PseudoTerminal X
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Trained for 6 epochs and 8000 steps.
Browse filesTrained with datasets ['text-embeds-pixart-nofilter', 'photo-concept-bucket']
Learning rate 1e-06, batch size 32, and 2 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
VAE: madebyollin/sdxl-vae-fp16-fix
- README.md +2 -2
- optimizer.bin +1 -1
- random_states_0.pkl +2 -2
- scheduler.bin +1 -1
- training_state-photo-concept-bucket.json +2 -2
- training_state.json +1 -1
- transformer/diffusion_pytorch_model.safetensors +1 -1
README.md
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@@ -1006,7 +1006,7 @@ You may reuse the base model text encoder for inference.
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## Training settings
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- Training epochs: 6
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- Training steps:
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- Learning rate: 1e-06
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- Effective batch size: 512
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- Micro-batch size: 32
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### photo-concept-bucket
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- Repeats: 0
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- Total number of images: ~550912
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- Total number of aspect buckets:
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- Resolution: 1.0 megapixels
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- Cropped: False
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- Crop style: None
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## Training settings
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- Training epochs: 6
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- Training steps: 8000
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- Learning rate: 1e-06
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- Effective batch size: 512
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- Micro-batch size: 32
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### photo-concept-bucket
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- Repeats: 0
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- Total number of images: ~550912
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- Total number of aspect buckets: 2
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- Resolution: 1.0 megapixels
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- Cropped: False
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- Crop style: None
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optimizer.bin
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random_states_0.pkl
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scheduler.bin
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training_state-photo-concept-bucket.json
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training_state.json
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{"global_step":
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{"global_step": 8000, "epoch_step": 739, "epoch": 7, "exhausted_backends": [], "repeats": {"photo-concept-bucket": 0}}
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transformer/diffusion_pytorch_model.safetensors
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