---
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
- standard
inference: true
widget:
- text: unconditional (blank prompt)
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_0_0.png
- text: >-
    In the style of a b3nbr4nd painting, A steaming bowl of ramen with
    chopsticks resting on the edge, against a background of concentric orange
    and blue circles. The noodles are detailed in a geometric pattern and the
    steam creates a rhythmic design.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_1_0.png
- text: >-
    In the style of a b3nbr4nd painting, A vintage record player with vinyl
    spinning, set on a yellow table. The background features an alternating
    chevron pattern in purple and green. The turntable's mechanical parts are
    rendered in precise geometric shapes.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_2_0.png
- text: >-
    In the style of a b3nbr4nd painting, A sleeping cat curled up in a modernist
    chair, with a background of interlocking hexagons in red and blue. The cat's
    fur is stylized into rhythmic curves, matching the geometric environment.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_3_0.png
- text: >-
    In the style of a b3nbr4nd painting, A classic motorcycle viewed from the
    side, against a backdrop of radiating diamond patterns in teal and gold. The
    chrome parts reflect abstract shapes, and the wheels create circular motifs
    in the composition.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_4_0.png
- text: >-
    In the style of a b3nbr4nd painting, Portrait of a woman with silver hair
    wearing dotted blue glasses and a white lace collar, against a swirling
    background of green and yellow patterns. The background features geometric
    circles and zigzag designs.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_5_0.png
- text: >-
    In the style of a b3nbr4nd painting, A storefront sign for 'Golden Palace
    Noodles' in both English and Chinese characters, mounted on a tall pole
    against a geometric cityscape with blue and tan buildings. A small arrow
    points to available parking.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_6_0.png
- text: >-
    In the style of a b3nbr4nd painting, Dark purple figs sliced in half on a
    terra cotta plate, revealing their seeded interiors. The background features
    a repeating pattern of blue and yellow squares, with wavy lines creating a
    dynamic lower section.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_7_0.png
- text: >-
    In the style of a b3nbr4nd painting, Two young people wearing matching navy
    shirts and light gray face masks, posed against a warm yellow background.
    Their curly hair and gentle head tilts create a symmetrical composition.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_8_0.png
- text: >-
    In the style of a b3nbr4nd painting, A hamster wearing tiny glasses and a
    bowtie sitting at a miniature desk with a tiny laptop, against a background
    of spiral patterns in teal and orange. Office supplies scaled to
    hamster-size are arranged neatly on the desk.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_9_0.png
- text: >-
    In the style of a b3nbr4nd painting, A bearded man in a plaid shirt and
    denim apron carefully sanding a mid-century modern chair, surrounded by
    woodworking tools. The background features overlapping triangles in rust and
    navy blue colors, with sawdust creating delicate patterns in the air.
  parameters:
    negative_prompt: blurry, cropped, ugly
  output:
    url: ./assets/image_10_0.png
- text: >-
    In the style of a b3nbr4nd painting, The image shows a view from a window
    overlooking a building with multiple rows of windows and shutters. Each
    window features a set of closed shutters, and a few potted plants are
    positioned on ledges below some of the windows. The scene is framed by the
    window panes from the vantage point of the observer.
  output:
    url: images/example_17i69oiaw.png
- text: >-
    In the style of a b3nbr4nd painting, An anime-style character with blond
    hair and green eyes stands partially obscured behind a dark painting easel,
    holding a brush. Behind her is a window with small panes of glass, and a
    vase containing brushes is visible on a table. Light filters through the
    window.
  output:
    url: images/example_a7hivjyit.png
- text: >-
    In the style of a b3nbr4nd painting, The image shows two people, one wearing
    a black suit with a polka dot tie and the other in a brown jacket, both
    covering their mouths with their hands. The person on the left is holding a
    phone to their ear while the person on the right has a phone in hand. Both
    individuals have expressions that suggest they are listening intently or
    reacting to what they are hearing.
  output:
    url: images/example_mrops9ghl.png
- text: >-
    In the style of a b3nbr4nd painting, A man with grey hair and a mustache,
    wearing a brown jacket, sits at a desk facing a blonde girl, who is seated
    with her back to the viewer. The desk is cluttered with books, a typewriter,
    and a lamp, situated in a dimly lit room with bookshelves and windows in the
    background. The overall color palette is dark with browns, yellows and
    highlights of light shining on objects.
  output:
    url: images/example_yr5gu9hvo.png
- text: >-
    In the style of a b3nbr4nd painting, blue creature, humanoid figure, yellow
    eyes, blue fur, black overalls, yellow shirt, folded hands, seated figure,
    green wall art, wavy patterns, orange stripes, purple background, centered
    figure, front-facing figure, no text, abstract setting.
  output:
    url: images/example_3ri3pmh25.png
- text: >-
    In the style of a b3nbr4nd painting, eye, close-up, centered, eyelashes,
    yellow eyelashes, blue iris, green iris, black pupil, pink eyelid, white
    highlights, thick lines, radiating iris lines, abstract design, blue
    background, geometric shapes, colorful, detailed eye features, vibrant
    image.
  output:
    url: images/example_mmmhly235.png
- text: >-
    In the style of a b3nbr4nd painting, A light-skinned, blonde-haired
    character with blue-green eyes is partially hidden behind a dark brown
    wooden easel, holding a thin paintbrush in their right hand. They are
    wearing a dark grey long-sleeved shirt and are positioned in a room with a
    window showing greenery outside. Sunlight appears to be coming through the
    window, casting a warm light on the scene.
  output:
    url: images/example_6i6lpyqqi.png
- text: >-
    In the style of a b3nbr4nd painting, blue vase, pink tulips, table with
    patterned tablecloth, orange mugs, green teapot, posters on wall, red
    poster, text on red poster, white text, yellow wall, drawers in background,
    multiple cups, ceramic kettle, lid on kettle, floral mug, window with
    striped curtains, indoor scene, objects on table, vase in center, tea set
  output:
    url: images/example_fkgwfvrpo.png

---

# Ben-Brand-LoRA

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


No validation prompt was used during training.

None



## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1024x1024`
- Skip-layer guidance: 

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: 1
- Training steps: 2500
- Learning rate: 0.0001
  - Learning rate schedule: polynomial
  - Warmup steps: 100
- Max grad norm: 0.1
- Effective batch size: 6
  - Micro-batch size: 2
  - Gradient accumulation steps: 3
  - Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 10.0%


- LoRA Rank: 64
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### ben-brand-256
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 6
- Resolution: 0.065536 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ben-brand-crop-256
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 1
- Resolution: 0.065536 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
### ben-brand-512
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 7
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ben-brand-crop-512
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
### ben-brand-768
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 2
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ben-brand-crop-768
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
### ben-brand-1024
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 7
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ben-brand-crop-1024
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
### ben-brand-1440
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 2
- Resolution: 2.0736 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ben-brand-crop-1440
- Repeats: 10
- Total number of images: 98
- Total number of aspect buckets: 1
- Resolution: 2.0736 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'davidrd123/Ben-Brand-LoRA'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "An astronaut is riding a horse through the jungles of Thailand."


## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
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(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```