CoolFace
Modelpublic

hbxartdev/kraken-flux-lora

sourceHugging Faceotherupdated 1y agoView on Hugging Face
0likes8downloads
Model Card

kraken-flux-lora

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

The main validation prompt used during training was:

editorial close-up of a woman, glowing flawless skin, peach blush and gloss, nude shimmer lips, editorial studio light

Validation settings

  • —CFG: 3.0
  • —CFG Rescale: 0.0
  • —Steps: 20
  • —Sampler: FlowMatchEulerDiscreteScheduler
  • —Seed: 42
  • —Resolution: 1024x1024
  • —Skip-layer guidance:

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

<Gallery />

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

Training settings

  • —Training epochs: 15
  • —Training steps: 2000
  • —Learning rate: 0.0002
  • —Learning rate schedule: constant
  • —Warmup steps: 100
  • —Max grad value: 2.0
  • —Effective batch size: 1
  • —Micro-batch size: 1
  • —Gradient accumulation steps: 1
  • —Number of GPUs: 1
  • —Gradient checkpointing: True
  • —Prediction type: flowmatching (extra parameters=['shift=3', 'fluxguidancemode=constant', 'fluxguidancevalue=1.0', 'fluxlora_target=all'])
  • —Optimizer: adamw_bf16
  • —Trainable parameter precision: Pure BF16
  • —Base model precision: no_change
  • —Caption dropout probability: 0.05%
  • —LoRA Rank: 16
  • —LoRA Alpha: None
  • —LoRA Dropout: 0.1
  • —LoRA initialisation style: default

Datasets

default_dataset

  • —Repeats: 0
  • —Total number of images: 125
  • —Total number of aspect buckets: 1
  • —Resolution: 0.147456 megapixels
  • —Cropped: True
  • —Crop style: center
  • —Crop aspect: square
  • —Used for regularisation data: No

Inference

python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'hbxartdev/kraken-flux-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "editorial close-up of a woman, glowing flawless skin, peach blush and gloss, nude shimmer lips, editorial studio light"


## 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
model_output = 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(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]

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