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

        - text: 'living room - kitchen in the style of <s0><s1> with a open floor plan, featuring a coffee table, a couch, a chandelier, and a set of dining table'
          output:
            url:
                "image_0.png"
        
        - text: 'living room - kitchen in the style of <s0><s1> with a open floor plan, featuring a coffee table, a couch, a chandelier, and a set of dining table'
          output:
            url:
                "image_1.png"
        
        - text: 'living room - kitchen in the style of <s0><s1> with a open floor plan, featuring a coffee table, a couch, a chandelier, and a set of dining table'
          output:
            url:
                "image_2.png"
        
        - text: 'living room - kitchen in the style of <s0><s1> with a open floor plan, featuring a coffee table, a couch, a chandelier, and a set of dining table'
          output:
            url:
                "image_3.png"
        
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: living room - kitchen in style of <s0><s1> with an open floor plan
license: openrail++
---

# SD1.5 LoRA DreamBooth - htuannn/living-room-sd-1-5-16

<Gallery />

## Model description

### These are htuannn/living-room-sd-1-5-16 LoRA adaption weights for runwayml/stable-diffusion-v1-5.

## Download model

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

- **LoRA**: download **[`living-room-sd-1-5-16.safetensors` here 💾](/htuannn/living-room-sd-1-5-16/blob/main/living-room-sd-1-5-16.safetensors)**.
    - Place it on your `models/Lora` folder.
    - On AUTOMATIC1111, load the LoRA by adding `<lora:living-room-sd-1-5-16:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`living-room-sd-1-5-16_emb.safetensors` here 💾](/htuannn/living-room-sd-1-5-16/blob/main/living-room-sd-1-5-16_emb.safetensors)**.
    - Place it on it on your `embeddings` folder
    - Use it by adding `living-room-sd-1-5-16_emb` to your prompt. For example, `living room - kitchen in style of living-room-sd-1-5-16_emb with an open floor plan`
    (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('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('htuannn/living-room-sd-1-5-16', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='htuannn/living-room-sd-1-5-16', filename='living-room-sd-1-5-16_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
        
image = pipeline('living room - kitchen in the style of <s0><s1> with a open floor plan, featuring a coffee table, a couch, a chandelier, and a set of dining table').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 `TOK` → use `<s0><s1>` in your prompt 



## Details
All [Files & versions](/htuannn/living-room-sd-1-5-16/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_sd15_advanced.py).

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: None.