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Modularize Dreambooth LoRA SD inferencing during and after training #6654
Modularize Dreambooth LoRA SD inferencing during and after training #6654
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This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread. Please note that issues that do not follow the contributing guidelines are likely to be ignored. |
cc @sayakpaul |
@@ -1255,12 +1322,9 @@ def compute_text_embeddings(prompt): | |||
if global_step >= args.max_train_steps: | |||
break | |||
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images = [] |
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Why do we need to initialize it here?
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We don't. I will delete this line
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Looks very good. Thanks a lot for working on this.
Could you also provide some sample training commands with which we could test this?
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
Hi @sayakpaul, you could test it with the following line
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Just one change requested. Thanks a lot for working on this!
Ran with your command:
accelerate launch train_dreambooth_lora.py --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" --instance_data_dir="dog" --output_dir=dog_lora --instance_prompt="a photo of sks dog" --train_batch_size=1 --resolution=512 --checkpointing_steps=100 --learning_rate=1e-4 --max_train_steps=500 --validation_prompt="a photo of sks dog in a bucket" --validation_epochs=50 --seed="0" --report_to="wandb"
Working perfectly: https://wandb.ai/sayakpaul/dreambooth-lora/runs/ligw3fg4
Thanks a lot for working on this! |
What does this PR do?
Part of #6545
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