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license: openrail |
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Results from an experiment using Condition Embedding Perturbation. https://arxiv.org/pdf/2405.20494 |
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Code is very simple, add noise during the training loop between the text_encoder forward and the Unet prediction based on this code: |
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perturbation_deviation = embedding_perturbation / math.sqrt(encoder_hidden_states.shape[2]) |
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perturbation_delta = torch.randn_like(encoder_hidden_states) * (perturbation_deviation) |
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encoder_hidden_states = encoder_hidden_states + perturbation_delta |
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embedding_perturbation is the tunable lambda. |
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Models are SD1.5 trained for 30 epochs, Unet only, AdamW8bit, 2e-6 constant LR, batch size 12. EveryDream2Trainer config json included. |
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#!bin/bash |
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# python train.py --config train_ff7_emb_pert000.json --project_name "ff7r embedding_perturbation 0.0" --embedding_perturbation 0.0 |
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python train.py --config train_ff7_emb_pert000.json --project_name "ff7r embedding_perturbation 1.0" --embedding_perturbation 1.0 |
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python train.py --config train_ff7_emb_pert000.json --project_name "ff7r embedding_perturbation 1.7" --embedding_perturbation 1.7 |
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python train.py --config train_ff7_emb_pert000.json --project_name "ff7r embedding_perturbation 3.5" --embedding_perturbation 3.5 |
