PseudoTerminal X
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Parent(s):
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Trained for 0 epochs and 14000 steps.
Browse filesTrained with datasets ['text-embeds-pixart-filter', 'photo-concept-bucket', 'text-1mp', 'ideogram', 'midjourney-v6-520k-raw', 'sfwbooru', 'nijijourney-v6-520k-raw', 'dalle3']
Learning rate 1e-06, batch size 24, and 1 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: terminusresearch/pixart-900m-1024-ft-v0.6
VAE: madebyollin/sdxl-vae-fp16-fix
- README.md +1 -1
- optimizer.bin +1 -1
- random_states_0.pkl +1 -1
- scheduler.bin +1 -1
- training_state-dalle3.json +2 -2
- training_state-ideogram.json +0 -0
- training_state-photo-concept-bucket.json +2 -2
- training_state-text-1mp.json +0 -0
- training_state.json +1 -1
- transformer/diffusion_pytorch_model.safetensors +1 -1
README.md
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@@ -1562,7 +1562,7 @@ 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: 192
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- Micro-batch size: 24
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## Training settings
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- Training epochs: 0
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- Training steps: 14000
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- Learning rate: 1e-06
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- Effective batch size: 192
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- Micro-batch size: 24
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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-dalle3.json
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training_state-ideogram.json
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training_state-photo-concept-bucket.json
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training_state-text-1mp.json
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training_state.json
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{"global_step":
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{"global_step": 14000, "epoch_step": 14000, "epoch": 1, "exhausted_backends": ["sfwbooru", "nijijourney-v6-520k-raw", "midjourney-v6-520k-raw"], "repeats": {"ideogram": 12, "sfwbooru": 0, "text-1mp": 14, "nijijourney-v6-520k-raw": 0, "midjourney-v6-520k-raw": 0}}
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transformer/diffusion_pytorch_model.safetensors
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