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Lonuhbow
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makeout2

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Diffusers
diffusers-training
lora
krea2
krea2-diffusers
template:sd-lora
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  1. README.md +75 -0
  2. checkpoint-1000/optimizer.bin +3 -0
  3. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
  4. checkpoint-1000/random_states_0.pkl +3 -0
  5. checkpoint-1000/scheduler.bin +3 -0
  6. checkpoint-1200/optimizer.bin +3 -0
  7. checkpoint-1200/pytorch_lora_weights.safetensors +3 -0
  8. checkpoint-1200/random_states_0.pkl +3 -0
  9. checkpoint-1200/scheduler.bin +3 -0
  10. checkpoint-1400/optimizer.bin +3 -0
  11. checkpoint-1400/pytorch_lora_weights.safetensors +3 -0
  12. checkpoint-1400/random_states_0.pkl +3 -0
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  14. checkpoint-1600/optimizer.bin +3 -0
  15. checkpoint-1600/pytorch_lora_weights.safetensors +3 -0
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  26. checkpoint-2000/optimizer.bin +3 -0
  27. checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
  28. checkpoint-2000/random_states_0.pkl +3 -0
  29. checkpoint-2000/scheduler.bin +3 -0
  30. checkpoint-400/optimizer.bin +3 -0
  31. checkpoint-400/pytorch_lora_weights.safetensors +3 -0
  32. checkpoint-400/random_states_0.pkl +3 -0
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  34. checkpoint-600/optimizer.bin +3 -0
  35. checkpoint-600/pytorch_lora_weights.safetensors +3 -0
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  38. checkpoint-800/optimizer.bin +3 -0
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  40. checkpoint-800/random_states_0.pkl +3 -0
  41. checkpoint-800/scheduler.bin +3 -0
  42. pytorch_lora_weights.safetensors +3 -0
  43. training_metrics.jsonl +0 -0
README.md ADDED
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+ ---
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+ base_model:
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+ - krea/Krea-2-Turbo
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+ - krea/Krea-2-Raw
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+ library_name: diffusers
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+ license: apache-2.0
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+ instance_prompt: Makeout2
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+ widget: []
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+ tags:
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+ - text-to-image
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+ - diffusers-training
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+ - diffusers
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+ - lora
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+ - krea2
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+ - krea2-diffusers
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+ - template:sd-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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+ # Krea 2 DreamBooth LoRA - Lonuhbow/makeout2
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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 Lonuhbow/makeout2 DreamBooth LoRA weights, trained on krea/Krea-2-Raw.
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+
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+ The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Krea 2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_krea2.md).
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+
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+ Krea 2 ships as two checkpoints: **RAW** (the non-distilled base you fine-tune on) and **Turbo** (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo.
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+
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+ ## Trigger words
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+
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+ You should use `Makeout2` to trigger the image generation.
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+
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+ ## Download model
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+
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+ [Download the *.safetensors LoRA](Lonuhbow/makeout2/tree/main) in the Files & versions tab.
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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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+ >>> import torch
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+ >>> from diffusers import Krea2Pipeline
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+
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+ >>> # Load the LoRA onto Krea 2 Turbo (the distilled inference model)
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+ >>> pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
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+ >>> pipe.load_lora_weights("Lonuhbow/makeout2")
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+
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+ >>> # Turbo recipe: 8 steps, no classifier-free guidance
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+ >>> image = pipe("Makeout2", num_inference_steps=8, guidance_scale=0.0).images[0]
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+ >>> image.save("output.png")
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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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+
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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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