metadata

license: creativeml-openrail-m
base_model: black-forest-labs/FLUX.1-dev
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: ''''
    output:
      url: ./assets/image_0_0.png
  - text: your prompt to validate on
    parameters:
      negative_prompt: ''''
    output:
      url: ./assets/image_1_0.png
  - text: another prompt to validate on
    parameters:
      negative_prompt: ''''
    output:
      url: ./assets/image_2_0.png
  - text: >-
      A fashionable woman wearing oversized white sunglasses and orange
      lipstick. woman, fashion, sunglasses, oversized glasses, white sunglasses,
      orange lipstick, makeup, portrait, close-up, monochrome, dark hair,
      straight hair, chic, stylish, modern, glamour, glossy, high contrast,
      minimalistic background, beauty, contemporary, bold, accessory, trendy,
      cosmetic, facial expression, luxurious, elegant, sophisticated, high
      fashion.
    parameters:
      negative_prompt: ''''
    output:
      url: ./assets/image_3_0.png

lora-training

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

The main validation prompt used during training was:

A fashionable woman wearing oversized white sunglasses and orange lipstick. woman, fashion, sunglasses, oversized glasses, white sunglasses, orange lipstick, makeup, portrait, close-up, monochrome, dark hair, straight hair, chic, stylish, modern, glamour, glossy, high contrast, minimalistic background, beauty, contemporary, bold, accessory, trendy, cosmetic, facial expression, luxurious, elegant, sophisticated, high fashion.

Validation settings

CFG: 3.5
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
'

Prompt
your prompt to validate on

Negative Prompt
'

Prompt
another prompt to validate on

Negative Prompt
'

Prompt
A fashionable woman wearing oversized white sunglasses and orange lipstick. woman, fashion, sunglasses, oversized glasses, white sunglasses, orange lipstick, makeup, portrait, close-up, monochrome, dark hair, straight hair, chic, stylish, modern, glamour, glossy, high contrast, minimalistic background, beauty, contemporary, bold, accessory, trendy, cosmetic, facial expression, luxurious, elegant, sophisticated, high fashion.

Negative Prompt
'

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

Training settings

Training epochs: 142
Training steps: 2000
Learning rate: 8e-07
Effective batch size: 20
- Micro-batch size: 10
- Gradient accumulation steps: 2
- Number of GPUs: 1
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Not used
LoRA Rank: 16
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: loftq

Datasets

poc1_lundlund_hugocomte

Repeats: 0
Total number of images: 280
Total number of aspect buckets: 1
Resolution: 512 px
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 = 'hmpark/lora-training'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "A fashionable woman wearing oversized white sunglasses and orange lipstick. woman, fashion, sunglasses, oversized glasses, white sunglasses, orange lipstick, makeup, portrait, close-up, monochrome, dark hair, straight hair, chic, stylish, modern, glamour, glossy, high contrast, minimalistic background, beauty, contemporary, bold, accessory, trendy, cosmetic, facial expression, luxurious, elegant, sophisticated, high fashion."


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=3.5,
).images[0]
image.save("output.png", format="PNG")