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Flux-Marsden-Hartley-LoKr-SimpleTuner-03

This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.

The main validation prompt used during training was:

hamster in the style of MRSDN

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.

You can find some example images in the following gallery:

Prompt
unconditional (blank prompt)
Negative Prompt
blurry, cropped, ugly
Prompt
A rocky Maine coastline with bold, geometric shapes representing cliffs and waves. Strong colors and simplified forms dominate the composition, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
An abstract composition inspired by Berlin's urban life. Fragmented shapes, numbers, and symbols arranged in a Cubist-influenced style, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
A still life of flowers in a vase, rendered with thick brushstrokes and vibrant, non-naturalistic colors. Simplified forms show Cubist influence, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
A stark New Mexico landscape with stylized mountains and desert flora. Bold outlines and earthy colors capture the essence of the Southwest, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
A portrait of a WWI German soldier, composed of geometric shapes and military symbols. Strong, emotive use of color and form, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
Mount Katahdin in Maine, depicted with sharp angles and bold colors. The landscape is reduced to its essential forms, emphasizing its rugged nature, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
Modern New York skyscrapers rendered in Hartley's style. Geometric shapes and bold colors create a dynamic urban composition, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
An orbiting space station viewed through a Modernist lens. Fragmented forms and symbolic elements represent the futuristic structure, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
An electric car charging station, depicted with Cubist-inspired fragmentation. Bold colors and geometric shapes represent energy and technology, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
A composition of social media icons and symbols, arranged in a Modernist style reminiscent of Hartley's German officer paintings, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
An abstract representation of climate change, using Hartley's bold style to depict melting ice caps, rising seas, and changing weather patterns, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
A person wearing a VR headset, surrounded by fragmented, Cubist-inspired virtual elements. Bold colors and geometric forms dominate the composition, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
hamster, in the style of MRSDN
Negative Prompt
blurry, cropped, ugly
Prompt
hamster in the style of MRSDN
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: 12
  • Training steps: 7400
  • Learning rate: 0.0004
  • Effective batch size: 2
    • Micro-batch size: 2
    • Gradient accumulation steps: 1
    • Number of GPUs: 1
  • Prediction type: flow-matching
  • Rescaled betas zero SNR: False
  • Optimizer: adamw_bf16
  • Precision: Pure BF16
  • Quantised: Yes: int8-quanto
  • Xformers: Not used
  • 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

marsden-hartley-Flux-CC-512

  • Repeats: 10
  • Total number of images: 25
  • Total number of aspect buckets: 4
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

marsden-hartley-Flux-CC-1024

  • Repeats: 10
  • Total number of images: 25
  • Total number of aspect buckets: 8
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

marsden-hartley-Flux-CC-512-crop

  • Repeats: 10
  • Total number of images: 25
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square

marsden-hartley-Flux-CC-1024-crop

  • Repeats: 10
  • Total number of images: 25
  • Total number of aspect buckets: 1
  • Resolution: 1.048576 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: square

Inference

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 = "hamster in the style of MRSDN"

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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