flux-sfwbooru-3.5M-lokr-attempt5

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

The main validation prompt used during training was:

A photo-realistic image of a cat

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 garfield waifu wearing an apron with a red sphere over her head that reads It is Time

Negative Prompt

blurry, cropped, ugly

Prompt

a void of fursuit furries hanging onto the edge of reality as they get sucked into a vortex

Negative Prompt

blurry, cropped, ugly

Prompt

furries congregate at walmart to teach about gelatin fountains to adult furries

Negative Prompt

blurry, cropped, ugly

Prompt

the furry church congregation looking up at a cinematic movie screen with text on it that reads MOOSE = PONY

Negative Prompt

blurry, cropped, ugly

Prompt

furry church congregation singing hymns while they look to a screen with lyrics on it that reads THE NEW FRONTIER OF PONY MODELS?

Negative Prompt

blurry, cropped, ugly

Prompt

a furry giving a TED talk with a screen in the background showing bullet points: - what furry means, and, - what furry does not mean

Negative Prompt

blurry, cropped, ugly

Prompt

a sugar bear named brownie plays basketball with lumps of poop

Negative Prompt

blurry, cropped, ugly

Prompt

A photo-realistic image of a cat

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: 0
• Training steps: 50000
• Learning rate: 2e-06
• Effective batch size: 3
  • Micro-batch size: 1
  • Gradient accumulation steps: 1
  • Number of GPUs: 3
• Prediction type: flow-matching
• Rescaled betas zero SNR: False
• Optimizer: optimi-lion
• Precision: Pure BF16
• Quantised: Yes: int8-quanto
• Xformers: Not used
• LyCORIS Config:

{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 8,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 6
            },
            "FeedForward": {
                "factor": 4
            }
        }
    }
}

Datasets

sfwbooru

• Repeats: 0
• Total number of images: ~638952
• Total number of aspect buckets: 75
• Resolution: 1.048576 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None

sfwbooru-crop

• Repeats: 0
• Total number of images: ~560661
• 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 = "A photo-realistic image of a cat"

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")