Mona

--- base_model: - krea/Krea-2-Turbo - 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 - papigia/mona <Gallery /> ## Model description These are papigia/mona 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](papigia/mona/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("papigia/mona") >>> # 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]