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---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- 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: 'This building in Saudi Arabia is a fine example of traditional desert architecture, characterized by its use of locally available materials such as sun-dried mud bricks and plaster, which provide natural insulation against the harsh climate. The tower, likely a watchtower or a minaret, showcases simple yet functional design, with narrow windows for ventilation and strategic visibility. The walls are robust and thick, demonstrating an emphasis on defense and durability. The flat roofs and crenelated parapets suggest the original grandeur and practical purposes of the structure, providing protection and elevated viewpoints. The craftsmanship reflects a deep understanding of the environmental demands, effectively merging form with function.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
---
# saudi-vernacular-flux-lora-v2
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:
```
This building in Saudi Arabia is a fine example of traditional desert architecture, characterized by its use of locally available materials such as sun-dried mud bricks and plaster, which provide natural insulation against the harsh climate. The tower, likely a watchtower or a minaret, showcases simple yet functional design, with narrow windows for ventilation and strategic visibility. The walls are robust and thick, demonstrating an emphasis on defense and durability. The flat roofs and crenelated parapets suggest the original grandeur and practical purposes of the structure, providing protection and elevated viewpoints. The craftsmanship reflects a deep understanding of the environmental demands, effectively merging form with function.
```
## 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: 4
- 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,
"linear_dim": 15000,
"linear_alpha": 1.25,
"factor": 4,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 4
},
"FeedForward": {
"factor": 4
}
}
}
}
```
## Datasets
### my-dataset-512
- Repeats: 1
- Total number of images: 149
- Total number of aspect buckets: 2
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### my-dataset-1024
- Repeats: 1
- Total number of images: 149
- Total number of aspect buckets: 2
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### my-dataset-512-crop
- Repeats: 1
- Total number of images: 149
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### my-dataset-1024-crop
- Repeats: 1
- Total number of images: 149
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
## 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 = "This building in Saudi Arabia is a fine example of traditional desert architecture, characterized by its use of locally available materials such as sun-dried mud bricks and plaster, which provide natural insulation against the harsh climate. The tower, likely a watchtower or a minaret, showcases simple yet functional design, with narrow windows for ventilation and strategic visibility. The walls are robust and thick, demonstrating an emphasis on defense and durability. The flat roofs and crenelated parapets suggest the original grandeur and practical purposes of the structure, providing protection and elevated viewpoints. The craftsmanship reflects a deep understanding of the environmental demands, effectively merging form with function."
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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