Training epochs: 2
Training steps: 100
Learning rate: 0.001
- Learning rate schedule: polynomial
- Warmup steps: 10
Max grad norm: 2.0
Effective batch size: 2
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 2
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['flow_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all+ffs'])
Optimizer: optimi-stableadamwweight_decay=0.01
Trainable parameter precision: Pure BF16
Caption dropout probability: 5.0%
LoRA Rank: 16
LoRA Alpha: 16.0
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

002-dsyjssbrhr-512

Repeats: 0
Total number of images: ~26
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

002-dsyjssbrhr-768

Repeats: 0
Total number of images: ~28
Total number of aspect buckets: 3
Resolution: 0.589824 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

002-dsyjssbrhr-1024

Repeats: 0
Total number of images: ~26
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'playerzer0x/002-dsyjssbrhr_20250212_013300_1e-3_100_bs2_polynomial_rank16_optimistableadamw'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "dsyjssbrhr style"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it 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
image = pipeline(
    prompt=prompt,
    num_inference_steps=28,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(69),
    width=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")