--- base_model: models/Qwen-Image library_name: diffusers license: apache-2.0 widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - qwen-image - qwen-image-diffusers - template:sd-lora --- # HiDream Image DreamBooth LoRA - viyer7/trained-qwen-lora_1500steps ## Model description These are viyer7/trained-qwen-lora_1500steps DreamBooth LoRA weights for models/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 `None` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](viyer7/trained-qwen-lora_1500steps/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"viyer7/trained-qwen-lora_1500steps") >>> image = pipe(f"None").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]