playerzer0x/labubu_dataset

labubu_dataset

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

The main validation prompt used during training was:

a photo of a daisy

Validation settings

• —CFG: 2.5
• —CFG Rescale: 0.0
• —Steps: 20
• —Sampler: FlowMatchEulerDiscreteScheduler
• —Seed: 69
• —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: 374
• —Training steps: 1500
• —Learning rate: 1e-05
• —Learning rate schedule: constant
• —Warmup steps: 100
• —Max grad value: 2.0
• —Effective batch size: 4
• —Micro-batch size: 1
• —Gradient accumulation steps: 4
• —Number of GPUs: 1
• —Gradient checkpointing: True
• —Prediction type: flowmatching (extra parameters=['flowscheduleautoshift', 'shift=0.0', 'fluxguidancemode=constant', 'fluxguidancevalue=1.0', 'fluxloratarget=fal'])
• —Optimizer: optimi-lion
• —Trainable parameter precision: Pure BF16
• —Base model precision: int8-quanto
• —Caption dropout probability: 0.05%

• —LoRA Rank: 16
• —LoRA Alpha: 16.0
• —LoRA Dropout: 0.1
• —LoRA initialisation style: default

Datasets

my-edited-images

• —Repeats: 0
• —Total number of images: 16
• —Total number of aspect buckets: 1
• —Resolution: 1.048576 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

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

prompt = "a photo of a daisy"


## 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,
    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(69),
    width=1024,
    height=1024,
    guidance_scale=2.5,
).images[0]

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