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terminus-xl-velocity-v2

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PseudoTerminal X commited on
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Trained for 0 epochs and 2900 steps.

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Trained with datasets ['text-embeds-sdxl', 'photocb-clip-embeds', 'celebrities', 'movieposters', 'normalnudes', 'propagandaposters', 'guys', '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 4e-07, batch size 8, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with v_prediction prediction type and rescaled_betas_zero_snr=True
Using 'trailing' timestep spacing.
Base model: ptx0/terminus-xl-velocity-v1
VAE: madebyollin/sdxl-vae-fp16-fix

Files changed (23) hide show
  1. README.md +1 -1
  2. optimizer.bin +1 -1
  3. random_states_0.pkl +1 -1
  4. scheduler.bin +1 -1
  5. training_state-anatomy.json +2 -2
  6. training_state-architecture.json +2 -2
  7. training_state-bg20k-1024.json +2 -2
  8. training_state-celebrities.json +2 -2
  9. training_state-cinemamix-1mp.json +2 -2
  10. training_state-ethnic.json +2 -2
  11. training_state-experimental.json +2 -2
  12. training_state-gay.json +2 -2
  13. training_state-moviecollection.json +2 -2
  14. training_state-movieposters.json +2 -2
  15. training_state-nsfw-1024.json +2 -2
  16. training_state-photo-aesthetics.json +2 -2
  17. training_state-photo-concept-bucket.json +2 -2
  18. training_state-shutterstock.json +2 -2
  19. training_state-sports.json +2 -2
  20. training_state-text-1mp.json +2 -2
  21. training_state-yoga.json +2 -2
  22. training_state.json +2 -2
  23. unet/diffusion_pytorch_model.safetensors +1 -1
README.md CHANGED
@@ -44,7 +44,7 @@ You may reuse the base model text encoder for inference.
44
  ## Training settings
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  - Training epochs: 0
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- - Training steps: 2800
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  - Learning rate: 4e-07
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  - Effective batch size: 8
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  - Micro-batch size: 8
 
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  ## Training settings
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  - Training epochs: 0
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+ - Training steps: 2900
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  - Learning rate: 4e-07
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  - Effective batch size: 8
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  - Micro-batch size: 8
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