    >>> 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",
    ...     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-v2-trainer")
    >>> image = pipe(f"a dog, yarn art style").images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers