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backnotprop
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np_cr_model3

Text-to-Image
Diffusers
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
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End of training

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  1. .gitattributes +4 -0
  2. README.md +95 -0
  3. checkpoint-1000/np_cr_model3_emb.safetensors +3 -0
  4. checkpoint-1000/optimizer.bin +3 -0
  5. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
  6. checkpoint-1000/random_states_0.pkl +3 -0
  7. checkpoint-1000/scheduler.bin +3 -0
  8. checkpoint-1500/np_cr_model3_emb.safetensors +3 -0
  9. checkpoint-1500/optimizer.bin +3 -0
  10. checkpoint-1500/pytorch_lora_weights.safetensors +3 -0
  11. checkpoint-1500/random_states_0.pkl +3 -0
  12. checkpoint-1500/scheduler.bin +3 -0
  13. checkpoint-2000/np_cr_model3_emb.safetensors +3 -0
  14. checkpoint-2000/optimizer.bin +3 -0
  15. checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
  16. checkpoint-2000/random_states_0.pkl +3 -0
  17. checkpoint-2000/scheduler.bin +3 -0
  18. checkpoint-2500/np_cr_model3_emb.safetensors +3 -0
  19. checkpoint-2500/optimizer.bin +3 -0
  20. checkpoint-2500/pytorch_lora_weights.safetensors +3 -0
  21. checkpoint-2500/random_states_0.pkl +3 -0
  22. checkpoint-2500/scheduler.bin +3 -0
  23. checkpoint-3000/np_cr_model3_emb.safetensors +3 -0
  24. checkpoint-3000/optimizer.bin +3 -0
  25. checkpoint-3000/pytorch_lora_weights.safetensors +3 -0
  26. checkpoint-3000/random_states_0.pkl +3 -0
  27. checkpoint-3000/scheduler.bin +3 -0
  28. checkpoint-3500/np_cr_model3_emb.safetensors +3 -0
  29. checkpoint-3500/optimizer.bin +3 -0
  30. checkpoint-3500/pytorch_lora_weights.safetensors +3 -0
  31. checkpoint-3500/random_states_0.pkl +3 -0
  32. checkpoint-3500/scheduler.bin +3 -0
  33. checkpoint-4000/np_cr_model3_emb.safetensors +3 -0
  34. checkpoint-4000/optimizer.bin +3 -0
  35. checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
  36. checkpoint-4000/random_states_0.pkl +3 -0
  37. checkpoint-4000/scheduler.bin +3 -0
  38. checkpoint-4500/np_cr_model3_emb.safetensors +3 -0
  39. checkpoint-4500/optimizer.bin +3 -0
  40. checkpoint-4500/pytorch_lora_weights.safetensors +3 -0
  41. checkpoint-4500/random_states_0.pkl +3 -0
  42. checkpoint-4500/scheduler.bin +3 -0
  43. checkpoint-500/np_cr_model3_emb.safetensors +3 -0
  44. checkpoint-500/optimizer.bin +3 -0
  45. checkpoint-500/pytorch_lora_weights.safetensors +3 -0
  46. checkpoint-500/random_states_0.pkl +3 -0
  47. checkpoint-500/scheduler.bin +3 -0
  48. checkpoint-5000/np_cr_model3_emb.safetensors +3 -0
  49. checkpoint-5000/optimizer.bin +3 -0
  50. checkpoint-5000/pytorch_lora_weights.safetensors +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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+ image_3.png filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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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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+ - lora
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+ - template:sd-lora
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+ widget:
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+
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+ - text: 'spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh'
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+ output:
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+ url:
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+ "image_0.png"
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+
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+ - text: 'spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh'
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+ output:
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+ url:
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+ "image_1.png"
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+
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+ - text: 'spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh'
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+ output:
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+ url:
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+ "image_2.png"
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+
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+ - text: 'spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh'
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+ output:
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+ url:
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+ "image_3.png"
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+
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ instance_prompt: something by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh
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+ license: openrail++
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+ ---
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+
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+ # SDXL LoRA DreamBooth - backnotprop/np_cr_model3
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+
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+ <Gallery />
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+
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+ ## Model description
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+
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+ ### These are backnotprop/np_cr_model3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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+
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+ ## Download model
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+
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+ ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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+
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+ - **LoRA**: download **[`np_cr_model3.safetensors` here 💾](/backnotprop/np_cr_model3/blob/main/np_cr_model3.safetensors)**.
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+ - Place it on your `models/Lora` folder.
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+ - On AUTOMATIC1111, load the LoRA by adding `<lora:np_cr_model3:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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+ - *Embeddings*: download **[`np_cr_model3_emb.safetensors` here 💾](/backnotprop/np_cr_model3/blob/main/np_cr_model3_emb.safetensors)**.
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+ - Place it on it on your `embeddings` folder
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+ - Use it by adding `np_cr_model3_emb` to your prompt. For example, `something by np_cr_model3_emb,minimalism,abstract,photoshop generated abstract colorful object mesh`
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+ (you need both the LoRA and the embeddings as they were trained together for this LoRA)
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+
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+
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+ ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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+
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+ ```py
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+ from diffusers import AutoPipelineForText2Image
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+ import torch
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+ from huggingface_hub import hf_hub_download
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+ from safetensors.torch import load_file
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+
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+ pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
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+ pipeline.load_lora_weights('backnotprop/np_cr_model3', weight_name='pytorch_lora_weights.safetensors')
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+ embedding_path = hf_hub_download(repo_id='backnotprop/np_cr_model3', filename='np_cr_model3_emb.safetensors' repo_type="model")
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+ state_dict = load_file(embedding_path)
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+ pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
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+ pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
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+
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+ image = pipeline('spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh').images[0]
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+ ```
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+
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+ For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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+
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+ ## Trigger words
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+
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+ To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
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+
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+ to trigger concept `TOK` → use `<s0><s1>` in your prompt
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+
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+
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+
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+ ## Details
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+ All [Files & versions](/backnotprop/np_cr_model3/tree/main).
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+
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+ The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
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+
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+ LoRA for the text encoder was enabled. False.
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+
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+ Pivotal tuning was enabled: True.
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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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