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this fixes the inconsistent use of self.device, sometimes a str and sometimes an obj

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This commit is contained in:
Lincoln Stein 2022-09-01 10:16:05 -04:00
parent dc30adfbb4
commit 01e05a98de
2 changed files with 31 additions and 38 deletions
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61
ldm/simplet2i.py
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@ -133,31 +133,31 @@ class T2I:
embedding_path=None,
# just to keep track of this parameter when regenerating prompt
latent_diffusion_weights=False,
device='cuda',
):
self.iterations = iterations
self.width = width
self.height = height
self.steps = steps
self.cfg_scale = cfg_scale
self.weights = weights
self.config = config
self.sampler_name = sampler_name
self.latent_channels = latent_channels
self.downsampling_factor = downsampling_factor
self.grid = grid
self.ddim_eta = ddim_eta
self.precision = precision
self.full_precision = full_precision
self.strength = strength
self.embedding_path = embedding_path
self.model = None # empty for now
self.sampler = None
self.iterations = iterations
self.width = width
self.height = height
self.steps = steps
self.cfg_scale = cfg_scale
self.weights = weights
self.config = config
self.sampler_name = sampler_name
self.latent_channels = latent_channels
self.downsampling_factor = downsampling_factor
self.grid = grid
self.ddim_eta = ddim_eta
self.precision = precision
self.full_precision = full_precision
self.strength = strength
self.embedding_path = embedding_path
self.model = None # empty for now
self.sampler = None
self.device = None
self.latent_diffusion_weights = latent_diffusion_weights
self.device = device
# for VRAM usage statistics
self.session_peakmem = torch.cuda.max_memory_allocated() if self.device == 'cuda' else None
device_type = choose_torch_device()
self.session_peakmem = torch.cuda.max_memory_allocated() if device_type == 'cuda' else None
if seed is None:
self.seed = self._new_seed()
@ -251,14 +251,15 @@ class T2I:
to create the requested output directory, select a unique informative name for each image, and
write the prompt into the PNG metadata.
"""
steps = steps or self.steps
seed = seed or self.seed
width = width or self.width
height = height or self.height
cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta
iterations = iterations or self.iterations
strength = strength or self.strength
# TODO: convert this into a getattr() loop
steps = steps or self.steps
seed = seed or self.seed
width = width or self.width
height = height or self.height
cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta
iterations = iterations or self.iterations
strength = strength or self.strength
self.log_tokenization = log_tokenization
model = (
@ -279,7 +280,7 @@ class T2I:
self._set_sampler()
tic = time.time()
torch.cuda.reset_peak_memory_stats() if self.device == 'cuda' else None
torch.cuda.reset_peak_memory_stats() if self.device.type == 'cuda' else None
results = list()
try:

8
scripts/dream.py
View File

@ -60,7 +60,6 @@ def main():
# this is solely for recreating the prompt
latent_diffusion_weights=opt.laion400m,
embedding_path=opt.embedding_path,
device=opt.device,
)
# make sure the output directory exists
@ -376,13 +375,6 @@ def create_argv_parser():
type=str,
help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
)
parser.add_argument(
'--device',
'-d',
type=str,
default='cuda',
help='Device to run Stable Diffusion on. Defaults to cuda `torch.cuda.current_device()` if avalible',
)
parser.add_argument(
'--prompt_as_dir',
'-p',