---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: openrail++
inference: true
instance_prompt: a red chicken in the style of <s0><s1>
widget:
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
  output:
    url: image_0.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
  output:
    url: image_1.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
  output:
    url: image_2.png
- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
  output:
    url: image_3.png
tags:
- text-to-image
- diffusers
- diffusers-training
- lora
- template:sd-lorastable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- template:sd-lorastable-diffusion
- stable-diffusion-diffusers
---

<!-- 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. -->


# SD1.5 LoRA DreamBooth - cindyloo337/sbne-chicken-sd21-lora

<Gallery />

## Model description

### These are cindyloo337/sbne-chicken-sd21-lora LoRA adaption weights for stabilityai/stable-diffusion-2-1.

## Download model

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

- **LoRA**: download **[`sbne-chicken-sd21-lora.safetensors` here 💾](/cindyloo337/sbne-chicken-sd21-lora/blob/main/sbne-chicken-sd21-lora.safetensors)**.
    - Place it on your `models/Lora` folder.
    - On AUTOMATIC1111, load the LoRA by adding `<lora:sbne-chicken-sd21-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`sbne-chicken-sd21-lora_emb.safetensors` here 💾](/cindyloo337/sbne-chicken-sd21-lora/blob/main/sbne-chicken-sd21-lora_emb.safetensors)**.
    - Place it on it on your `embeddings` folder
    - Use it by adding `sbne-chicken-sd21-lora_emb` to your prompt. For example, `a red chicken in the style of sbne-chicken-sd21-lora_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('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('cindyloo337/sbne-chicken-sd21-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='cindyloo337/sbne-chicken-sd21-lora', filename='sbne-chicken-sd21-lora_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('a <s0><s1> chicken on a beach, in the style of <s0><s1>').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](/cindyloo337/sbne-chicken-sd21-lora/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.



## 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]