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---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- not-for-all-audiences
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'Barefoot and casual, this hijabi girl lounges in comfort. She is wearing elegant, light-blue floral silk pajamas that exude a sense of relaxation and luxury. Her hijab, colorfully bright in pink and wrapped in a towel style, adds a playful touch to her serene morning look. She holds a stylish cup with a camera design, smiling warmly as she leans back against the cream tufted headboard of the bed. The cozy bedroom setting includes a clothing rack with a mint green blazer and a straw handbag, as well as a plant and minimalistic artwork, creating a peaceful and chic space.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
---
# wiki_flux_lora_v1
This is a LyCORIS adapter 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:
```
Barefoot and casual, this hijabi girl lounges in comfort. She is wearing elegant, light-blue floral silk pajamas that exude a sense of relaxation and luxury. Her hijab, colorfully bright in pink and wrapped in a towel style, adds a playful touch to her serene morning look. She holds a stylish cup with a camera design, smiling warmly as she leans back against the cream tufted headboard of the bed. The cozy bedroom setting includes a clothing rack with a mint green blazer and a straw handbag, as well as a plant and minimalistic artwork, creating a peaceful and chic space.
```
## 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).
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: 2
- Training steps: 5000
- Learning rate: 0.0001
- 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: No
- Xformers: Not used
- LyCORIS Config:
```json
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
```
## Datasets
### wiki-1024
- Repeats: 2
- Total number of images: 822
- Total number of aspect buckets: 8
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
## Inference
```python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "Barefoot and casual, this hijabi girl lounges in comfort. She is wearing elegant, light-blue floral silk pajamas that exude a sense of relaxation and luxury. Her hijab, colorfully bright in pink and wrapped in a towel style, adds a playful touch to her serene morning look. She holds a stylish cup with a camera design, smiling warmly as she leans back against the cream tufted headboard of the bed. The cozy bedroom setting includes a clothing rack with a mint green blazer and a straw handbag, as well as a plant and minimalistic artwork, creating a peaceful and chic space."
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")
```
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