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Val loss#1899

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kohya-ss merged 81 commits intosd3kohya-ss/sd-scripts:sd3from val-losskohya-ss/sd-scripts:val-lossCopy head branch name to clipboardJan 26, 2025

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Commits on Nov 5, 2023

• Add validation loss

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• new ratio code

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• Add validation split of datasets

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• Update args to validation_seed and validation_split

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• Add process_batch for train_network

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• Removed/cleanup a line

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• Remove unnecessary subset line from collate

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Commits on Nov 7, 2023

• Set grad enabled if is_train and train_text_encoder

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Commits on Mar 9, 2024

• val

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Commits on Mar 10, 2024

• improve

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• Update train_network.py

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• Update train_network.py

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Commits on Mar 11, 2024

• fix timesteps

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• only use train subset to val

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• Update train_network.py

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• Update train_network.py

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• Update train_network.py

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Commits on Mar 13, 2024

• fix

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• Merge remote-tracking branch 'kohya-ss/dev' into val

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• Update train_network.py

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Commits on Mar 16, 2024

• fix control net

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Commits on Apr 10, 2024

• Merge branch 'main' into val

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• Update config_util.py

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• Update train_util.py

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• Update config_util.py

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• Update config_util.py

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Commits on Apr 11, 2024

• Update train_util.py

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• Update config_util.py

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Commits on Aug 2, 2024

• Remove unnecessary is_train changes and use apply_debiased_estimation to calculate validation loss. Balances the influence of different time steps on training performance (without affecting actual …

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Commits on Aug 4, 2024

• Update train_db.py

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• Update train_db.py

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• Update train_db.py

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• Update train_network.py

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• Update train_network.py

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• Update train_network.py

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Commits on Oct 4, 2024

• Update train_network.py

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• Update train_db.py

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Commits on Dec 27, 2024

• Add Validation loss for LoRA training

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• Change val loss calculate method

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Commits on Dec 28, 2024

• Split val latents/batch and pick up val latents shape size which equal to training batch.

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• Change val latent loss compare

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Commits on Jan 2, 2025

• val

  

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• Adding modified train_util and config_util

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Commits on Jan 3, 2025

• Merge branch 'gesen2egee/val' into validation-loss-upstream

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• Merge remote-tracking branch 'hina/feature/val-loss' into validation-loss-upstream

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• Remove defunct code for train_controlnet.py

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• Typos and lingering is_train

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• Cleanup order, types, print to logger

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• Update text for train/reg with repeats

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• Add seed help clarifying info

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• Remove duplicate assignment

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• Fix training, validation split, revert to using upstream implemenation

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• Move get_huber_threshold_if_needed

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• Add step break for validation epoch. Remove unused variable

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• Add missing functions for training batch

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Commits on Jan 6, 2025

• Use self.get_noise_pred_and_target and drop fixed timesteps

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• Revert train_db.py

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• Cleanup unused code and formatting

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• Add validate_every_n_epochs, change name validate_every_n_steps

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• Fix validate epoch, cleanup imports

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• Clean up some validation help documentation

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• Set validation steps in multiple lines for readability

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Commits on Jan 8, 2025

• Remove unused train_util code, fix accelerate.log for wandb, add init_trackers library code

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• Fix documentation, remove unused function, fix bucket reso for sd1.5, fix multiple datasets

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• Handle tuple return from generate_dataset_group_by_blueprint

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• Revert bucket_reso_steps to correct 64

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• Fix incorrect destructoring for load_abritrary_dataset

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Commits on Jan 9, 2025

• Apply is_training_dataset only to DreamBoothDataset. Add validation_split check and warning

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Commits on Jan 12, 2025

• Add divergence to logs

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• Fix regularization images with validation

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• Fix validate_every_n_steps always running first step

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• Fix validate_every_n_steps for gradient accumulation

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• Remove Validating... print to fix output layout

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• Disable repeats for validation datasets

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Commits on Jan 23, 2025

• Fix loss recorder on 0. Fix validation for cached runs. Assert on validation dataset

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Commits on Jan 26, 2025

• Merge pull request #1864 from rockerBOO/validation-loss-upstream

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• Fix to work with validation dataset

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• README.md: Update recent updates section to include validation loss support for training scripts

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