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---
base_model: HiDream-ai/HiDream-I1-Full
library_name: diffusers
license: mit
instance_prompt: a dog, yarn art style
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
widget:
- text: yoda, yarn art style
output:
url: image_1.png
- text: cookie monster, yarn art style
output:
url: cookie.png
- text: the joker, yarn art style
output:
url: joker.png
- text: a capybara in a bubble batch, yarn art style
output:
url: capy.png
---
<!-- 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. -->
# HiDream Image DreamBooth LoRA - linoyts/hidream-yarn-art-lora-v2-trainer
<Gallery />
## Model description
These are linoyts/hidream-yarn-art-lora-v2-trainer 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 `a dog, yarn art style` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](linoyts/hidream-yarn-art-lora-v2-trainer/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",
... 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](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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