End of training

README.md

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+---
+base_model: krea/Krea-2-Raw
+library_name: diffusers
+license: apache-2.0
+instance_prompt: TOK
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- krea2
+- krea2-diffusers
+- template:sd-lora
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# Krea 2 DreamBooth LoRA - aimalias/jj49
+
+<Gallery />
+
+## Model description
+
+These are aimalias/jj49 DreamBooth LoRA weights, trained on krea/Krea-2-Raw.
+
+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).
+
+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.
+
+## Trigger words
+
+You should use `TOK` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](aimalias/jj49/tree/main) in the Files & versions tab.
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+>>> import torch
+>>> from diffusers import Krea2Pipeline
+
+>>> # Load the LoRA onto Krea 2 Turbo (the distilled inference model)
+>>> pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
+>>> pipe.load_lora_weights("aimalias/jj49")
+
+>>> # Turbo recipe: 8 steps, no classifier-free guidance
+>>> image = pipe("TOK", num_inference_steps=8, guidance_scale=0.0).images[0]
+>>> image.save("output.png")
+```
+
+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)
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

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