---
datasets:
- cookey39/blue_reflection
language:
- en
---

# SDXL LoRA DreamBooth - cookey39/reflector

### Examples:

https://www.pixiv.net/artworks/119270564

https://www.pixiv.net/artworks/119269797

<Gallery />

## Model description

### These are cookey39/reflector LoRA adaption weights for cookey39/hyper-sd-8step.

## Download model

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

- **LoRA**: download **[`reflector.safetensors` here 💾](/cookey39/reflector/blob/main/reflector.safetensors)**.
    - Place it on your `models/Lora` folder.
    - On AUTOMATIC1111, load the LoRA by adding `<lora:reflector:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`reflector_emb.safetensors` here 💾](/cookey39/reflector/blob/main/reflector_emb.safetensors)**.
    - Place it on it on your `embeddings` folder
    - Use it by adding `reflector_emb` to your prompt. For example, `blue_reflection:`
    (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
from diffusers import DiffusionPipeline, DDIMScheduler
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('cookey39/reflector', torch_dtype=torch.float16).to('cuda')

# lower eta results in more detail
instance_token = "<s0><s1>"
prompt = f"a {instance_token}masterpiece, best quality,long hair, cute face, white kneehighs, black hair, hair strand, twin braids, cat hair ornament, adorable girl, absurdres, huge_filesize, Japanese, game_cg, {instance_token}  "
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet, "
image = pipeline(prompt=prompt, negative_prompt = negative_prompt, num_inference_steps=50, cross_attention_kwargs={"scale": 1.0},width = 720, height=1080).images[0]
image
```

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 `TOK` → use `<s0><s1>` in your prompt 



## Details
All [Files & versions](/cookey39/reflector/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.