metadata

base_model: HiDream-ai/HiDream-I1-Full
library_name: diffusers
license: mit
instance_prompt: a dog, yarn art style
widget:
  - text: yoda, yarn art style
    output:
      url: image_0.png
  - text: yoda, yarn art style
    output:
      url: image_1.png
  - text: yoda, yarn art style
    output:
      url: image_2.png
  - text: yoda, yarn art style
    output:
      url: image_3.png
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - hidream
  - hidream-diffusers
  - template:sd-lora
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - hidream
  - hidream-diffusers
  - template:sd-lora

HiDream Image DreamBooth LoRA - linoyts/hidream-yarn-art-lora-v2-trainer-multi

Model description

These are linoyts/hidream-yarn-art-lora-v2-trainer-multi DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full.

The weights were trained using DreamBooth with the HiDream Image diffusers trainer.

Trigger words

You should use a dog, yarn art style to trigger the image generation.

Download model

Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

    >>> 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-multi")
    >>> 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

Intended uses & limitations

How to use

# 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]