---
license: creativeml-openrail-m
base_model: "stabilityai/stable-diffusion-3-medium-diffusers"
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
inference: true
widget:
- text: 'a studio portrait photograph of emma watson. she looks relaxed and happy.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
---

# sd3-lora-celebrities

This is a LoRA derived from [stabilityai/stable-diffusion-3-medium-diffusers](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers).

The main validation prompt used during training was:

```
a studio portrait photograph of emma watson. she looks relaxed and happy.
```

## Validation settings
- CFG: `5.0`
- CFG Rescale: `0.2`
- Steps: `50`
- Sampler: `euler`
- Seed: `2`
- Resolution: `1280x768`

Note: The validation settings are not necessarily the same as the [training settings](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 5
- Training steps: 9316
- Learning rate: 0.0001
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: v_prediction
- Rescaled betas zero SNR: True
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used
- LoRA Rank: 16
- LoRA Alpha: 16
- LoRA Dropout: 0.1
- LoRA initialisation style: default


## Datasets

### celebrities-sd3
- Repeats: 0
- Total number of images: 1830
- Total number of aspect buckets: 27
- Resolution: 0.5 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None


## Inference


```python
import torch
from diffusers import StableDiffusion3Pipeline



model_id = "sd3-lora-celebrities"
prompt = "a studio portrait photograph of emma watson. she looks relaxed and happy."
negative_prompt = "malformed, disgusting, overexposed, washed-out"

pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='blurry, cropped, ugly',
    num_inference_steps=50,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1152,
    height=768,
    guidance_scale=5.0,
    guidance_rescale=0.2,
).images[0]
image.save("output.png", format="PNG")
```