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# LoRA training Cog model
## Use on Replicate
Easy-to-use model pre-configured for faces, objects, and styles:
[](https://replicate.com/replicate/lora-training)
Advanced model with all the parameters:
[](https://replicate.com/replicate/lora-advanced-training)
Feed the trained model into this inference model to run predictions:
[](https://replicate.com/replicate/lora)
If you want to share your trained LoRAs, please join the `#lora` channel in the [Replicate Discord](https://discord.gg/replicate).
## Use locally
First, download the pre-trained weights [with your Hugging Face auth token](https://huggingface.co./settings/tokens):
```
cog run script/download-weights <your-hugging-face-auth-token>
```
Then, you can run train your dreambooth:
```
cog predict -i [email protected]
```
The resulting LoRA weights file can be used with `patch_pipe` function:
```python
from diffusers import StableDiffusionPipeline
from lora_diffusion import patch_pipe, tune_lora_scale, image_grid
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(
"cuda:1"
)
patch_pipe(pipe, "./my-images.safetensors")
prompt = "detailed photo of <s1><s2>, detailed face, a brown cloak, brown steampunk corset, belt, virtual youtuber, cowboy shot, feathers in hair, feather hair ornament, white shirt, brown gloves, shooting arrows"
tune_lora_scale(pipe.unet, 0.8)
tune_lora_scale(pipe.text_encoder, 0.8)
imgs = pipe(
[prompt],
num_inference_steps=50,
guidance_scale=4.5,
height=640,
width=512,
).images
...
```
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