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deepgoyal20
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lora13

Text-to-Image
Diffusers
stable-diffusion
stable-diffusion-diffusers
text-to-image-lora
lora
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deepgoyal20 commited on
Commit
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End of training

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Files changed (37) hide show
  1. README.md +17 -0
  2. checkpoint-160/optimizer.bin +3 -0
  3. checkpoint-160/pytorch_model.bin +3 -0
  4. checkpoint-160/random_states_0.pkl +3 -0
  5. checkpoint-160/scaler.pt +3 -0
  6. checkpoint-160/scheduler.bin +3 -0
  7. checkpoint-240/optimizer.bin +3 -0
  8. checkpoint-240/pytorch_model.bin +3 -0
  9. checkpoint-240/random_states_0.pkl +3 -0
  10. checkpoint-240/scaler.pt +3 -0
  11. checkpoint-240/scheduler.bin +3 -0
  12. checkpoint-320/optimizer.bin +3 -0
  13. checkpoint-320/pytorch_model.bin +3 -0
  14. checkpoint-320/random_states_0.pkl +3 -0
  15. checkpoint-320/scaler.pt +3 -0
  16. checkpoint-320/scheduler.bin +3 -0
  17. checkpoint-400/optimizer.bin +3 -0
  18. checkpoint-400/pytorch_model.bin +3 -0
  19. checkpoint-400/random_states_0.pkl +3 -0
  20. checkpoint-400/scaler.pt +3 -0
  21. checkpoint-400/scheduler.bin +3 -0
  22. checkpoint-480/optimizer.bin +3 -0
  23. checkpoint-480/pytorch_model.bin +3 -0
  24. checkpoint-480/random_states_0.pkl +3 -0
  25. checkpoint-480/scaler.pt +3 -0
  26. checkpoint-480/scheduler.bin +3 -0
  27. checkpoint-560/optimizer.bin +3 -0
  28. checkpoint-560/pytorch_model.bin +3 -0
  29. checkpoint-560/random_states_0.pkl +3 -0
  30. checkpoint-560/scaler.pt +3 -0
  31. checkpoint-560/scheduler.bin +3 -0
  32. checkpoint-80/optimizer.bin +3 -0
  33. checkpoint-80/pytorch_model.bin +3 -0
  34. checkpoint-80/random_states_0.pkl +3 -0
  35. checkpoint-80/scaler.pt +3 -0
  36. checkpoint-80/scheduler.bin +3 -0
  37. pytorch_lora_weights.bin +3 -0
README.md ADDED
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+
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+ ---
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+ license: creativeml-openrail-m
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+ base_model: deepgoyal19/mysd1
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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+ - lora
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+ inference: true
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+ ---
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+
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+ # LoRA text2image fine-tuning - deepgoyal20/lora13
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+ These are LoRA adaption weights for deepgoyal19/mysd1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
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+
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+
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