sullivan_guitar_man_wface

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:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

a hamster drumming in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

a man playing the saxophone in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

a woman holding a sign that says 'I LOVE PROMPTS!' in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

a hipster with a beard, building a chair in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

a transgender guitarist in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

concert event, pop star in motion, crowd blurred, flare from stadium lights in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

Two superheroes fighting a croissant in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

LeBron James and Shaquille O'Neal in the style of a r1ch5ull1v4n caricature

Negative Prompt

blurry, cropped, ugly

Prompt

a pig, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle

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: 56
• Training steps: 9000
• Learning rate: 0.0002
• Max grad norm: 0.1
• Effective batch size: 6
  • Micro-batch size: 3
  • Gradient accumulation steps: 2
  • Number of GPUs: 1
• Prediction type: flow-matching (flux parameters=['shift=3', 'flux_guidance_value=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

sullivan-512

• Repeats: 11
• Total number of images: 22
• Total number of aspect buckets: 4
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

sullivan-768

• Repeats: 11
• Total number of images: 22
• Total number of aspect buckets: 6
• Resolution: 0.589824 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

sullivan-1024

• Repeats: 5
• Total number of images: 22
• Total number of aspect buckets: 7
• Resolution: 1.048576 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

sullivan-1536

• Repeats: 2
• Total number of images: 22
• Total number of aspect buckets: 8
• Resolution: 2.359296 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")