README.md

metadata

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: >-
      In the style of a Leon Spilliaert luminous lithograph, A solitary figure
      in a long black coat stands at the edge of a pier, their back to the
      viewer. The dark water stretches out as a vast empty space below, while a
      pale moon hangs suspended in the grainy night sky.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, Two tall windows in
      a dimly lit room cast rectangles of pale light across an empty floor. A
      single wooden chair sits between them, its shadow elongated and distorted.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_2_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, A winding path
      through winter-bare trees leads to a small chapel with a pointed spire.
      The ground and sky merge in shades of grey, broken only by the stark
      silhouettes of branches.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_3_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, A hamster stands
      upright on its hind legs against an empty background, its shadow
      stretching impossibly long behind it. A single sunflower seed lies
      untouched before it.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_4_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, A Range Rover sits
      abandoned in deep snow, its angular form a dark mass against the white
      landscape. Long shadows from unseen trees stripe across its surface.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_5_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, A glass Coca-Cola
      bottle stands alone on a window sill, silhouetted against a pale sky. Its
      distinctive shape casts a elongated shadow across the bare wooden surface.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_6_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, A grandfather clock
      stands impossibly tall in an empty room, its pendulum frozen mid-swing.
      The room's corners fade into deep shadow while pale light filters through
      unseen windows.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_7_0.png
  - text: >-
      In the style of a Leon Spilliaert luminous lithograph, An empty rocking
      chair moves by itself on a wooden porch, its curved runners leaving traces
      in thick dust. The surrounding space is rendered in grainy, textured
      darkness.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_8_0.png

LeonSpilliaert-Flux-LoKr

This is a LyCORIS adapter derived from 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: 896x1280
• Skip-layer guidance:

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A solitary figure in a long black coat stands at the edge of a pier, their back to the viewer. The dark water stretches out as a vast empty space below, while a pale moon hangs suspended in the grainy night sky.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, Two tall windows in a dimly lit room cast rectangles of pale light across an empty floor. A single wooden chair sits between them, its shadow elongated and distorted.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A winding path through winter-bare trees leads to a small chapel with a pointed spire. The ground and sky merge in shades of grey, broken only by the stark silhouettes of branches.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A hamster stands upright on its hind legs against an empty background, its shadow stretching impossibly long behind it. A single sunflower seed lies untouched before it.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A Range Rover sits abandoned in deep snow, its angular form a dark mass against the white landscape. Long shadows from unseen trees stripe across its surface.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A glass Coca-Cola bottle stands alone on a window sill, silhouetted against a pale sky. Its distinctive shape casts a elongated shadow across the bare wooden surface.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, A grandfather clock stands impossibly tall in an empty room, its pendulum frozen mid-swing. The room's corners fade into deep shadow while pale light filters through unseen windows.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Leon Spilliaert luminous lithograph, An empty rocking chair moves by itself on a wooden porch, its curved runners leaving traces in thick dust. The surrounding space is rendered in grainy, textured darkness.

Negative Prompt

blurry, cropped, ugly

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

• Training epochs: 33

• Training steps: 11000

• Learning rate: 8e-05

  • Learning rate schedule: constant
  • Warmup steps: 100

• Max grad norm: 0.1

• Effective batch size: 3

  • Micro-batch size: 3
  • Gradient accumulation steps: 1
  • Number of GPUs: 1

• Gradient checkpointing: True

• Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flux_beta_schedule_alpha=8.0', 'flux_beta_schedule_beta=2.0', 'flow_matching_loss=compatible'])

• Optimizer: adamw_bf16

• Trainable parameter precision: Pure BF16

• Caption dropout probability: 10.0%

• SageAttention: Enabled inference

LyCORIS Config:

{
    "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

leonspilliaert-512

• Repeats: 11
• Total number of images: 17
• Total number of aspect buckets: 2
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

leonspilliaert-768

• Repeats: 11
• Total number of images: 17
• Total number of aspect buckets: 4
• Resolution: 0.589824 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

leonspilliaert-1024

• Repeats: 5
• Total number of images: 17
• Total number of aspect buckets: 3
• Resolution: 1.048576 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

leonspilliaert-1536

• Repeats: 2
• Total number of images: 17
• Total number of aspect buckets: 3
• Resolution: 2.359296 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

leonspilliaert-crops-512

• Repeats: 11
• Total number of images: 17
• Total number of aspect buckets: 1
• Resolution: 0.262144 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

leonspilliaert-crops-1024

• Repeats: 5
• Total number of images: 17
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_repo_id = 'davidrd123/LeonSpilliaert-Flux-LoKr'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

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=896,
    height=1280,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")