diffusers flux flux-diffusers text-to-image simpletuner lora template:sd-lora base_model:black-forest-labs/FLUX.1-dev base_model:adapter:black-forest-labs/FLUX.1-dev license:other region:us
---
base_model: black-forest-labs/FLUX.1-dev
license: other
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- lora
- template:sd-lora
inference: true
---

# simpletuner-lora-flux

This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).


The main validation prompt used during training was:



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

## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- 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: 7
- Training steps: 0
- Learning rate: 8e-05
- Effective batch size: 40
  - Micro-batch size: 10
  - Gradient accumulation steps: 4
  - 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: 64
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### transparent_objects_custom
- Repeats: 0
- Total number of images: 160826
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'rohn132/simpletuner-lora-flux'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "ethnographic photography of teddy bear at a picnic"

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    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=3.0,
).images[0]
image.save("output.png", format="PNG")
```

