exception as e:
report_model_error(opt, e)
@ -731,11 +732,6 @@ def del_config(model_name:str, gen, opt, completer):
completer.update_models(gen.model_manager.list_models())
def edit_model(model_name:str, gen, opt, completer):
current_model = gen.model_name
# if model_name == current_model:
# print("** Can't edit the active model. !switch to another model first. **")
# return
manager = gen.model_manager
if not (info := manager.model_info(model_name)):
print(f'** Unknown model {model_name}')
@ -887,7 +883,7 @@ def prepare_image_metadata(
try:
filename = opt.fnformat.format(**wildcards)
except KeyError as e:
print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use \'{{prefix}}.{{seed}}.png\' instead')
print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use {{prefix}}.{{seed}}.png\' instead')
filename = f'{prefix}.{seed}.png'
except IndexError:
print(f'** The filename format is broken or complete. Will use \'{{prefix}}.{{seed}}.png\' instead')

6
ldm/invoke/args.py
View File

@ -482,6 +482,12 @@ class Args(object):
action='store_true',
help='Force free gpu memory before final decoding',
)
model_group.add_argument(
'--xformers',
action=argparse.BooleanOptionalAction,
default=True,
help='Enable/disable xformers support (default enabled if installed)',
)
model_group.add_argument(
"--always_use_cpu",
dest="always_use_cpu",

3
ldm/invoke/generator/diffusers_pipeline.py
View File

@ -39,6 +39,7 @@ from diffusers.utils.outputs import BaseOutput
from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ldm.invoke.globals import Globals
from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, ThresholdSettings
from ldm.modules.textual_inversion_manager import TextualInversionManager
@ -306,7 +307,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
textual_inversion_manager=self.textual_inversion_manager
)
if is_xformers_available():
if is_xformers_available() and not Globals.disable_xformers:
self.enable_xformers_memory_efficient_attention()
def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,

4
ldm/invoke/generator/txt2img2img.py
View File

@ -3,6 +3,7 @@ ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
'''
import math
from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
from typing import Callable, Optional
import torch
@ -66,6 +67,8 @@ class Txt2Img2Img(Generator):
second_pass_noise = self.get_noise_like(resized_latents)
verbosity = get_verbosity()
set_verbosity_error()
pipeline_output = pipeline.img2img_from_latents_and_embeddings(
resized_latents,
num_inference_steps=steps,
@ -73,6 +76,7 @@ class Txt2Img2Img(Generator):
strength=strength,
noise=second_pass_noise,
callback=step_callback)
set_verbosity(verbosity)
return pipeline.numpy_to_pil(pipeline_output.images)[0]

3
ldm/invoke/globals.py
View File

@ -43,6 +43,9 @@ Globals.always_use_cpu = False
# The CLI will test connectivity at startup time.
Globals.internet_available = True
# Whether to disable xformers
Globals.disable_xformers = False
# whether we are forcing full precision
Globals.full_precision = False

7
ldm/invoke/model_manager.py
View File

@ -25,6 +25,7 @@ import torch
import safetensors
import transformers
from diffusers import AutoencoderKL, logging as dlogging
from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from picklescan.scanner import scan_file_path
@ -827,11 +828,11 @@ class ModelManager(object):
return model
# diffusers really really doesn't like us moving a float16 model onto CPU
import logging
logging.getLogger('diffusers.pipeline_utils').setLevel(logging.CRITICAL)
verbosity = get_verbosity()
set_verbosity_error()
model.cond_stage_model.device = 'cpu'
model.to('cpu')
logging.getLogger('pipeline_utils').setLevel(logging.INFO)
set_verbosity(verbosity)
for submodel in ('first_stage_model','cond_stage_model','model'):
try:

1
scripts/configure_invokeai.py
View File

@ -676,6 +676,7 @@ def download_weights(opt:dict) -> Union[str, None]:
return
access_token = authenticate()
if access_token is not None:
HfFolder.save_token(access_token)
print('\n** DOWNLOADING WEIGHTS **')

43
scripts/textual_inversion_fe.py
View File

@ -115,6 +115,14 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
value=self.precisions.index(saved_args.get('mixed_precision','fp16')),
max_height=4,
)
self.num_train_epochs = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Number of training epochs:',
out_of=1000,
step=50,
lowest=1,
value=saved_args.get('num_train_epochs',100)
)
self.max_train_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Max Training Steps:',
@ -131,6 +139,22 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
lowest=1,
value=saved_args.get('train_batch_size',8),
)
self.gradient_accumulation_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Gradient Accumulation Steps (may need to decrease this to resume from a checkpoint):',
out_of=10,
step=1,
lowest=1,
value=saved_args.get('gradient_accumulation_steps',4)
)
self.lr_warmup_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Warmup Steps:',
out_of=100,
step=1,
lowest=0,
value=saved_args.get('lr_warmup_steps',0),
)
self.learning_rate = self.add_widget_intelligent(
npyscreen.TitleText,
name="Learning Rate:",
@ -154,22 +178,6 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
scroll_exit = True,
value=self.lr_schedulers.index(saved_args.get('lr_scheduler','constant')),
)
self.gradient_accumulation_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Gradient Accumulation Steps:',
out_of=10,
step=1,
lowest=1,
value=saved_args.get('gradient_accumulation_steps',4)
)
self.lr_warmup_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name='Warmup Steps:',
out_of=100,
step=1,
lowest=0,
value=saved_args.get('lr_warmup_steps',0),
)
def initializer_changed(self):
placeholder = self.placeholder_token.value
@ -236,7 +244,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
# all the integers
for attr in ('train_batch_size','gradient_accumulation_steps',
'max_train_steps','lr_warmup_steps'):
'num_train_epochs','max_train_steps','lr_warmup_steps'):
args[attr] = int(getattr(self,attr).value)
# the floats (just one)
@ -324,6 +332,7 @@ if __name__ == '__main__':
save_args(args)
try:
print(f'DEBUG: args = {args}')
do_textual_inversion_training(**args)
copy_to_embeddings_folder(args)
except Exception as e: