Skip to content

[bug fix] advanced dreambooth lora sdxl - fixes bugs described in #6486 #6599

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
sayakpaul merged 4 commits into from
Jan 17, 2024
Merged

[bug fix] advanced dreambooth lora sdxl - fixes bugs described in #6486 #6599

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
70ebdf3
fixes bugs:
linoytsaban Jan 16, 2024
b3b4179
param upscaling
linoytsaban Jan 16, 2024
0d49bb2
Merge branch 'huggingface:main' into advanced_script_fixes
linoytsaban Jan 17, 2024
0416806
style
linoytsaban Jan 17, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 14 additions & 9 deletions examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1279,7 +1279,7 @@ def main(args):
for name, param in text_encoder_one.named_parameters():
if "token_embedding" in name:
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
param = param.to(dtype=torch.float32)
param.data = param.to(dtype=torch.float32)
param.requires_grad = True
text_lora_parameters_one.append(param)
else:
Expand All @@ -1288,7 +1288,7 @@ def main(args):
for name, param in text_encoder_two.named_parameters():
if "token_embedding" in name:
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
param = param.to(dtype=torch.float32)
param.data = param.to(dtype=torch.float32)
param.requires_grad = True
text_lora_parameters_two.append(param)
else:
Expand Down Expand Up @@ -1725,19 +1725,19 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)
elif args.train_text_encoder_ti: # args.train_text_encoder_ti
num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)

# flag used for textual inversion
pivoted = False
for epoch in range(first_epoch, args.num_train_epochs):
# if performing any kind of optimization of text_encoder params
if args.train_text_encoder or args.train_text_encoder_ti:
if epoch == num_train_epochs_text_encoder:
print("PIVOT HALFWAY", epoch)
# stopping optimization of text_encoder params
# re setting the optimizer to optimize only on unet params
optimizer.param_groups[1]["lr"] = 0.0
optimizer.param_groups[2]["lr"] = 0.0
# this flag is used to reset the optimizer to optimize only on unet params
pivoted = True

else:
# still optimizng the text encoder
# still optimizing the text encoder
text_encoder_one.train()
text_encoder_two.train()
# set top parameter requires_grad = True for gradient checkpointing works
Expand All @@ -1747,6 +1747,12 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):

unet.train()
for step, batch in enumerate(train_dataloader):
if pivoted:
# stopping optimization of text_encoder params
# re setting the optimizer to optimize only on unet params
optimizer.param_groups[1]["lr"] = 0.0
optimizer.param_groups[2]["lr"] = 0.0

with accelerator.accumulate(unet):
prompts = batch["prompts"]
# encode batch prompts when custom prompts are provided for each image -
Expand Down Expand Up @@ -1885,8 +1891,7 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):

# every step, we reset the embeddings to the original embeddings.
if args.train_text_encoder_ti:
for idx, text_encoder in enumerate(text_encoders):
embedding_handler.retract_embeddings()
embedding_handler.retract_embeddings()

# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
Expand Down