--- base_model: HiDream-ai/HiDream-I1-Full library_name: diffusers license: mit instance_prompt: a dog, yarn art style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora --- # HiDream Image DreamBooth LoRA - linoyts/HiDream-yarn-art-LoRA ## Model description These are linoyts/dog-hidream-lora-offload-mini-test DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [HiDream Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md). ## Trigger words You should use `yarn art style` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](https://huggingface.co/linoyts/HiDream-yarn-art-LoRA/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py >>> import torch >>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM >>> from diffusers import HiDreamImagePipeline >>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") >>> text_encoder_4 = LlamaForCausalLM.from_pretrained( ... "meta-llama/Meta-Llama-3.1-8B-Instruct", ... output_hidden_states=True, ... output_attentions=True, ... torch_dtype=torch.bfloat16, ... ) >>> pipe = HiDreamImagePipeline.from_pretrained( ... "HiDream-ai/HiDream-I1-Full", ... scheduler=scheduler, ... tokenizer_4=tokenizer_4, ... text_encoder_4=text_encoder_4, ... torch_dtype=torch.bfloat16, ... ) >>> pipe.enable_model_cpu_offload() >>> pipe.load_lora_weights(f"linoyts/HiDream-yarn-art-LoRA") >>> image = pipe(f"yoda, yarn art style").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)