Trained for 0 epochs and 4500 steps.
Browse filesTrained with datasets ['text-embeds-sd3-nofilter', 'photo-concept-bucket', 'dalle3']
Learning rate 1e-06, batch size 6, 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: stabilityai/stable-diffusion-3-medium-diffusers
VAE: None
- README.md +2 -2
- optimizer.bin +1 -1
- random_states_0.pkl +2 -2
- scheduler.bin +1 -1
- training_state-dalle3.json +0 -0
- training_state-photo-concept-bucket.json +0 -0
- training_state.json +1 -1
- transformer/diffusion_pytorch_model.safetensors +1 -1
README.md
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@@ -1164,7 +1164,7 @@ You may reuse the base model text encoder for inference.
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## Training settings
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- Training epochs: 0
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- Training steps:
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- Learning rate: 1e-06
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- Effective batch size: 96
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- Micro-batch size: 6
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### photo-concept-bucket
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- Repeats: 0
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- Total number of images: ~557568
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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: 0
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+
- Training steps: 4500
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- Learning rate: 1e-06
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- Effective batch size: 96
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- Micro-batch size: 6
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### photo-concept-bucket
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- Repeats: 0
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- Total number of images: ~557568
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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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training_state-dalle3.json
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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": 4500, "epoch_step": 4500, "epoch": 1, "exhausted_backends": [], "repeats": {}}
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