Training epochs: 218
Training steps: 1600
Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 100
Max grad value: 0.01
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: flow_matching[]
Optimizer: optimi-lion
Trainable parameter precision: Pure BF16
Base model precision: int8-quanto
Caption dropout probability: 0.0%
LoRA Rank: 8
LoRA Alpha: 8.0
LoRA Dropout: 0.1
LoRA initialisation style: default
LoRA mode: Standard

Datasets

dreambooth-1024

Repeats: 0
Total number of images: 26
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'Qwen/Qwen-Image'
adapter_id = 'bghira/simpletuner-example-qwen_image-peft-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "An domokun running through a field with flowers all around him."
negative_prompt = 'ugly, cropped, blurry, low-quality, mediocre average'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=4.0,
).images[0]

model_output.save("output.png", format="PNG")

terminusresearch/simpletuner-example-qwen_image-peft-lora

simpletuner-example-qwen_image-peft-lora

Validation settings

Training settings

Datasets

dreambooth-1024

Inference

terminusresearch/
simpletuner-example-qwen_image-peft-lora