diff options
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 4 | ||||
| -rw-r--r-- | modules/prompt_parser.py | 4 | ||||
| -rw-r--r-- | modules/sd_disable_initialization.py | 71 | ||||
| -rw-r--r-- | modules/sd_models.py | 1 | ||||
| -rw-r--r-- | modules/textual_inversion/preprocess.py | 7 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 4 |
6 files changed, 48 insertions, 43 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 300d3975..194679e8 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -619,7 +619,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, epoch_num = hypernetwork.step // steps_per_epoch
epoch_step = hypernetwork.step % steps_per_epoch
- pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+ description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ shared.state.textinfo = description
if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
# Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index f70872c4..870218db 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -49,6 +49,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): [[5, 'a c'], [10, 'a {b|d{ c']]
>>> g("((a][:b:c [d:3]")
[[3, '((a][:b:c '], [10, '((a][:b:c d']]
+ >>> g("[a|(b:1.1)]")
+ [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
"""
def collect_steps(steps, tree):
@@ -84,7 +86,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): yield args[0].value
def __default__(self, data, children, meta):
for child in children:
- yield from child
+ yield child
return AtStep().transform(tree)
def get_schedule(prompt):
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 088ac24b..c72d8efc 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -20,6 +20,19 @@ class DisableInitialization: ```
"""
+ def __init__(self):
+ self.replaced = []
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
def __enter__(self):
def do_nothing(*args, **kwargs):
pass
@@ -37,11 +50,14 @@ class DisableInitialization: def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
# this file is always 404, prevent making request
- if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
- raise transformers.utils.hub.EntryNotFoundError
+ if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+ return None
try:
- return original(url, *args, local_files_only=True, **kwargs)
+ res = original(url, *args, local_files_only=True, **kwargs)
+ if res is None:
+ res = original(url, *args, local_files_only=False, **kwargs)
+ return res
except Exception as e:
return original(url, *args, local_files_only=False, **kwargs)
@@ -54,42 +70,19 @@ class DisableInitialization: def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
- self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
- self.init_no_grad_normal = torch.nn.init._no_grad_normal_
- self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
- self.create_model_and_transforms = open_clip.create_model_and_transforms
- self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
- self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
- self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
- self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
- self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
-
- torch.nn.init.kaiming_uniform_ = do_nothing
- torch.nn.init._no_grad_normal_ = do_nothing
- torch.nn.init._no_grad_uniform_ = do_nothing
- open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
- ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
- if self.transformers_modeling_utils_load_pretrained_model is not None:
- transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
- if self.transformers_tokenization_utils_base_cached_file is not None:
- transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
- if self.transformers_configuration_utils_cached_file is not None:
- transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
- if self.transformers_utils_hub_get_from_cache is not None:
- transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
+ self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+ self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+ self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+ self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+ self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+ self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+ self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
def __exit__(self, exc_type, exc_val, exc_tb):
- torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
- torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
- torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
- open_clip.create_model_and_transforms = self.create_model_and_transforms
- ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
- if self.transformers_modeling_utils_load_pretrained_model is not None:
- transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
- if self.transformers_tokenization_utils_base_cached_file is not None:
- transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
- if self.transformers_configuration_utils_cached_file is not None:
- transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
- if self.transformers_utils_hub_get_from_cache is not None:
- transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
+ for obj, field, original in self.replaced:
+ setattr(obj, field, original)
+
+ self.replaced.clear()
diff --git a/modules/sd_models.py b/modules/sd_models.py index 084ba7fa..c466f273 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -334,6 +334,7 @@ def load_model(checkpoint_info=None): timer = Timer()
sd_model = None
+
try:
with sd_disable_initialization.DisableInitialization():
sd_model = instantiate_from_config(sd_config.model)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index feb876c6..3c1042ad 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre params.process_caption_deepbooru = process_caption_deepbooru
params.preprocess_txt_action = preprocess_txt_action
- for index, imagefile in enumerate(tqdm.tqdm(files)):
+ pbar = tqdm.tqdm(files)
+ for index, imagefile in enumerate(pbar):
params.subindex = 0
filename = os.path.join(src, imagefile)
try:
@@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre except Exception:
continue
+ description = f"Preprocessing [Image {index}/{len(files)}]"
+ pbar.set_description(description)
+ shared.state.textinfo = description
+
params.src = filename
existing_caption = None
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3866c154..b915b091 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -476,7 +476,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ epoch_num = embedding.step // steps_per_epoch
epoch_step = embedding.step % steps_per_epoch
- pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+ description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ shared.state.textinfo = description
if embedding_dir is not None and steps_done % save_embedding_every == 0:
# Before saving, change name to match current checkpoint.
embedding_name_every = f'{embedding_name}-{steps_done}'
|