flux-lora-training

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

The main validation prompt used during training was:

anime illustraion of a group photo of ddadvege and ooozar together

Validation settings

  • CFG: 3.5
  • CFG Rescale: 0.0
  • Steps: 15
  • Sampler: None
  • Seed: 42
  • Resolution: 512

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
'
Prompt
anime illustraion of a group photo of ddadvege and ooozar together
Negative Prompt
'

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

Training settings

  • Training epochs: 2999
  • Training steps: 9000
  • Learning rate: 0.0001
  • Effective batch size: 1
    • Micro-batch size: 1
    • Gradient accumulation steps: 1
    • Number of GPUs: 1
  • Prediction type: flow-matching
  • Rescaled betas zero SNR: False
  • Optimizer: AdamW, stochastic bf16
  • Precision: Pure BF16
  • Xformers: Enabled
  • LoRA Rank: 32
  • LoRA Alpha: None
  • LoRA Dropout: 0.1
  • LoRA initialisation style: default

Datasets

default_dataset

  • Repeats: 0
  • Total number of images: 3
  • Total number of aspect buckets: 1
  • Resolution: 0.5 megapixels
  • Cropped: True
  • Crop style: center
  • Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'krigeta/flux-lora-training'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "anime illustraion of a group photo of ddadvege and ooozar together"


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=15,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=512,
    height=512,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
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