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davidrd123
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MarcoBackground-SimpleTrigger-EMA-Flux-LoKr

Text-to-Image
Diffusers
Safetensors
flux
flux-diffusers
simpletuner
safe-for-work
lora
template:sd-lora
lycoris
Model card Files Community

MarcoBackground-SimpleTrigger-EMA-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: 1408x768
  • 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 m4rc0 anime background paintings, A series of industrial machines are arranged in rows inside a large, spacious warehouse. Bright natural light streams in from expansive windows, casting shadows across the wooden floor. The interior is filled with structural elements like beams and supports, suggesting a manufacturing environment.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A moonlit alley with clothes hanging on a line and dimly lit buildings. The sky is overcast with clouds partially covering the moon. Balconies and beams create shadows across the scene.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A dark, starry night sky with swirling clouds over a mountainous landscape. A small, illuminated caravan sits in an open field dotted with white flowers.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A green chalkboard with handwritten text partially covered by shadows cast from a window. The window frame and sunlight create distinct lines and patterns on the board. Artwork pages are pinned at the top.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A misty morning harbor with fishing boats gently bobbing in the water. The rising sun casts long shadows across weathered wooden docks, while seabirds circle overhead. Stacked crates and coiled ropes line the pier.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A two-story library interior with spiral staircases and towering wooden bookshelves. Autumn sunlight filters through stained glass windows, creating colorful patterns on leather armchairs and scattered open books.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, An abandoned Victorian greenhouse with broken glass panels and overgrown vines. Shafts of afternoon light pierce through the dusty air, illuminating scattered terra cotta pots and rusted gardening tools.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of m4rc0 anime background paintings, A rural train platform at dusk with a wooden waiting shelter. Paper lanterns cast a warm glow on the wooden planks, while steam from a distant locomotive drifts across the purple-orange sky.
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: 7

  • Training steps: 8800

  • Learning rate: 0.0001

    • 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=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', '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

marco-background-512

  • Repeats: 22
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

marco-background-768

  • Repeats: 22
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 0.589824 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

marco-background-1024

  • Repeats: 11
  • Total number of images: 34
  • Total number of aspect buckets: 3
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

marco-background-1536

  • Repeats: 5
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 2.359296 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

marco-background-512-crop

  • Repeats: 11
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square
  • Used for regularisation data: No

marco-background-768-crop

  • Repeats: 11
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 0.589824 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square
  • Used for regularisation data: No

marco-background-1024-crop

  • Repeats: 5
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 1.048576 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square
  • Used for regularisation data: No

marco-background-1536-crop

  • Repeats: 2
  • Total number of images: 34
  • Total number of aspect buckets: 1
  • Resolution: 2.359296 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/MarcoBackground-SimpleTrigger-EMA-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=1408,
    height=768,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")

Exponential Moving Average (EMA)

SimpleTuner generates a safetensors variant of the EMA weights and a pt file.

The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.

The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.

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