--- base_model: black-forest-labs/FLUX.2-klein-9B library_name: diffusers license: other instance_prompt: a photo of AndyYang widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux2-klein - flux2-klein-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - flux2-klein - flux2-klein-diffusers - template:sd-lora --- # Flux.2 [Klein] DreamBooth LoRA - mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277 ## Model description These are mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277 DreamBooth LoRA weights for black-forest-labs/FLUX.2-klein-9B. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux2.md). Quant training? None ## Trigger words You should use `a photo of AndyYang` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.2", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a photo of AndyYang').images[0] ``` 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) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.2/blob/main/LICENSE.md). ## 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]