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fengyang0317
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sd-naruto-model-lora-sdxl-10k

Text-to-Image
Diffusers
stable-diffusion-xl
stable-diffusion-xl-diffusers
diffusers-training
lora
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  1. .gitattributes +4 -0
  2. README.md +48 -0
  3. checkpoint-1000/optimizer.bin +3 -0
  4. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
  5. checkpoint-1000/random_states_0.pkl +3 -0
  6. checkpoint-1000/scaler.pt +3 -0
  7. checkpoint-1000/scheduler.bin +3 -0
  8. checkpoint-10000/optimizer.bin +3 -0
  9. checkpoint-10000/pytorch_lora_weights.safetensors +3 -0
  10. checkpoint-10000/random_states_0.pkl +3 -0
  11. checkpoint-10000/scaler.pt +3 -0
  12. checkpoint-10000/scheduler.bin +3 -0
  13. checkpoint-10500/optimizer.bin +3 -0
  14. checkpoint-10500/pytorch_lora_weights.safetensors +3 -0
  15. checkpoint-10500/random_states_0.pkl +3 -0
  16. checkpoint-10500/scaler.pt +3 -0
  17. checkpoint-10500/scheduler.bin +3 -0
  18. checkpoint-11000/optimizer.bin +3 -0
  19. checkpoint-11000/pytorch_lora_weights.safetensors +3 -0
  20. checkpoint-11000/random_states_0.pkl +3 -0
  21. checkpoint-11000/scaler.pt +3 -0
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  23. checkpoint-11500/optimizer.bin +3 -0
  24. checkpoint-11500/pytorch_lora_weights.safetensors +3 -0
  25. checkpoint-11500/random_states_0.pkl +3 -0
  26. checkpoint-11500/scaler.pt +3 -0
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  28. checkpoint-12000/optimizer.bin +3 -0
  29. checkpoint-12000/pytorch_lora_weights.safetensors +3 -0
  30. checkpoint-12000/random_states_0.pkl +3 -0
  31. checkpoint-12000/scaler.pt +3 -0
  32. checkpoint-12000/scheduler.bin +3 -0
  33. checkpoint-1500/optimizer.bin +3 -0
  34. checkpoint-1500/pytorch_lora_weights.safetensors +3 -0
  35. checkpoint-1500/random_states_0.pkl +3 -0
  36. checkpoint-1500/scaler.pt +3 -0
  37. checkpoint-1500/scheduler.bin +3 -0
  38. checkpoint-2000/optimizer.bin +3 -0
  39. checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
  40. checkpoint-2000/random_states_0.pkl +3 -0
  41. checkpoint-2000/scaler.pt +3 -0
  42. checkpoint-2000/scheduler.bin +3 -0
  43. checkpoint-2500/optimizer.bin +3 -0
  44. checkpoint-2500/pytorch_lora_weights.safetensors +3 -0
  45. checkpoint-2500/random_states_0.pkl +3 -0
  46. checkpoint-2500/scaler.pt +3 -0
  47. checkpoint-2500/scheduler.bin +3 -0
  48. checkpoint-3000/optimizer.bin +3 -0
  49. checkpoint-3000/pytorch_lora_weights.safetensors +3 -0
  50. checkpoint-3000/random_states_0.pkl +3 -0
.gitattributes CHANGED
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ image_0.png filter=lfs diff=lfs merge=lfs -text
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+ image_1.png filter=lfs diff=lfs merge=lfs -text
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+ image_2.png filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ library_name: diffusers
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+ license: creativeml-openrail-m
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+ inference: true
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+ tags:
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - text-to-image
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+ - diffusers
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+ - diffusers-training
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+ - lora
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the training script had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+
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+ # LoRA text2image fine-tuning - fengyang0317/sd-naruto-model-lora-sdxl-10k
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+
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+ These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following.
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+
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+ ![img_0](./image_0.png)
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+ ![img_1](./image_1.png)
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+ ![img_2](./image_2.png)
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+ ![img_3](./image_3.png)
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+
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+
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+ LoRA for the text encoder was enabled: False.
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+
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+ Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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+ ```python
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+ # TODO: add an example code snippet for running this diffusion pipeline
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+ ```
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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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