---
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: 'an architectural sketch of a modern architecture, concrete, two stories, gray, windows, urban landscape, cultural building, side view, geometric shape, clean lines, flat roof, minimalistic design, public space, large mass, dominant volume, no visible vegetation, straight edges, sleek facade'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# arch_sktechs_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:



```
an architectural sketch of a modern architecture, concrete, two stories, gray, windows, urban landscape, cultural building, side view, geometric shape, clean lines, flat roof, minimalistic design, public space, large mass, dominant volume, no visible vegetation, straight edges, sleek facade
```

## 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: 0
- Training steps: 4500
- 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": 15000,
    "linear_alpha": 2,
    "factor": 4,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 4
            },
            "FeedForward": {
                "factor": 4
            }
        }
    }
}
```

## Datasets

### img-512
- Repeats: 10
- Total number of images: 114
- Total number of aspect buckets: 5
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### img-1024
- Repeats: 10
- Total number of images: 114
- Total number of aspect buckets: 14
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### img-512-crop
- Repeats: 10
- Total number of images: 114
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### img-1024-crop
- Repeats: 10
- Total number of images: 114
- 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 = "an architectural sketch of a modern architecture, concrete, two stories, gray, windows, urban landscape, cultural building, side view, geometric shape, clean lines, flat roof, minimalistic design, public space, large mass, dominant volume, no visible vegetation, straight edges, sleek facade"

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")
```