---
base_model: Qwen/Qwen-Image
library_name: diffusers
license: apache-2.0
instance_prompt: yoda, yarn art style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- qwen-image
- qwen-image-diffusers
- template:sd-lora
---

<!-- 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/lora_jobs_test_2

<Gallery />

## Model description

These are linoyts/lora_jobs_test_2 DreamBooth LoRA weights for Qwen/Qwen-Image.

The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Qwen Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_qwen.md).

## Trigger words

You should use `yoda, yarn art style` to trigger the image generation.

## Download model

[Download the *.safetensors LoRA](linoyts/lora_jobs_test_2/tree/main) in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
    >>> import torch
    >>> from diffusers import QwenImagePipeline

    >>> pipe = QwenImagePipeline.from_pretrained(
    ...     "Qwen/Qwen-Image",
    ...     torch_dtype=torch.bfloat16,
    ... )
    >>> pipe.enable_model_cpu_offload()
    >>> pipe.load_lora_weights(f"linoyts/lora_jobs_test_2")
    >>> 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)


## 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]