simpletuner-lora

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

The main validation prompt used during training was:

A picture of Philippe

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 breathtaking anime-style portrait of Philippe, capturing his essence with vibrant colors and expressive features

Negative Prompt
blurry, cropped, ugly

Prompt
a high-quality, detailed photograph of Philippe as a sous-chef, immersed in the art of culinary creation

Negative Prompt
blurry, cropped, ugly

Prompt
a lifelike and intimate portrait of Philippe, showcasing his unique personality and charm

Negative Prompt
blurry, cropped, ugly

Prompt
a cinematic, visually stunning photo of Philippe, emphasizing his dramatic and captivating presence

Negative Prompt
blurry, cropped, ugly

Prompt
an elegant and timeless portrait of Philippe, exuding grace and sophistication

Negative Prompt
blurry, cropped, ugly

Prompt
a dynamic and adventurous photo of Philippe, captured in an exciting, action-filled moment

Negative Prompt
blurry, cropped, ugly

Prompt
a mysterious and enigmatic portrait of Philippe, shrouded in shadows and intrigue

Negative Prompt
blurry, cropped, ugly

Prompt
a vintage-style portrait of Philippe, evoking the charm and nostalgia of a bygone era

Negative Prompt
blurry, cropped, ugly

Prompt
an artistic and abstract representation of Philippe, blending creativity with visual storytelling

Negative Prompt
blurry, cropped, ugly

Prompt
a futuristic and cutting-edge portrayal of Philippe, set against a backdrop of advanced technology

Negative Prompt
blurry, cropped, ugly

Prompt
A picture of Philippe

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: 636
Training steps: 7000
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_bf16
Precision: Pure BF16
Quantised: No
Xformers: Not used
LoRA Rank: 16
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

dreambooth-subject

Repeats: 0
Total number of images: 11
Total number of aspect buckets: 1
Resolution: 1.048576 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 = 'PhilSad/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "A picture of Philippe"

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")