---
license: creativeml-openrail-m
base_model: "black-forest-labs/FLUX.1-dev"
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true

---

# baahubaliflux

This is a 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:

```
full body portrait of baahubali standing on the edge of a cliff looking at the camera
```

## Validation settings
- CFG: `3.5`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024`

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: 3
- Training steps: 2000
- Learning rate: 0.0003
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- 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

### baahubaliflux
- Repeats: 0
- Total number of images: 599
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'baahubaliflux'
pipeline = DiffusionPipeline.from_pretrained(model_id)\pipeline.load_lora_weights(adapter_id)

prompt = "full body portrait of baahubali standing on the edge of a cliff looking at the camera"
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='blurry, cropped, ugly',
    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=1152,
    height=768,
    guidance_scale=3.5,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")
```