mipat12/moon_synthetic_hipster

moonsynthetichipster

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: 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:

<Gallery />

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

Training settings

• —Training epochs: 9
• —Training steps: 3500
• —Learning rate: 0.0002
• —Max grad norm: 0.1
• —Effective batch size: 4
• —Micro-batch size: 4
• —Gradient accumulation steps: 1
• —Number of GPUs: 1
• —Prediction type: flow-matching (flux parameters=['shift=3', 'fluxguidancevalue=1.0'])
• —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

moonsynth-512

• —Repeats: 15
• —Total number of images: 39
• —Total number of aspect buckets: 1
• —Resolution: 0.262144 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

moonsynth-768

• —Repeats: 15
• —Total number of images: 39
• —Total number of aspect buckets: 1
• —Resolution: 0.589824 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

moonsynth-1024

• —Repeats: 7
• —Total number of images: 21
• —Total number of aspect buckets: 1
• —Resolution: 1.048576 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

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

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")