diffusers sd3 sd3-diffusers text-to-image simpletuner lora template:sd-lora base_model:stabilityai/stable-diffusion-3-medium-diffusers base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers license:other region:us
---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
license: other
tags:
- sd3
- sd3-diffusers
- text-to-image
- diffusers
- simpletuner
- lora
- template:sd-lora
inference: true
---

# sd3-lora-training-v2

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:



```
ethnographic photography of teddy bear at a picnic
```

## Validation settings
- CFG: `5.0`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`

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




<Gallery />

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


## Training settings

- Training epochs: 89
- Training steps: 2960
- Learning rate: 8e-07
- Effective batch size: 2
  - Micro-batch size: 1
  - Gradient accumulation steps: 2
  - Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: bf16
- Quantised: No
- Xformers: Not used
- LoRA Rank: 16
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### Pal_BLIP
- Repeats: 0
- Total number of images: 73
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
adapter_id = 'zwloong/sd3-lora-training-v2'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "ethnographic photography of teddy bear at a picnic"
negative_prompt = 'blurry, cropped, ugly'
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=30,
    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=5.0,
).images[0]
image.save("output.png", format="PNG")
```

