---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:

        - text: 'a phot in the fashion style of <s0>'
        
instance_prompt: a phot in the fashion style of <s0>
license: openrail++
---

# SDXL LoRA DreamBooth - armhebb/65995e622d50edfb3ead

<Gallery />

## Model description

### These are armhebb/65995e622d50edfb3ead LoRA adaption weights.

## Download model

### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

- **LoRA**: download **[`/korean_sample_checkpoint.safetensors` here 💾](/armhebb/65995e622d50edfb3ead/blob/main//korean_sample_checkpoint.safetensors)**.
    - Place it on your `models/Lora` folder.
    - On AUTOMATIC1111, load the LoRA by adding `<lora:/korean_sample_checkpoint:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`/korean_sample_checkpoint_emb.safetensors` here 💾](/armhebb/65995e622d50edfb3ead/blob/main//korean_sample_checkpoint_emb.safetensors)**.
    - Place it on it on your `embeddings` folder
    - Use it by adding `/korean_sample_checkpoint_emb` to your prompt. For example, `a phot in the fashion style of /korean_sample_checkpoint_emb`
    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
    

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

```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('armhebb/65995e622d50edfb3ead', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='armhebb/65995e622d50edfb3ead', filename='/korean_sample_checkpoint_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('a phot in the fashion style of <s0>').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)

## Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept `<GFJ>` → use `<s0>` in your prompt 



## Details
All [Files & versions](/armhebb/65995e622d50edfb3ead/tree/main).

The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: None.