Trained for 9 epochs and 29000 steps.

Trained with datasets ['text-embeds', 'shutterstock', 'nijijourney', 'bg20k-1024', 'photo-aesthetics', 'text-1mp', 'cinemamix-1mp']
Learning rate 1e-06, batch size 16, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'linspace' timestep spacing.
Base model: terminusresearch/pixart-900m-1024-ft-v0.6
VAE: madebyollin/sdxl-vae-fp16-fix

README.md

@@ -552,7 +552,7 @@ You may reuse the base model text encoder for inference.
 ## Training settings
 
 - Training epochs: 9
-- Training steps: 28000
+- Training steps: 29000
 - Learning rate: 1e-06
 - Effective batch size: 16
   - Micro-batch size: 16

optimizer.bin

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training_state-bg20k-1024.json

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training_state.json

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-{"global_step": 28000, "epoch_step": 28000, "epoch": 10, "exhausted_backends": ["celebrities", "gay", "moviecollection", "ethnic", "cinemamix-1mp", "shutterstock", "nijijourney", "photo-aesthetics"], "repeats": {"movieposters": 20, "celebrities": 0, "normalnudes": 3, "moviecollection": 0, "gay": 0, "ethnic": 0, "nsfw-1024": 0, "experimental": 0, "cinemamix-1mp": 0, "text-1mp": 3, "shutterstock": 0, "nijijourney": 0, "photo-aesthetics": 0}}
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transformer/config.json

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 {
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   "_diffusers_version": "0.30.0.dev0",
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   "activation_fn": "gelu-approximate",
   "attention_bias": true,
   "attention_head_dim": 72,

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