Trained for 0 epochs and 1500 steps.

Trained with datasets ['text-embeds-pixart-filter', 'midjourney-v6-520k-raw', 'sfwbooru', 'mj-60', 'nijijourney-v6-520k-raw', 'dalle3', 'celebrities', 'movieposters', 'normalnudes', 'propagandaposters', 'pixel-art', 'signs', 'moviecollection', 'bookcovers', 'nijijourney', 'experimental', 'ethnic', 'sports', 'gay', 'architecture', 'shutterstock', 'cinemamix-1mp', 'nsfw-1024', 'anatomy', 'bg20k-1024', 'yoga', 'photo-aesthetics', 'text-1mp', 'photo-concept-bucket']
Learning rate 1e-06, batch size 32, 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: ptx0/pixart-900m-1024-ft-large
VAE: madebyollin/sdxl-vae-fp16-fix

README.md

@@ -752,7 +752,7 @@ You may reuse the base model text encoder for inference.
 ## Training settings
 
 - Training epochs: 0
-- Training steps: 1000
+- Training steps: 1500
 - Learning rate: 1e-06
 - Effective batch size: 256
   - Micro-batch size: 32

optimizer.bin

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random_states_0.pkl

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+size 16036

scheduler.bin

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training_state-moviecollection.json

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