simpletuner-lora / README.md
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metadata
license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
  - flux
  - flux-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - not-for-all-audiences
  - lora
  - template:sd-lora
  - standard
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: >-
      casual profile headshot photo of TOK woman for instagram. hasselblad
      photography.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png

simpletuner-lora

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

The main validation prompt used during training was:

casual profile headshot photo of TOK woman for instagram. hasselblad photography.

Validation settings

  • CFG: 3.0
  • CFG Rescale: 0.0
  • Steps: 20
  • Sampler: euler
  • Seed: 42
  • Resolution: 576x1024

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
casual profile headshot photo of TOK woman for instagram. hasselblad photography.
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: 14
  • Training steps: 1250
  • Learning rate: 0.0005
  • 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: No
  • Xformers: Not used
  • LoRA Rank: 16
  • LoRA Alpha: 16.0
  • LoRA Dropout: 0.1
  • LoRA initialisation style: default

Datasets

rita-512

  • Repeats: 10
  • Total number of images: 16
  • Total number of aspect buckets: 1
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None

Inference

import torch
from diffusers import DiffusionPipeline

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

prompt = "casual profile headshot photo of TOK woman for instagram. hasselblad photography."

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=576,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")