README.md

---
license: other
base_model: "stabilityai/stable-diffusion-3-medium-diffusers"
tags:
  - sd3
  - sd3-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - safe-for-work
  - lora
  - template:sd-lora
  - standard
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, floating leaves'
  output:
    url: ./assets/image_0_0.png
- text: 'One canola seedling that is about 11 days old in a bright blue cylindrical cup on a bright blue background'
  parameters:
    negative_prompt: 'blurry, floating leaves'
  output:
    url: ./assets/image_1_0.png
---

# test

This is a standard PEFT 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:



```
One canola seedling that is about 11 days old in a bright blue cylindrical cup on a bright blue background
```

## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `10`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024`

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: 6
- Training steps: 129
- Learning rate: 0.00105
- Max grad norm: 0.01
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LoRA Rank: 256
- LoRA Alpha: 256.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### canola-test
- Repeats: 1
- Total number of images: 10
- Total number of aspect buckets: 1
- Resolution: 1024 px
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
adapter_id = 'rdeinla/test'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "One canola seedling that is about 11 days old in a bright blue cylindrical cup on a bright blue background"
negative_prompt = 'blurry, floating leaves'
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=10,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1024,
    height=1024,
    guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")
```